Статті в журналах: "Flood forecasting Computer programs"

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Manik, Ngarap Im. "Perancangan Program Peramalan Kanal Banjir Barat Jakarta Menggunakan Autoregresi Multivariant." ComTech: Computer, Mathematics and Engineering Applications 3, no. 1 (June 1, 2012): 186. http://dx.doi.org/10.21512/comtech.v3i1.2402.

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This paper discusses the design of computer programs that is able to discern the characteristics description of water surface elevation data in Manggarai water gate, which variable is the most influential on the water surface elevation model and find a proper flood forecasting model using multivariate autoregressive model. The result of this study is able to assist the water gate officer in delivering early warning, prevention and anticipation of flood countermeasure. The forecast equation model obtained is Yt = 109,.7828 + 0,9291 CHt-6 – 24,484 T t-2 – 0,06245 PM t-2 + 1,4706 KB t-2 in which temperature and water surface elevation is a variable that owns the strongest correlation. This variable owns negative correlation which means that if the temperature falls, the water levels will rise. The coefficient of determination has a value of R2 = 0.4056 and the Durbin Watson statistics for DW = 0.7429.

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Rakhymberdina, M. Ye, E. V. Grokhotov, Zh A. Assylkhanova, and M. M. Toguzova. "USING SPACE SURVEY MATERIALS FOR MODELING HYDRODYNAMIC ACCIDENTS AT MINING ENTERPRISES IN KAZAKHSTAN." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-5/W1-2022 (February 3, 2022): 193–98. http://dx.doi.org/10.5194/isprs-archives-xlvi-5-w1-2022-193-2022.

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Abstract. The timeliness of using modern computer programs for modelling flood zones, the consequences of hydraulic accidents, dam breakthroughs, flood and flood forecasting in a complex system of rivers and channels for the prevention of hydro meteorological emergencies is beyond doubt. The use of BIM technologies will make it possible to move from point-based flood risk assessments to areal ones, which will significantly improve the reliability of planned measures to prevent natural and anthropogenic emergencies.The purpose - to perform works on modelling of hydrodynamic accident and forecast of its development by the example of tailings dumps in concentration plant in East Kazakhstan. As the initial data - digital model for the area of work, technical reports on engineering-hydrographical survey, topographic-geodetic works, engineering-geological survey, high-resolution satellite images in a panchromatic survey mode. On the basis of geoinformation modelling methods with use of initial and remote sensing data, final digital terrain model was built in Digital software. The method based on direct hydrodynamic modelling of area flooding was used to calculate hydrodynamic accidents, to model the dynamics of area flooding because of tailings dam break in several levels. The practical result is numerical hydrodynamic modelling of dynamics flooding area because of partial destruction, erosion of embankment dam of tailings concentrator, total area and extent of flooding, as well as the area and depth of partially flooded buildings of residential development was estimated, thematic maps of flooded area were created, as well as maps of water passage with flow velocities during the hydrodynamic accident.Thus, the application of advanced space imagery, GIS technologies in full measure allow for simulating the occurrence, development of hydrodynamic accidents in structures, to determine area, time of flooding.

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Benoit, R., N. Kouwen, W. Yu, S. Chamberland, and P. Pellerin. "Hydrometeorological aspects of the Real-Time Ultrafinescale Forecast Support during the Special Observing Period of the MAP*." Hydrology and Earth System Sciences 7, no. 6 (December 31, 2003): 877–89. http://dx.doi.org/10.5194/hess-7-877-2003.

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Abstract. During the Special Observation Period (SOP, 7 September–15 November, 1999) of the Mesoscale Alpine Programme (MAP), the Canadian Mesoscale Compressible Community Model (MC2) was run in real time at a horizontal resolution of 3 km on a computational domain of 350☓300☓50 grid points, covering the whole of the Alpine region. The WATFLOOD model was passively coupled to the MC2; the former is an integrated set of computer programs to forecast flood flows, using all available data, for catchments with response times ranging from one hour to several weeks. The unique aspect of this contribution is the operational application of numerical weather prediction data to forecast flows over a very large, multinational domain. An overview of the system performance from the hydrometeorological aspect is presented, mostly for the real-time results, but also from subsequent analyses. A streamflow validation of the precipitation is included for large basins covering upper parts of the Rhine and the Rhone, and parts of the Po and of the Danube. In general, the MC2/WATFLOOD model underestimated the total runoff because of the under-prediction of precipitation by MC2 during the MAP SOP. After the field experiment, a coding error in the cloud microphysics scheme of MC2 explains this underestimation to a large extent. A sensitivity study revealed that the simulated flows reproduce the major features of the observed flow record for most of the flow stations. The experiment was considered successful because two out of three possible flood events in the Swiss-Italian border region were predicted correctly by data from the numerical weather models linked to the hydrological model and no flow events were missed. This study has demonstrated that a flow forecast from a coupled atmospheric-hydrological model can serve as a useful first alert and quantitative forecast. Keywords: mesoscale atmospheric model, hydrological model, flood forecasting, Alps

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4

Thirumalaiah, Konda, and M. C. Deo. "Real-Time Flood Forecasting Using Neural Networks." Computer-Aided Civil and Infrastructure Engineering 13, no. 2 (March 1998): 101–11. http://dx.doi.org/10.1111/0885-9507.00090.

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Bhola, Punit Kumar, Bhavana B. Nair, Jorge Leandro, Sethuraman N. Rao, and Markus Disse. "Flood inundation forecasts using validation data generated with the assistance of computer vision." Journal of Hydroinformatics 21, no. 2 (December 7, 2018): 240–56. http://dx.doi.org/10.2166/hydro.2018.044.

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Abstract Forecasting flood inundation in urban areas is challenging due to the lack of validation data. Recent developments have led to new genres of data sources, such as images and videos from smartphones and CCTV cameras. If the reference dimensions of objects, such as bridges or buildings, in images are known, the images can be used to estimate water levels using computer vision algorithms. Such algorithms employ deep learning and edge detection techniques to identify the water surface in an image, which can be used as additional validation data for forecasting inundation. In this study, a methodology is presented for flood inundation forecasting that integrates validation data generated with the assistance of computer vision. Six equifinal models are run simultaneously, one of which is selected for forecasting based on a goodness-of-fit (least error), estimated using the validation data. Collection and processing of images is done offline on a regular basis or following a flood event. The results show that the accuracy of inundation forecasting can be improved significantly using additional validation data.

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Puttinaovarat, Supattra, and Paramate Horkaew. "Application Programming Interface for Flood Forecasting from Geospatial Big Data and Crowdsourcing Data." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 11 (November 15, 2019): 137. http://dx.doi.org/10.3991/ijim.v13i11.11237.

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Nowadays, natural disasters tend to increase and become more severe. They do affect life and belongings of great numbers of people. One kind of such disasters that hap-pen frequently almost every year is floods in all regions across the world. A prepara-tion measure to cope with upcoming floods is flood forecasting in each particular area in order to use acquired data for monitoring and warning to people and involved per-sons, resulting in the reduction of damage. With advanced computer technology and remote sensing technology, large amounts of applicable data from various sources are provided for flood forecasting. Current flood forecasting is done through computer processing by different techniques. The famous one is machine learning, of which the limitation is to acquire a large amount big data. The one currently used still requires manpower to download and record data, causing delays and failures in real-time flood forecasting. This research, therefore, proposed the development of an automatic big data downloading system from various sources through the development of applica-tion programming interface (API) for flood forecasting by machine learning. This research relied on 4 techniques, i.e., maximum likelihood classification (MLC), fuzzy logic, self-organization map (SOM), and artificial neural network with RBF Kernel. According to accuracy assessment of flood forecasting, the most accurate technique was MLC (99.2%), followed by fuzzy logic, SOM, and RBF (97.8%, 96.6%, and 83.3%), respectively.

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Supatmi, Sri, Rongtao Hou, and Irfan Dwiguna Sumitra. "Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia." Computational Intelligence and Neuroscience 2019 (February 25, 2019): 1–13. http://dx.doi.org/10.1155/2019/6203510.

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An experimental investigation was conducted to explore the fundamental difference among the Mamdani fuzzy inference system (FIS), Takagi–Sugeno FIS, and the proposed flood forecasting model, known as hybrid neurofuzzy inference system (HN-FIS). The study aims finding which approach gives the best performance for forecasting flood vulnerability. Due to the importance of forecasting flood event vulnerability, the Mamdani FIS, Sugeno FIS, and proposed models are compared using trapezoidal-type membership functions (MFs). The fuzzy inference systems and proposed model were used to predict the data time series from 2008 to 2012 for 31 subdistricts in Bandung, West Java Province, Indonesia. Our research results showed that the proposed model has a flood vulnerability forecasting accuracy of more than 96% with the lowest errors compared to the existing models.

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8

Leedal, D., A. H. Weerts, P. J. Smith, and K. J. Beven. "A data based mechanistic real-time flood forecasting module for NFFS FEWS." Hydrology and Earth System Sciences Discussions 9, no. 6 (June 8, 2012): 7271–96. http://dx.doi.org/10.5194/hessd-9-7271-2012.

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Abstract. The data based mechanistic (DBM) approach for identifying and estimating rainfall to level, and level to level models has been shown to perform well for flood forecasting in several studies. The DELFT-FEWS open shell operational flood forecasting system provides a framework linking hydrological/meteorological real-time data, real-time forecast models, and a human/computer interaction interface. This infrastructure is used by the UK National Flood Forecasting System (NFFS) and the European Flood Alert System (EFAS) among others. The open shell nature of the FEWS framework has been specifically designed to make it easy to add new forecasting models written as FEWS modules. This paper shows the development of the DBM forecast model as a FEWS module and presents results for the Eden catchment (Cumbria UK) as a case study.

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9

Johnson, Lynn E., and John L. Dallmann. "Flood Flow Forecasting Using Microcomputer Graphics and Radar Imagery." Computer-Aided Civil and Infrastructure Engineering 2, no. 2 (November 6, 2008): 85–99. http://dx.doi.org/10.1111/j.1467-8667.1987.tb00136.x.

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10

Ding, Yukai, Yuelong Zhu, Jun Feng, Pengcheng Zhang, and Zirun Cheng. "Interpretable spatio-temporal attention LSTM model for flood forecasting." Neurocomputing 403 (August 2020): 348–59. http://dx.doi.org/10.1016/j.neucom.2020.04.110.

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Faruq, Amrul, Aminaton Marto, and Shahrum Shah Abdullah. "Flood Forecasting of Malaysia Kelantan River using Support Vector Regression Technique." Computer Systems Science and Engineering 39, no. 3 (2021): 297–306. http://dx.doi.org/10.32604/csse.2021.017468.

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12

Perry, C., and T. Euler. "Cost-effective forecasting: Lessons my computer programs never taught me." Omega 18, no. 3 (January 1990): 241–46. http://dx.doi.org/10.1016/0305-0483(90)90038-b.

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Don, Nguyen Thanh, Nguyen Van Que, Tran Quang Hung, and Nguyen Hong Phong. "Data assimilation method in flood forecasting for Red river system using high performent computer." Vietnam Journal of Mechanics 37, no. 1 (February 28, 2015): 29–42. http://dx.doi.org/10.15625/0866-7136/37/1/5213.

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Around the world, the data assimilation framework has been reported to be of great interest for weather forecasting, oceanography modeling and for shallow water flows particularly for flood model. For flood model this method is a power full tool to identify time-independent parameters (e.g. Manning coefficients and initial conditions) and time-dependent parameters (e.g. inflow). This paper demonstrates the efficiency of the method to identify time-dependent parameter: inflow discharge with a real complex case Red River. Firstly, we briefly discuss about current methods for determining flow rate which encompasses the new technologies, then present the ability to recover flow rate of this method. For the case of very long time series, a temporal strategy with time overlapping is suggested to decrease the amount of memory required. In addition, some different aspects of data assimilation are covered from this case.

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14

Thierion, Vincent, Pierre-Alain Ayral, Geisel Jacob, Sauvagnargues-Lesage Sophie, and Payrastre Olivier. "Grid Technology Reliability for Flash Flood Forecasting: End-user Assessment." Journal of Grid Computing 9, no. 3 (January 20, 2011): 405–22. http://dx.doi.org/10.1007/s10723-010-9173-9.

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15

Kouwen, Nicholas. "WATFLOOD: a Micro-Computer Based Flood Forecasting System Based on Real-Time Weather Radar." Canadian Water Resources Journal 13, no. 1 (January 1988): 62–77. http://dx.doi.org/10.4296/cwrj1301062.

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DAWSON, CHRISTIAN W., MARTIN R. BROWN, and ROBERT L. WILBY. "INDUCTIVE LEARNING APPROACHES TO RAINFALL-RUNOFF MODELLING." International Journal of Neural Systems 10, no. 01 (February 2000): 43–57. http://dx.doi.org/10.1142/s0129065700000053.

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Trying to model the rainfall-runoff process is a complex activity as it is influenced by a number of implicit and explicit factors — for example, precipitation distribution, evaporation, transpiration, abstraction, watershed topography, and soil types. However, this kind of forecasting is particularly important as it is used to predict serious flooding, estimate erosion and identify problems associated with low flow. Inductive learning approaches (e.g. decision trees and artificial neural networks) are particularly well suited to problems of this nature as they can often interpret underlying factors (such as seasonal variations) which cannot be modelled by other techniques. In addition, these approaches can easily be trained on the explicit factors (e.g. rainfall) and the inexplicit factors (e.g. abstraction) that affect river flow. Inductive learning approaches can also be extended to account for new factors that emerge over a period of time. This paper evaluates the application of decision trees and two artificial neural network models (the multilayer perceptron and the radial basis function network) to river flow forecasting in two flood prone UK catchments using real hydrometric data. Comparisons are made between the performance of these approaches and conventional flood forecasting systems.

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Tremper, Bruce, and Rand Decker. "Computer applications for avalanche forecasting in the United States." Annals of Glaciology 18 (1993): 46–52. http://dx.doi.org/10.3189/s0260305500011241.

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Avalanche hazard forecasters must evaluate a number of different important parameters which often vary markedly over time and distance. Computer applications have been developed to help the avalanche forecaster manage the complex data. These include programs which simply graph and tabulate data into easily ingested displays, database and statistical software, deterministic models of the snowpack evolution and stability, and finally, networks of avalanche information. This paper is an overview of computer software currently available in English and in use in the United States.

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Tremper, Bruce, and Rand Decker. "Computer applications for avalanche forecasting in the United States." Annals of Glaciology 18 (1993): 46–52. http://dx.doi.org/10.1017/s0260305500011241.

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Avalanche hazard forecasters must evaluate a number of different important parameters which often vary markedly over time and distance. Computer applications have been developed to help the avalanche forecaster manage the complex data. These include programs which simply graph and tabulate data into easily ingested displays, database and statistical software, deterministic models of the snowpack evolution and stability, and finally, networks of avalanche information. This paper is an overview of computer software currently available in English and in use in the United States.

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Fang, Shifeng, Lida Xu, Huan Pei, Yongqiang Liu, Zhihui Liu, Yunqiang Zhu, Jianwu Yan, and Huifang Zhang. "An Integrated Approach to Snowmelt Flood Forecasting in Water Resource Management." IEEE Transactions on Industrial Informatics 10, no. 1 (February 2014): 548–58. http://dx.doi.org/10.1109/tii.2013.2257807.

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Argyle, Elizabeth M., Jonathan J. Gourley, Chen Ling, Randa L. Shehab, and Ziho Kang. "Effects of display design on signal detection in flash flood forecasting." International Journal of Human-Computer Studies 99 (March 2017): 48–56. http://dx.doi.org/10.1016/j.ijhcs.2016.11.004.

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Chen, Chen, Qiang Hui, Wenxuan Xie, Shaohua Wan, Yang Zhou, and Qingqi Pei. "Convolutional Neural Networks for forecasting flood process in Internet-of-Things enabled smart city." Computer Networks 186 (February 2021): 107744. http://dx.doi.org/10.1016/j.comnet.2020.107744.

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Indra, G., and N. Duraipandian. "Modeling of Optimal Deep Learning Based Flood Forecasting Model Using Twitter Data." Intelligent Automation & Soft Computing 35, no. 2 (2023): 1455–70. http://dx.doi.org/10.32604/iasc.2023.027703.

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Abdul Majid, M., M. Hafidz Omar, M. Salmi M. Noorani, and F. Abdul Razak. "River-flood forecasting methods: the context of the Kelantan River in Malaysia." IOP Conference Series: Earth and Environmental Science 880, no. 1 (October 1, 2021): 012021. http://dx.doi.org/10.1088/1755-1315/880/1/012021.

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Abstract River-flood forecasting is among the most important feasible non-structural approaches used in reducing economic losses and alleviating human sufferings. In spite of uncertainty in the forecasting of natural disasters, the current prevailing methods developed in many parts of the world in the recent history has made good progress to a great extent. The advancement is attributed mainly due to the availability of high-resolution weather data and the use of sophisticated computer modelling algorithms. However, it is desirable to conduct exploratory review studies to further improving the current state of affairs. The present paper reviews briefly the river-flood forecasting methods currently used worldwide with a specific focus in the context of the Kelantan River in Malaysia. Flooding in Malaysia is recurrent covering a large inhabited area compared with other natural disasters. Some of the popularly used methods in the literature such as statistical methods machine learning and methods based on chaos theory have been reviewed, The paper will also attempt to explore the future direction for research and development that might be useful specifically for dealing with the recurrent rivers flooding in Malaysia. A reasonably acceptable prediction of river streamflow is significantly important in disaster management and water resources management.

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Goudarzi, Shidrokh, Seyed Ahmad Soleymani, Mohammad Hossein Anisi, Domenico Ciuonzo, Nazri Kama, Salwani Abdullah, Mohammad Abdollahi Azgomi, Zenon Chaczko, and Azri Azmi. "Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network." Computers, Materials & Continua 70, no. 1 (2022): 715–38. http://dx.doi.org/10.32604/cmc.2022.019550.

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Narejo, Sanam, Muhammad Moazzam Jawaid, Shahnawaz Talpur, Rizwan Baloch, and Eros Gian Alessandro Pasero. "Multi-step rainfall forecasting using deep learning approach." PeerJ Computer Science 7 (May 4, 2021): e514. http://dx.doi.org/10.7717/peerj-cs.514.

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Rainfall prediction is immensely crucial in daily life routine as well as for water resource management, stochastic hydrology, rain run-off modeling and flood risk mitigation. Quantitative prediction of rainfall time series is extremely challenging as compared to other meteorological parameters due to its variability in local features that involves temporal and spatial scales. Consequently, this requires a highly complex system having an advance model to accurately capture the highly non linear processes occurring in the climate. The focus of this work is direct prediction of multistep forecasting, where a separate time series model for each forecasting horizon is considered and forecasts are computed using observed data samples. Forecasting in this method is performed by proposing a deep learning approach, i.e, Temporal Deep Belief Network (DBN). The best model is selected from several baseline models on the basis of performance analysis metrics. The results suggest that the temporal DBN model outperforms the conventional Convolutional Neural Network (CNN) specifically on rainfall time series forecasting. According to our experimentation, a modified DBN with hidden layes (300-200-100-10) performs best with 4.59E−05, 0.0068 and 0.94 values of MSE, RMSE and R value respectively on testing samples. However, we found that training DBN is more exhaustive and computationally intensive than other deep learning architectures. Findings of this research can be further utilized as basis for the advance forecasting of other weather parameters with same climate conditions.

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Wong, James, Albert Chan, and Y. H. Chiang. "A Critical Review of Forecasting Models to Predict Manpower Demand." Construction Economics and Building 4, no. 2 (November 18, 2012): 43–56. http://dx.doi.org/10.5130/ajceb.v4i2.2930.

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Forecasting manpower requirements has been useful for economic planners, policy makers and training providers in order to avoid the imbalance of skills in the labour market. Although reviews of the manpower planning models have been conducted previously, with the accumulated experience and the booming of advanced statistical techniques and computer programs, the study of forecasting practices has undrgone considerable changes and achieved maturity during the past decade. This paper assesses the latest employment and manpower dmand estimating methods by examining their rationale, strength and constraints. It aims to identify enhancements for further development of manpower forecasting model for the construction industry and compare the reliability and capacity of different forecasting metodologies. It is cocluded that the top-down forecasting approach is the dominant methodology to forecast occupational manpower demand. It precedes other methodologies by its dynamic nature and sensitivity to aa variety of factors affecting the level and structure of employment. Given the improvement of the data available, advanced modelling techniques and computer programs, manpower planning is likely to be more accessible with improved accuracy at every level of the society.

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Kenny, Ian. "Hydrographical Flow Modelling of the River Severn Using Particle Swarm Optimization." Computer Journal 63, no. 11 (November 17, 2019): 1713–26. http://dx.doi.org/10.1093/comjnl/bxz106.

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Abstract A model is presented to model hydrographical flow, which we apply to flood forecasting in the River Severn catchment area. The approach uses Particle Swarm Optimization (PSO), a swarm computation heuristic, to produce a predictive model of hydrographical flow. Hydrological flow data from 1980 to 1990 are considered, comprising the daily average flow through the River Severn and its tributaries. PSO models are developed from each year of data and are applied to predict flow in the other 10 years; model performance is shown to be largely independent of the training year, suggesting the catchment system is stable and the approach is robust. Importantly, and in contrast to most of the existing alternatives, flow is derived from data measurements taken 2 days previously, as demanded for early-warning flood prediction. The cross-validated model for prediction of extreme (Q95) events R2 = 0.96, significantly improving upon multiple linear regression R2 = 0.93, the best performing of current existing methods.

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Watson, Grace, and Jeong Eun Ahn. "A Systematic Review: To Increase Transportation Infrastructure Resilience to Flooding Events." Applied Sciences 12, no. 23 (December 2, 2022): 12331. http://dx.doi.org/10.3390/app122312331.

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This study investigated literature databases of Google Scholar and Scopus from 1900 to 2021 and reviewed relevant studies conducted to increase transportation infrastructure resilience to flood events. This review has three objectives: (1) determine which natural hazard or natural disaster had the most vulnerability studies; (2) identify which infrastructure type was most prevalent in studies related to flood resilience infrastructure; and (3) investigate the current stage of research. This review was conducted with three stages. Based on stage one, floods have been extremely present in research from 1981 to 2021. Based on stage two, transportation infrastructure was most studied in studies related to flood resilience. Based on stage three, this systematic review focused on a total of 133 peer-reviewed, journal articles written in English. In stage three, six research categories were identified: (1) flood risk analysis; (2) implementation of real-time flood forecasting and prediction; (3) investigation of flood impacts on transportation infrastructure; (4) vulnerability analysis of transportation infrastructure; (5) response and preparatory measures towards flood events; and (6) several other studies that could be related to transportation infrastructure resilience to flood events. Current stage of studies for increasing transportation resilience to flood events was investigated within these six categories. Current stage of studies shows efforts to advance modeling systems, improve data collections and analysis (e.g., real-time data collections, imagery analysis), enhance methodologies to assess vulnerabilities, and more.

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ZHU, CHANGMING, and JIANSHENG WU. "HYBRID OF GENETIC ALGORITHM AND SIMULATED ANNEALING FOR SUPPORT VECTOR REGRESSION OPTIMIZATION IN RAINFALL FORECASTING." International Journal of Computational Intelligence and Applications 12, no. 02 (June 2013): 1350012. http://dx.doi.org/10.1142/s1469026813500120.

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Accurate forecasting of rainfall has been one of the most important issues in hydrological research such as river training works and design of flood warning systems. Support vector regression (SVR) is a popular regression method in rainfall forecasting. Type of kernel function and kernel parameter setting in the SVR traing procedure, along with the input feature subset selection, significantly influence regression accuracy. In this paper, an effective hybrid optimization strategy by combining the strengths of genetic algorithm (GA) and simulated annealing (SA), is employed to simultaneously optimize the input feature subset selection, the type of kernel function and the kernel parameter setting of SVR, namely GASA–SVR. The developed GASA–SVR model is being applied for monthly rainfall forecasting in Guilin of Guangxi. The GA is carried out as a main frame of this hybrid algorithm while SA is used as a local search strategy to help GA jump out of local optima and avoid sinking into the local optimal solution early. Compared with SVR, pure GA–SVR and HGA–SVR, results show that the hybrid GASA–SVR model can correctly select the discriminating input features subset, successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in rainfall forecasting, can also significantly improve the rainfall forecasting accuracy. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. Those results show that the proposed GASA–SVR model provides a promising alternative to monthly rainfall prediction.

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Tracy, Fred Thomas, Jodi L. Ryder, Martin T. Schultz, Ghada S. Ellithy, Benjamin R. Breland, T. Chris Massey, and Maureen K. Corcoran. "Monte Carlo Simulations of Coupled Transient Seepage Flow and Soil Deformation in Levees." Scalable Computing: Practice and Experience 21, no. 1 (March 19, 2020): 147–56. http://dx.doi.org/10.12694/scpe.v21i1.1629.

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The purpose of this research is to compare the results from two different computer programs of flow analysesof two levees at Port Arthur, Texas where rising water of a flood from Hurricane Ike occurred on the levees. The first program (Program 1) is a two-dimensional (2-D) transient finite element program that couples the conservation of mass flow equation with accompanying hydraulic boundary conditions with the conservation of force equations with accompanying x and y displacement and force boundary conditions, thus yielding total head, x displacement, and y displacement as unknowns at each finite element node. The second program (Program 2) is a 2-D transient finite element program that considers only the conservation of mass flowequation with its accompanying hydraulic boundary conditions, yielding only total head as the unknown at each finite element node. Compressive stresses can be computed at the centroid of each finite element when using the coupled program. Programs 1 and 2 were parallelized for high performance computing to consider thousands of realisations of the material properties. Since a single realisation requires as much as one hour of computer time for certain levees, the large realisation computation is made possible by utilising HPC. This Monte Carlo type analysis was used to compute the probability of unsatisfactory performance for under seepage, through seepage, and uplift for the two levees. Respective hydrographs from the flood resulting from Hurricane Ike were applied to each levee. When comparing the computations from the two programs, the most significant result was the two programs yielded significantly different values in the computed results in the two clay levees considered in this research.

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Puttinaovarat, Supattra, and Paramate Horkaew. "Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques." IEEE Access 8 (2020): 5885–905. http://dx.doi.org/10.1109/access.2019.2963819.

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32

Palchevsky, E. V., and V. V. Antonov. "Decision Support System based on Application of the Second Generation Neural Network." Programmnaya Ingeneria 13, no. 6 (June 22, 2022): 301–8. http://dx.doi.org/10.17587/prin.13.301-308.

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The issue of the feasibility of using existing statistical and hydrological methods for short-term and early forecasting in the framework of forecasting the levels of water rise in water bodies is considered: a comparative review is given, which describes their advantages and disadvantages. In the course of analyzing the shortcomings of these methods, the problem of operational and early (advance) forecasting of water rise levels was identified. To solve this problem, a decision support system is proposed for predicting the water rise levels in advance, based on a neural network (intelligent) analysis of retrospective data (date, water level, air temperature, atmospheric pressure and wind speed) to calculate the water level values for 5 days in advance. The artificial neural network itself is based on the freely distributed library of machine learning programs "TensorFlow", and a modified backpropagation method is used as training, the main difference of which is an increase in the learning rate of an artificial neural network. The results of the analysis of the effectiveness showed that the proposed decision support system is more accurate (the error between the real and calculated values does not exceed 2.10 %), compared to existing common methods/systems (8.36 %). This will allow to give the necessary time to special services for the implementation of flood control measures to prepare for the protection of technical facilities of enterprises.

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Acosta-Coll, Melisa, Andres Solano-Escorcia, Lilia Ortega-Gonzalez, and Ronald Zamora-Musa. "Forecasting and communication key elements for low-cost fluvial flooding early warning system in urban areas." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (October 1, 2021): 4143. http://dx.doi.org/10.11591/ijece.v11i5.pp4143-4156.

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Fluvial flooding occurs when a river overspills its banks due to excessive rainfall, and it is the most common flood event. In urban areas, the increment of urbanization makes communities more susceptible to fluvial flooding since the excess of impervious surfaces reduced the natural permeable areas. As flood prevention strategies, early warning systems (EWS) are used to reduce damage and protect people, but key elements need to be selected. This manuscript proposes the monitoring instruments, communication protocols, and media to forecast and disseminate EWS alerts efficiently during fluvial floods in urban areas. First, we conducted a systematic review of different EWS architectures for fluvial floods in urban areas and identified that not all projects monitor the most important variables related to the formation of fluvial floods and most use communication protocols with high-energy consumption. ZigBee and LoRaWAN are the communication protocols with lower power consumption from the review, and to determine which technology has better performance in urban areas, two wireless sensor networks were deployed and simulated in two urban areas susceptible to fluvial floods using Radio Mobile software. The results showed that although Zigbee technology has better-received signal strength, the difference with LoRAWAN is lower than 2 dBm, but LoRaWAN has a better signal-to-noise ratio, power consumption, coverage, and deployment cost.

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Varshanina, Tatiana, Rashid Khunagov, and Viktor Korobkov. "Information content of geoinformation computational visualization of natural object formation processes." InterCarto. InterGIS 28, no. 1 (2022): 508–22. http://dx.doi.org/10.35595/2414-9179-2022-1-28-508-522.

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The paper discusses informative possibilities and prospects of application of digital nature-like methodology for computational visualization of natural processes of natural system formation based on the proposed order parameter—intensity of integral geophysical field created by host structure-forming outer space. Since the designated order parameter defines the structure and properties of natural systems and processes, the gradient of the parameter defining the structure of the natural object is a measure of its order parameter and, therefore, can serve as a predictor of forecasting the change in its properties and structure. The declared approach is tested using an example of forecasting the process of flood formation and processes of visualization of tectonic stress fields. The authors have developed a method of point prediction of the onset time and flood level based on a three-level neural network model and a method of vector space-time visualization of a hierarchy of tectonic stress fields on the territory of an unlimited area. The research shows that computational operations with the parameter of the regional temperature gradient along with intelligent forecasting methods illustrate the prospects of point medium-, long-term forecasting of hydrometeorological processes provided with long rows of instrumental observation data. Computational visualization of general, background and local fields of tectonic stresses in the territories of unlimited area serves as a source of parametric data for geoinformation-mathematical modeling of tectonic stress field restructuring in processes of tectonosphere self-organization, calculation of position of geodynamic instability loci—epicenters of possible earthquakes, visualization of tectonic currents in the Earth’s crust. Monitoring geophysical data at geodynamic instability loci opens up prospects for point prediction of earthquakes. Calculation of order parameters of natural objects and processes opens up prospects of their computational modeling, derivation of numerical laws of their conjugate development and a scale series of interaction, as well as prediction of the state of geo objects and geo processes at a given geo space point in conditions of increasing global natural variability.

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Hofmann, Julian, and Holger Schüttrumpf. "Risk-Based and Hydrodynamic Pluvial Flood Forecasts in Real Time." Water 12, no. 7 (July 2, 2020): 1895. http://dx.doi.org/10.3390/w12071895.

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The effective forecast and warning of pluvial flooding in real time is one of the key elements and remaining challenges of an integrated urban flood risk management. This paper presents a new methodology for integrating risk-based solutions and 2D hydrodynamic models into the early warning process. Whereas existing hydrodynamic forecasting methods are based on rigid systems with extremely high computational demands, the proposed framework builds on a multi-model concept allowing the use of standard computer systems. As a key component, a pluvial flood alarm operator (PFA-Operator) is developed for selecting and controlling affected urban subcatchment models. By distributed computing of hydrologic independent models, the framework overcomes the issue of high computational times of hydrodynamic simulations. The PFA-Operator issues warnings and flood forecasts based on a two-step process: (1) impact-based rainfall thresholds for flood hotspots and (2) hydrodynamic real-time simulations of affected urban subcatchments models. Based on the open-source development software Qt, the system can be equipped with interchangeable modules and hydrodynamic software while building on the preliminary results of flood risk analysis. The framework was tested using a historic pluvial flood event in the city of Aachen, Germany. Results indicate the high efficiency and adaptability of the proposed system for operational warning systems in terms of both accuracy and computation time.

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Fitriansyah, Fitriansyah, Miftahul Iman, and Aminullah Aminullah. "Pemodelan Numerik Kekuatan Pintu Air Baja." Media Ilmiah Teknik Sipil 9, no. 2 (June 1, 2021): 116–23. http://dx.doi.org/10.33084/mits.v9i2.2098.

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The intake building for Embung Sei Bubu had been designed to be an open channel with the flood gate. The flood gate had been designed from steel with a net width of 1.35 m and the height of the door opening is 0.0076 m. The corrosion is one of the hazardous threat to the strengthness and durability of the flood gate. This research numerically models flood gate that was attacked by pitting corrosion. The pitting corrosion had been modelled in several small holes randomly were distributed on the surface of the flood gate, precisely on the surface of the water. The numerical modeling had been performed in finite element method utilized computer programs such Abaqus. The results showed there was a reduction in the capacity of the steel flood gate due the hole increasing. The reduction in stress capacity had been indicated by the stress concentration that was occured around the pitting corrosion. The stress reduction occured with the change in the percentage of pitting corrosion distribution area of 10% (225 MPa), 20% (175 MPa) and 30% (120 MPa)

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37

Darnell, MSCE, EIT, Andrew, Richard Wise, MSCE, EIT, and John Quaranta, PhD, PE. "Comparison of ArcToolbox and Terrain Tiles processing procedures for inundation mapping in mountainous terrain." Journal of Emergency Management 11, no. 2 (February 16, 2017): 133. http://dx.doi.org/10.5055/jem.2013.0132.

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Floodplain management consists of efforts to reduce flood damage to critical infrastructure and to protect the life and health of individuals from flooding. A major component of this effort is the monitoring of flood control structures such as dams because the potential failure of these structures may have catastrophic consequences. To prepare for these threats, engineers use inundation maps that illustrate the flood resulting from high river stages. To create the maps, the structure and river systems are modeled using engineering software programs, and hydrologic events are used to simulate the conditions leading to the failure of the structure. The output data are then exported to other software programs for the creation of inundation maps. Although the computer programs for this process have been established, the processing procedures vary and yield inconsistent results. Thus, these processing methods need to be examined to determine the functionality of each in floodplain management practices. The main goal of this article is to present the development of a more integrated, accurate, and precise graphical interface tool for interpretation by emergency managers and floodplain engineers. To accomplish this purpose, a potential dam failure was simulated and analyzed for a candidate river system using two processing methods: ArcToolbox and Terrain Tiles. The research involved performing a comparison of the outputs, which revealed that both procedures yielded similar inundations for single river reaches. However, the results indicated key differences when examining outputs for large river systems. On the basis of criteria involving the hydrologic accuracy and effects on infrastructure, the Terrain Tiles inundation surpassed the ArcToolbox inundation in terms of following topography and depicting flow rates and flood extents at confluences, bends, and tributary streams. Thus, the Terrain Tiles procedure is a more accurate representation of flood extents for use by floodplain engineers, hydrologists, geographers, and emergency managers.

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Martínez Cervera, Daniel Esteban, Octavio José Salcedo Parra, and Marco Antonio Aguilera Prado. "Forecasting model with machine learning in higher education ICFES exams." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (December 1, 2021): 5402. http://dx.doi.org/10.11591/ijece.v11i6.pp5402-5410.

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In this paper, we proposed to make different forecasting models in the University education through the algorithms K-means, K-closest neighbor, neural network, and naïve Bayes, which apply to specific exams of engineering, licensed and scientific mathematical thinking in Saber Pro of Colombia. ICFES Saber Pro is an exam required for the degree of all students who carry out undergraduate programs in higher education. The Colombian government regulated this exam in 2009 in the decree 3963 intending to verify the development of competencies, knowledge level, and quality of the programs and institutions. The objective is to use data to convert into information, search patterns, and select the best variables and harness the potential of data (average 650.000 data per semester). The study has found that the combination of features was: women have greater participation (68%) in Mathematics, Engineering, and Teaching careers, the urban area continues to be the preferred place to apply for higher studies (94%), Internet use increased by 50% in the last year, the support of the family nucleus is still relevant for the support in the formation of the children.

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Melby, Jeffrey, Norberto Caraballo-Nadal, and Nobuhisa Kobayashi. "WAVE RUNUP PREDICTION FOR FLOOD MAPPING." Coastal Engineering Proceedings 1, no. 33 (December 28, 2012): 79. http://dx.doi.org/10.9753/icce.v33.management.79.

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Wave runup determines the extent over which waves act. Wave runup is therefore an important parameter to determine flood inundation extents from coastal storms. Cross-shore and longshore sediment transport are a function of the hydrodynamics on the beach and are therefore related to wave runup. Several benchmark wave runup data sets are summarized and used to evaluate the available tools for predicting wave runup for flood hazard assessment. Benchmark data span a range of shoreline conditions including sandy beaches on the Pacific and Atlantic coasts, dissipative to reflective beaches, as well as structures ranging from impermeable smooth levees to rough permeable rubble mounds. Data include laboratory and prototype measurements. Tools for predicting wave runup are analyzed including empirical equations, computer programs based on empirical equations, and the CSHORE time-averaged cross-shore model. Most of the tools show fairly high degrees of skill but some do not. The study recommends using CSHORE to model runup for most beach and structure conditions. However, CSHORE is not likely to predict wave runup on infragravity-dominated dissipative beaches well. For these cases, it is recommended that one of the recommended empirical equations for beaches be used.

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40

Hadid, Baya, Eric Duviella, and Stéphane Lecoeuche. "Data-driven modeling for river flood forecasting based on a piecewise linear ARX system identification." Journal of Process Control 86 (February 2020): 44–56. http://dx.doi.org/10.1016/j.jprocont.2019.12.007.

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41

Lee, Jae-Yeong, and Ji-Sung Kim. "Detecting Areas Vulnerable to Flooding Using Hydrological-Topographic Factors and Logistic Regression." Applied Sciences 11, no. 12 (June 18, 2021): 5652. http://dx.doi.org/10.3390/app11125652.

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As a result of rapid urbanization and population movement, flooding in urban areas has become one of the most common types of natural disaster, causing huge losses of both life and property. To mitigate and prevent the damage caused by the recent increase in floods, a number of measures are required, such as installing flood prevention facilities, or specially managing areas vulnerable to flooding. In this study, we presented a technique for determining areas susceptible to flooding using hydrological-topographic characteristics for the purpose of managing flood vulnerable areas. To begin, we collected digital topographic maps and stormwater drainage system data regarding the study area. Using the collected data, surface, locational, and resistant factors were analyzed. In addition, the maximum 1-h rainfall data were collected as an inducing factor and assigned to all grids through spatial interpolation. Next, a logistic regression analysis was performed by inputting hydrological-topographic factors and historical inundation trace maps for each grid as independent and dependent variables, respectively, through which a model for calculating the flood vulnerability of the study area was established. The performance of the model was evaluated by analyzing the receiver operating characteristics (ROC) curve of flood vulnerability and inundation trace maps, and it was found to be improved when the rainfall that changes according to flood events was also considered. The method presented in this study can be used not only to reasonably and efficiently select target sites for flood prevention facilities, but also to pre-detect areas vulnerable to flooding by using real-time rainfall forecasting.

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Zhao, Jun Hua, Jing Ning, Suo Ying Mao, and Wei Feng Xu. "Design and Implementation of an Automatic Hydrological Telemetry System." Applied Mechanics and Materials 511-512 (February 2014): 752–56. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.752.

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It is very significant for hydrological telemetry system to gather such real-time information as rainfall and water level of reservoir in order to improve forecasting level of flood prevention and flood disaster. In this paper, a novel hydrological telemetry system is presented. The system mainly consists of remote terminal units (RTUs) and monitoring center. The RTU is mainly designed to collect rainfall and water level, and send this information to the host computer in the monitoring center by GPRS network or other wireless network. The RTU is completely implemented on a low power consumption hardware platform. The host computer receives the data from the RTUs and analyzes them, then gives detailed tables, diagrams and some decision-making conclusions, which helps the competent authorities of the reservoir or the dam to realize remote monitoring and alarming system. The design principles, difficulties and skills are discussed detailedly in the paper. The low power consumption of the RTU and bit error rate for GPRS communication are both tested. A prototype is developed to validate above design cruces.

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43

Kulkarni, Aseema, and Ajit More. "Formulation of a Prediction Index with the Help of WEKA Tool for Guiding the Stock Market Investors." Oriental journal of computer science and technology 9, no. 3 (October 24, 2016): 212–25. http://dx.doi.org/10.13005/ojcst/09.03.07.

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Prediction of stock prices using various computer programs is on rise. Popularly known in the field of finance as algorithmic trading, a radical transformation has taken place in the field of stock markets for decision making through automated decision making agents. Machine learning techniques can be applied for predicting stock prices. This paper attempts to study the various stock market forecasting processes available in the forecasting plugin of the WEKA tool. Twenty experiments have been conducted on twenty different stocks to analyse the prediction capacity of the tool.

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44

Wise, MSCE, EIT, Richard, Andrew Darnell, MSCE, EIT, and John Quaranta, PhD, PE. "Critical review of Terrain Tile and Google Earth: Virtual image mapping methods for floodplain management." Journal of Emergency Management 10, no. 6 (March 21, 2018): 433. http://dx.doi.org/10.5055/jem.2012.0120.

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Inundation mapping is a major component of floodplain management, providing critical information as to the consequences of potential failures of flood control structures. Flood mitigation efforts rely on the creation of inundation maps to develop appropriate response measures for crisis situations, including dam failures. To develop inundation maps, a dam and river system is modeled with engineering computer programs, and a simulation of the dam failure is performed to generate data for the flood. This output data are input into other programs to develop inundation maps. Inundation maps have traditionally been produced in a paper format, but recent advances in computer modeling have provided the capability for virtual inundation maps. Virtual inundation maps offer new methods of presentation and analysis of flood impacts; thus, these mapping methods need to be investigated to determine the applications and relevance to floodplain management. The goal of this research is to advance the development and use of inundation maps by floodplain managers and emergency agencies. A simulation of a potential dam failure was performed using computer modeling for a candidate river system, and the inundation maps were created using two procedures: Terrain Tiles and Google Earth. An analysis of the strengths and weaknesses of each mapping procedure was conducted. The results indicated that the Terrain Tiles procedure has advantages in displaying critical information, such as arrival times and water depths. However, this mapping procedure is more labor intensive, and the online file sharing may not be accessible for all users. The strengths of the Google Earth procedure include two-dimensional and three-dimensional views for analysis, user-friendly file sharing, and the inclusion of built-in critical infrastructure and terrain data. Drawbacks of this procedure are that the inundation must still be generated in ArcGIS, the display of critical information is not as clear, and the online file sharing may pose security issues. Thus, the Terrain Tiles procedure should be used for the development of emergency response measures, and the Google Earth procedure should be used by emergency responders in the event of an actual emergency.

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45

Tuyen, Do Ngoc, Tran Manh Tuan, Le Hoang Son, Tran Thi Ngan, Nguyen Long Giang, Pham Huy Thong, Vu Van Hieu, Vassilis C. Gerogiannis, Dimitrios Tzimos, and Andreas Kanavos. "A Novel Approach Combining Particle Swarm Optimization and Deep Learning for Flash Flood Detection from Satellite Images." Mathematics 9, no. 22 (November 10, 2021): 2846. http://dx.doi.org/10.3390/math9222846.

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Flood is one of the deadliest natural hazards worldwide, with the population affected being more than 2 billion between 1998–2017 with a lack of warning systems according to WHO. Especially, flash floods have the potential to generate fatal damages due to their rapid evolution and the limited warning and response time. An effective Early Warning Systems (EWS) could support detection and recognition of flash floods. Information about a flash flood can be mainly provided from observations of hydrology and from satellite images taken before the flash flood happens. Then, predictions from satellite images can be integrated with predictions based on sensors’ information to improve the accuracy of a forecasting system and subsequently trigger warning systems. The existing Deep Learning models such as UNET has been effectively used to segment the flash flood with high performance, but there are no ways to determine the most suitable model architecture with the proper number of layers showing the best performance in the task. In this paper, we propose a novel Deep Learning architecture, namely PSO-UNET, which combines Particle Swarm Optimization (PSO) with UNET to seek the best number of layers and the parameters of layers in the UNET based architecture; thereby improving the performance of flash flood segmentation from satellite images. Since the original UNET has a symmetrical architecture, the evolutionary computation is performed by paying attention to the contracting path and the expanding path is synchronized with the following layers in the contracting path. The UNET convolutional process is performed four times. Indeed, we consider each process as a block of the convolution having two convolutional layers in the original architecture. Training of inputs and hyper-parameters is performed by executing the PSO algorithm. In practice, the value of Dice Coefficient of our proposed model exceeds 79.75% (8.59% higher than that of the original UNET model). Experimental results on various satellite images prove the advantages and superiority of the PSO-UNET approach.

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46

McGrath, H., E. Stefanakis, and M. Nastev. "RAPID RISK EVALUATION (ER2) USING MS EXCEL SPREADSHEET: A CASE STUDY OF FREDERICTON (NEW BRUNSWICK, CANADA)." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-8 (June 7, 2016): 27–34. http://dx.doi.org/10.5194/isprsannals-iii-8-27-2016.

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Анотація:

Conventional knowledge of the flood hazard alone (extent and frequency) is not sufficient for informed decision-making. The public safety community needs tools and guidance to adequately undertake flood hazard risk assessment in order to estimate respective damages and social and economic losses. While many complex computer models have been developed for flood risk assessment, they require highly trained personnel to prepare the necessary input (hazard, inventory of the built environment, and vulnerabilities) and analyze model outputs. As such, tools which utilize open-source software or are built within popular desktop software programs are appealing alternatives. The recently developed Rapid Risk Evaluation (ER2) application runs scenario based loss assessment analyses in a Microsoft Excel spreadsheet. User input is limited to a handful of intuitive drop-down menus utilized to describe the building type, age, occupancy and the expected water level. In anticipation of local depth damage curves and other needed vulnerability parameters, those from the U.S. FEMA’s Hazus-Flood software have been imported and temporarily accessed in conjunction with user input to display exposure and estimated economic losses related to the structure and the content of the building. Building types and occupancies representative of those most exposed to flooding in Fredericton (New Brunswick) were introduced and test flood scenarios were run. The algorithm was successfully validated against results from the Hazus-Flood model for the same building types and flood depths.

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47

McGrath, H., E. Stefanakis, and M. Nastev. "RAPID RISK EVALUATION (ER²) USING MS EXCEL SPREADSHEET: A CASE STUDY OF FREDERICTON (NEW BRUNSWICK, CANADA)." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-8 (June 7, 2016): 27–34. http://dx.doi.org/10.5194/isprs-annals-iii-8-27-2016.

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Анотація:

Conventional knowledge of the flood hazard alone (extent and frequency) is not sufficient for informed decision-making. The public safety community needs tools and guidance to adequately undertake flood hazard risk assessment in order to estimate respective damages and social and economic losses. While many complex computer models have been developed for flood risk assessment, they require highly trained personnel to prepare the necessary input (hazard, inventory of the built environment, and vulnerabilities) and analyze model outputs. As such, tools which utilize open-source software or are built within popular desktop software programs are appealing alternatives. The recently developed Rapid Risk Evaluation (ER2) application runs scenario based loss assessment analyses in a Microsoft Excel spreadsheet. User input is limited to a handful of intuitive drop-down menus utilized to describe the building type, age, occupancy and the expected water level. In anticipation of local depth damage curves and other needed vulnerability parameters, those from the U.S. FEMA’s Hazus-Flood software have been imported and temporarily accessed in conjunction with user input to display exposure and estimated economic losses related to the structure and the content of the building. Building types and occupancies representative of those most exposed to flooding in Fredericton (New Brunswick) were introduced and test flood scenarios were run. The algorithm was successfully validated against results from the Hazus-Flood model for the same building types and flood depths.

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48

Chitwatkulsiri, Detchphol, Hitoshi Miyamoto, Kim Neil Irvine, Sitang Pilailar, and Ho Huu Loc. "Development and Application of a Real-Time Flood Forecasting System (RTFlood System) in a Tropical Urban Area: A Case Study of Ramkhamhaeng Polder, Bangkok, Thailand." Water 14, no. 10 (May 20, 2022): 1641. http://dx.doi.org/10.3390/w14101641.

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In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused severe damage and prompted a renewed focus to manage their impacts. The development of a real-time warning system could provide timely information to initiate flood management protocols, thereby reducing impacts. Therefore, we developed an innovative real-time flood forecasting system (RTFlood system) and applied it to the Ramkhamhaeng polder in Bangkok, which is particularly vulnerable to flash floods. The RTFlood system consists of three modules. The first module prepared rainfall input data for subsequent use by a hydraulic model. This module used radar rainfall data measured by the Bangkok Metropolitan Administration and developed forecasts using the TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) rainfall model. The second module provided a real-time task management system that controlled all processes in the RTFlood system, i.e., input data preparation, hydraulic simulation timing, and post-processing of the output data for presentation. The third module provided a model simulation applying the input data from the first and second modules to simulate flash floods. It used a dynamic, conceptual model (PCSWMM, Personal Computer version of the Stormwater Management Model) to represent the drainage systems of the target urban area and predict the inundation areas. The RTFlood system was applied to the Ramkhamhaeng polder to evaluate the system’s accuracy for 116 recent flash floods. The result showed that 61.2% of the flash floods were successfully predicted with accuracy high enough for appropriate pre-warning. Moreover, it indicated that the RTFlood system alerted inundation potential 20 min earlier than separate flood modeling using radar and local rain stations individually. The earlier alert made it possible to decide on explicit flood controls, including pump and canal gate operations.

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Melnik, V. "IMPROVING THE QUALITY OF LOGISTICS SUPPORTED ON THE BASIS OF FORECASTING MODELS AND APPLICATION OF COMPUTER PROGRAMS." Collection of scientific works of Odesa Military Academy 2, no. 14 (January 25, 2021): 164–68. http://dx.doi.org/10.37129/2313-7509.2020.14.2.164-168.

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At the present stage of development of the Armed Forces of Ukraine, tasks are being solved for further development of the established logistics system, which is based on the experience gained in conducting anti-terrorist operations in Donetsk and Luhansk regions (Joint Forces operations), automation of accounting processes, other principles and standards of logistics. security requirements applicable in NATO member countries. Analysis of the functioning of the existing system of logistics of the Armed Forces of Ukraine, draws attention to some problematic issues that may hinder the successful solution of the tasks assigned to this system. One of the main issues that needs to be addressed immediately is the issue of improving the management system of logistics through the use of military management software and hardware tools for automated support of the processes of organization, management and control. The article proposes the application of modern information technologies based on mathematical modeling of processes and computerization of complex computational processes in order to select targeted measures to improve the optimization of the operation of weapons and military equipment, including the critical interval of their operation. In addition, a promising direction of information support for the use of weapons by introducing a statistical model to predict the dynamics of changes in the service life of the residual normalized resource over the course of a typical sample of weapons and a statistical model to predict the dynamics of changes in the service life of this model of weapons. Keywords: armaments and military equipment, organization of logistical support, residual normalized resource, readiness factor, computer specialized programs.

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50

Mishra, Debesh, and Suchismita Satapathy. "MCDM Approach for Mitigation of Flooding Risks in Odisha (India) Based on Information Retrieval." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 2 (April 2020): 77–91. http://dx.doi.org/10.4018/ijcini.2020040105.

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Multi-criteria decision-making (MCDM) provides a suitable platform for groups as well as promotion of the participants' role in decision processes. This also enables the development of real participatory processes essential for the successful implementation and sustainable flood management programs. The present study contributes by applying two MCDM approaches for weighting the criteria related to the environmental impacts of flooding. Moreover, an attempt was made in this study by an extensive review of literature, and consultations with experts to identify the environmental impacts of flooding in Odisha State (India). Then, the Best Worst Method (BWM) followed by the Step-Wise Weight Assessment Ratio Analysis (SWARA) method was used to rank the environmental impacts which were considered as the risk factors. The result of this study will be useful to the governance system for an effective and proper planning, and implementation of flood mitigation projects.

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Статті в журналах з теми "Flood forecasting Computer programs"

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