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Статті в журналах з теми "Flood forecasting Computer programs"
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.
Повний текст джерелаRakhymberdina, 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.
Повний текст джерелаBenoit, 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<sup>*</sup>." Hydrology and Earth System Sciences 7, no. 6 (December 31, 2003): 877–89. http://dx.doi.org/10.5194/hess-7-877-2003.
Повний текст джерелаThirumalaiah, 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.
Повний текст джерелаBhola, 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.
Повний текст джерелаPuttinaovarat, 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.
Повний текст джерелаSupatmi, 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.
Повний текст джерелаLeedal, 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.
Повний текст джерелаJohnson, 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.
Повний текст джерелаDing, 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.
Повний текст джерелаДисертації з теми "Flood forecasting Computer programs"
Brownell, Dorie Lynn. "Application of a Geographical Information System to Estimate the Magnitude and Frequency of Floods in the Sandy and Clackamas River Basins, Oregon." PDXScholar, 1995. https://pdxscholar.library.pdx.edu/open_access_etds/4877.
Повний текст джерелаVaroonchotikul, Pichaid. "Flood forecasting using artificial neural networks /." Lisse : Balkema, 2003. http://www.e-streams.com/es0704/es0704_3168.html.
Повний текст джерелаNilsson, Andreas. "FloodViewer : Web-based visual interface to a flood forecasting system." Thesis, Linköping University, Department of Science and Technology, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1394.
Повний текст джерелаThis diploma work has been done as a part of the EC funded projects, MUSIC VK1- CT-2000-00058 and SmartDoc IST-2000-28137. The objective was to create an intuitive and easy to use visualization of flood forecasting data provided in the MUSIC project. This visualization is focused on the Visual User Interface and is built on small, reusable components. The visualization, FloodViewer, is small enough to ensure the possibility of distribution via the Internet, yet capable of enabling collaboration possibilities and embedment in electronic documents of the entire visualization. Thus, FloodViewer has been developed in three versions for different purposes.
Analysis and report generation (FloodViewer ) Collaborative analysis (FloodViewerNet ) Presentation and documentation (FloodViewerX).
Zarekarizi, Mahkameh. "Ensemble Data Assimilation for Flood Forecasting in Operational Settings: from Noah-MP to WRF-Hydro and the National Water Model." PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4651.
Повний текст джерелаDunn, Adam. "A model of wildfire propagation using the interacting spatial automata formalism." University of Western Australia. School of Computer Science and Software Engineering, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0071.
Повний текст джерелаCwalinski, Tim A. "Simulated forecasting of yellow perch (Perca flavescens) relative population density for Indiana waters of Lake Michigan : responses to varying harvest and alewife density." Virtual Press, 1996. http://liblink.bsu.edu/uhtbin/catkey/1036196.
Повний текст джерелаDepartment of Biology
Nigrini, Lucas Bernardo. "Developing a neural network model to predict the electrical load demand in the Mangaung municipal area." Thesis, [Bloemfontein?] : Central University of Technology, Free State, 2012. http://hdl.handle.net/11462/176.
Повний текст джерелаBecause power generation relies heavily on electricity demand, consumers are required to wisely manage their loads to consolidate the power utility‟s optimal power generation efforts. Consequently, accurate and reliable electric load forecasting systems are required. Prior to the present situation, there were various forecasting models developed primarily for electric load forecasting. Modelling short term load forecasting using artificial neural networks has recently been proposed by researchers. This project developed a model for short term load forecasting using a neural network. The concept was tested by evaluating the forecasting potential of the basic feedforward and the cascade forward neural network models. The test results showed that the cascade forward model is more efficient for this forecasting investigation. The final model is intended to be a basis for a real forecasting application. The neural model was tested using actual load data of the Bloemfontein reticulation network to predict its load for half an hour in advance. The cascade forward network demonstrates a mean absolute percentage error of less than 5% when tested using four years of utility data. In addition to reporting the summary statistics of the mean absolute percentage error, an alternate method using correlation coefficients for presenting load forecasting performance results are shown. This research proposes that a 6:1:1 cascade forward neural network can be trained with data from a month of a year and forecast the load for the same month of the following year. This research presents a new time series modeling for short term load forecasting, which can model the forecast of the half-hourly loads of weekdays, as well as of weekends and public holidays. Obtained results from extensive testing on the Bloemfontein power system network confirm the validity of the developed forecasting approach. This model can be implemented for on-line testing application to adopt a final view of its usefulness.
Pearcy, Charles M. "The impact of background resolution on Target Acquisitions Weapons Software (TAWS) sensor performance." Thesis, Monterey, California. Naval Postgraduate School, 2005. http://hdl.handle.net/10945/2232.
Повний текст джерелаThis study evaluated the sensitivity of TAWS detection range calculations to the spatial resolution of scenario backgrounds. Sixteen independent sites were analyzed to determine TAWS background. Multispectral satellite data were processed to different spatial resolutions from 1m to 8km. The resultant imagery was further processed to determine TAWS background type. The TAWS background type was refined to include soil moisture characteristics. Soil moisture analyses were obtained using in situ measurements, the Air Force's Agricultural-Meteorological (AGRMET) model and the Army's Fast All-seasons Soil Strength (FASST) model. The analyzed imagery was compared to the current default 1o latitude by 1o of longitude database in TAWS. The use of the current default TAWS background database was shown to result in TAWS ranges differing from the 1m standard range by 18-23%. The uncertainty was reduced to 5% when background resolution was improved to 8km in rural areas. By contrast, in urban regions the uncertainty was reduced to 14% when spatial resolution was reduced to 30m. These results suggest that the rural and urban designations are important to the definition of a background database.
First Lieutenant, United States Air Force
Lumsden, Trevor Graeme. "Development and evaluation of a sugarcane yield forecasting system." Thesis, 2000. http://hdl.handle.net/10413/4955.
Повний текст джерелаThesis (M.Sc.)-University of Natal, Pietermaritzburg, 2000.
Pillay, Maldean. "Gabor filter parameter optimization for multi-textured images : a case study on water body extraction from satellite imagery." Thesis, 2012. http://hdl.handle.net/10413/11070.
Повний текст джерелаThesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2012.
Книги з теми "Flood forecasting Computer programs"
Flood forecasting using artificial neural networks. Lisse, Netherlands: Balkema, 2003.
Знайти повний текст джерелаPrettenthaler, Franz, and Hansjörg Albrecher. Hochwasserrisiko und dessen Versicherung in Österreich: Evaluierung und ökonomische Analyse des von der Versicherungswirtschaft vorgeschlagenen Modells NatKat. Wien: Verlag der österreichischen Akademie der Wissenschaften, 2009.
Знайти повний текст джерелаJones, Perry M. Characterization of rainfall-runoff response and estimation of the effect of wetland restoration on runoff, Heron Lake basin, southwestern Minnesota, 1991-97. Mounds View, Minn: U.S. Dept. of the Interior, U.S. Geological Survey, 2000.
Знайти повний текст джерелаFlynn, Kathleen M. User's manual for Program PeakFQ, annual flood-frequency analysis using Bulletin 17B guidelines. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2006.
Знайти повний текст джерелаKeiichi, Toda, Miguez Marcelo Gomes, and Inoue Kazuya 1941-, eds. Flood risk simulation. Southampton: WIT, 2005.
Знайти повний текст джерелаCaroline, Peani, and Peani Pablo, eds. Forecasting software manual. Cincinnati, Ohio: South-Western College Pub., 1999.
Знайти повний текст джерелаBerenbrock, Charles. Simulation of water-surface elevations for a hypothetical 100-year peak flow in Birch Creek at the Idaho National Engineering and Environmental Laboratory, Idaho. Boise, Idaho: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.
Знайти повний текст джерелаBerenbrock, Charles. Simulation of water-surface elevations for a hypothetical 100-year peak flow in Birch Creek at the Idaho National Engineering and Environmental Laboratory, Idaho. Boise, Idaho: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.
Знайти повний текст джерелаBerenbrock, Charles. Simulation of water-surface elevations for a hypothetical 100-year peak flow in Birch Creek at the Idaho National Engineering and Environmental Laboratory, Idaho. Boise, Idaho: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.
Знайти повний текст джерелаBerenbrock, Charles. Simulation of water-surface elevations for a hypothetical 100-year peak flow in Birch Creek at the Idaho National Engineering and Environmental Laboratory, Idaho. Boise, Idaho: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.
Знайти повний текст джерелаЧастини книг з теми "Flood forecasting Computer programs"
Freudenthaler, Bernhard, and Reinhard Stumptner. "Adaptive Flood Forecasting for Small Catchment Areas." In Computer Aided Systems Theory – EUROCAST 2015, 211–18. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27340-2_27.
Повний текст джерелаYan, Yaoxing, and Yutao Cao. "A Mode of Storm Flood Forecasting DSS Establish Ion." In Communications in Computer and Information Science, 261–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24282-3_35.
Повний текст джерелаGamage, Dhananjali, and Kalani Ilmini. "Flood Forecasting Using Artificial Neural Network for Kalu Ganga." In Communications in Computer and Information Science, 92–102. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9129-3_7.
Повний текст джерелаWang, Wangsong, and Yan Tang. "Watershed Flood Forecasting Based on Cluster Analysis and BP Neural Network." In Computer Supported Cooperative Work and Social Computing, 498–506. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3044-5_37.
Повний текст джерелаLiao, Weihong, and Xiaohui Lei. "Multi-model Combination Techniques for Flood Forecasting from the Distributed Hydrological Model EasyDHM." In Communications in Computer and Information Science, 396–402. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34289-9_44.
Повний текст джерелаYan, Le, Jun Feng, Yirui Wu, and Tingting Hang. "Data-Driven Fast Real-Time Flood Forecasting Model for Processing Concept Drift." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 363–74. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48513-9_30.
Повний текст джерелаBurkard, Simon, Frank Fuchs-Kittowski, and Anna O’Faolain de Bhroithe. "Mobile Crowd Sensing of Water Level to Improve Flood Forecasting in Small Drainage Areas." In Environmental Software Systems. Computer Science for Environmental Protection, 124–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-89935-0_11.
Повний текст джерелаSeal, Victor, Arnab Raha, Shovan Maity, Souvik Kr Mitra, Amitava Mukherjee, and Mrinal Kanti Naskar. "A Real Time Multivariate Robust Regression Based Flood Prediction Model Using Polynomial Approximation for Wireless Sensor Network Based Flood Forecasting Systems." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 432–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27317-9_44.
Повний текст джерелаSolo, Ashu M. G. "The Interdisciplinary Fields of Political Engineering, Public Policy Engineering, Computational Politics, and Computational Public Policy." In Handbook of Research on Politics in the Computer Age, 1–16. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0377-5.ch001.
Повний текст джерелаSerban, Cristina, and Carmen Maftei. "Using Grid Computing and Satellite Remote Sensing in Evapotranspiration Estimation." In Biometrics, 994–1016. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0983-7.ch039.
Повний текст джерелаТези доповідей конференцій з теми "Flood forecasting Computer programs"
Abbassi, Kamel, Hamadi Lirathni, Mohamed Hechmi Jeridi, and Tahar Ezzedine. "Flood Forecasting with Bayesian Approach." In 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, 2021. http://dx.doi.org/10.23919/softcom52868.2021.9559122.
Повний текст джерелаAntony Sylvia, J. Michael, M. Pushpa Rani, and Bashiru Aremu. "Analysis of IoT Big Weather Data For Early Flood Forecasting System." In 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2021. http://dx.doi.org/10.1109/icecct52121.2021.9616941.
Повний текст джерелаSoomlek, Chitsutha, Nattawadee Kaewchainam, Thawat Simano, and Chakchai So-In. "Using backpropagation neural networks for flood forecasting in PhraNakhon Si Ayutthaya, Thailand." In 2015 International Computer Science and Engineering Conference (ICSEC). IEEE, 2015. http://dx.doi.org/10.1109/icsec.2015.7401424.
Повний текст джерелаFinger, Alice, and Aline Loreto. "Applications of Numerical Methods with Linear Complexity in Flood Forecasting in Rivers." In 2011 Workshop-School on Theoretical Computer Science (WEIT). IEEE, 2011. http://dx.doi.org/10.1109/weit.2011.32.
Повний текст джерелаGomes, João L., Gonçalo Jesus, João Rogeiro, Anabela Oliveira, Ricardo da Costa, and André B. Fortunato. "Molines – towards a responsive web platform for flood forecasting and risk mitigation." In 2015 Federated Conference on Computer Science and Information Systems. IEEE, 2015. http://dx.doi.org/10.15439/2015f265.
Повний текст джерелаUllah, Kaleem, Zahid Ullah, Irfanullah Khan, Fazale Wahab, Waqar Uddin, Athar Waseem, Aun Haider, Ghulam Hafeez, Sahibzada Muhammad Ali, and Khadim Ullah Jan. "Load Forecasting Schemes and Demand Response Programs within Smart Grid." In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). IEEE, 2020. http://dx.doi.org/10.1109/icecce49384.2020.9179280.
Повний текст джерелаYin, Zhiyuan, Fang Yang, and Tieyuan Shen. "Application of Set Pair Analysis on QPE and Rain Gauge in Flood Forecasting." In 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/isaeece-17.2017.10.
Повний текст джерелаZainorzuli, Siti Maisarah, Syahrul Afzal Che Abdullah, Ramli Adnan, and Fazlina Ahmat Ruslan. "Comparative Study of Elman Neural Network (ENN) and Neural Network Autoregressive With Exogenous Input (NARX) For Flood Forecasting." In 2019 IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2019. http://dx.doi.org/10.1109/iscaie.2019.8743796.
Повний текст джерелаDeshmukh, Rahul P., and A. A. Ghatol. "Notice of Retraction: Comparative study of Jorden and Elman model of neural network for short term flood forecasting." In 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccsit.2010.5564917.
Повний текст джерелаWang, Kai, Mingkai Qian, Shunfeng Peng, Shijin Xu, Fengsheng Li, and Min Xu. "The Deft-FEWS flood forecasting system and its application in the middle-upper parts of the Huaihe River Basin, China." In 2013 International Conference on Software Engineering and Computer Science. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/icsecs-13.2013.43.
Повний текст джерела