Relevant bibliographies by topics / Flood forecasting Statistical methods

Academic literature on the topic 'Flood forecasting Statistical methods'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Flood forecasting Statistical methods.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Flood forecasting Statistical methods"

1

Mandryk, Oleg, Andriy Oliynyk, Roman Mykhailyuk, and Lidiia Feshanych. "Flood Development Process Forecasting Based on Water Resources Statistical Data." Grassroots Journal of Natural Resources 4, no. 2 (May 30, 2021): 65–76. http://dx.doi.org/10.33002/nr2581.6853.040205.

Full text
Abstract:
The Ukrainian Carpathians is the territory with a great threat of floods. This is due to natural and climatic conditions of this region, which is characterized by mountainous terrain, high density of hydrological network and a significant amount of precipitation. Amount of precipitation here ranges from 600 mm on plains to 1,600 mm on mountain tops. The main factors of floods occurrence are excessive precipitation, low water permeability of soil and a high proportion of low-permeability rocks (flysch layers with a predominance of clay layers). Therefore, catastrophic floods in the region were also observed in previous centuries, when the anthropogenic impact on the environment, including forest ecosystems, was not comparable with the current one. Any flood is characterized by a period of development, a period of its critical (maximum) intensity and a period of decline. In the present paper, based on the use of methods for approximating the curves and the results of experimental studies of flood waters, a method of mathematical description and forecasting of the flood development is suggested. The recommended direction of further research may be related to the development of experimental means to determine the parameters that affect the process of flood formation.
APA, Harvard, Vancouver, ISO, and other styles
2

Margaryan, Varduhi, Ekaterina Gaidukova, and Gennady Tsibulskii. "Methods for long-term forecasting of water availability in spring floods (r. Arpa – p. Jermuk)." E3S Web of Conferences 333 (2021): 02007. http://dx.doi.org/10.1051/e3sconf/202133302007.

Full text
Abstract:
The article discusses the main physical and geographical factors, affecting the runoff of spring floods in the Arpa rivers catchment in the station Jermuk. Also the article discusses the development of a methodology for long-term forecasting of runoff volume of spring flood (WIV–VI) of river Arpa, station Jermuk. The study used data of water discharges of Arpa river catchment (station Jermuk), air temperature, precipitation, reserve water in snow at meteostation Jermuk. A linear correlation was also revealed between the values of the annual runoff and runoff of spring floods in Arpa river catchment, which can be used to predict the annual runoff. To predict the volume of spring flood runoff, regression method and obtained multivariate correlation dependencies. Assessment of statistical significance and stability the proposed models showed their «satisfactory» quality and the possibility of using in the practice of engineering and hydrological forecasts.
APA, Harvard, Vancouver, ISO, and other styles
3

Šaur, David, and Lukáš Pavlík. "Comparison of accuracy of forecasting methods of convective precipitation." MATEC Web of Conferences 210 (2018): 04035. http://dx.doi.org/10.1051/matecconf/201821004035.

Full text
Abstract:
This article is focused on the comparison of the accuracy of quantitative, numerical, statistical and nowcasting forecasting methods of convective precipitation including three flood events that occurred in the Zlin region in the years 2015 - 2017. Quantitative prediction is applied to the Algorithm of Storm Prediction for outputs “The probability of convective precipitation and The statistical forecast of convective precipitation”. The quantitative prediction of the probability of convective precipitation is primarily compared with the precipitation forecasts calculated by publicly available NWP models; secondary to statistical and nowcasting predictions. The statistical prediction is computed on the historical selection criteria and is intended as a complementary prediction to the first algorithm output. The nowcasting prediction operates with radar precipitation measurements, specifically with X-band meteorological radar outputs of the Zlín Region. Compared forecasting methods are used for the purposes of verification and configuration prediction parameters for accuracy increase of algorithm outputs.
APA, Harvard, Vancouver, ISO, and other styles
4

Atashi, Vida, Hamed Taheri Gorji, Seyed Mojtaba Shahabi, Ramtin Kardan, and Yeo Howe Lim. "Water Level Forecasting Using Deep Learning Time-Series Analysis: A Case Study of Red River of the North." Water 14, no. 12 (June 20, 2022): 1971. http://dx.doi.org/10.3390/w14121971.

Full text
Abstract:
The Red River of the North is vulnerable to floods, which have caused significant damage and economic loss to inhabitants. A better capability in flood-event prediction is essential to decision-makers for planning flood-loss-reduction strategies. Over the last decades, classical statistical methods and Machine Learning (ML) algorithms have greatly contributed to the growth of data-driven forecasting systems that provide cost-effective solutions and improved performance in simulating the complex physical processes of floods using mathematical expressions. To make improvements to flood prediction for the Red River of the North, this paper presents effective approaches that make use of a classical statistical method, a classical ML algorithm, and a state-of-the-art Deep Learning method. Respectively, the methods are seasonal autoregressive integrated moving average (SARIMA), Random Forest (RF), and Long Short-Term Memory (LSTM). We used hourly level records from three U.S. Geological Survey (USGS), at Pembina, Drayton, and Grand Forks stations with twelve years of data (2007–2019), to evaluate the water level at six hours, twelve hours, one day, three days, and one week in advance. Pembina, at the downstream location, has a water level gauge but not a flow-gauging station, unlike the others. The floodwater-level-prediction results show that the LSTM method outperforms the SARIMA and RF methods. For the one-week-ahead prediction, the RMSE values for Pembina, Drayton, and Grand Forks are 0.190, 0.151, and 0.107, respectively. These results demonstrate the high precision of the Deep Learning algorithm as a reliable choice for flood-water-level prediction.
APA, Harvard, Vancouver, ISO, and other styles
5

Wu, Jian, Haixing Liu, Guozhen Wei, Tianyu Song, Chi Zhang, and Huicheng Zhou. "Flash Flood Forecasting Using Support Vector Regression Model in a Small Mountainous Catchment." Water 11, no. 7 (June 27, 2019): 1327. http://dx.doi.org/10.3390/w11071327.

Full text
Abstract:
Flash floods in mountainous catchments are often caused by the rainstorm, which may result in more severe consequences than plain area floods due to less timescale and a fast-flowing front of water and debris. Flash flood forecasting is a huge challenge for hydrologists and managers due to its instantaneity, nonlinearity, and dependency. Among different methods of flood forecasting, data-driven models have become increasingly popular in recent years due to their strong ability to simulate nonlinear hydrological processes. This study proposed a Support Vector Regression (SVR) model, which is a powerful artificial intelligence-based model originated from statistical learning theory, to forecast flash floods at different lead times in a small mountainous catchment. The lagged average rainfall and runoff are identified as model input variables, and the time lags associated with the model input variables are determined by the hydrological concept of the time of response. There are 69 flash flood events collected from 1984 to 2012 in a mountainous catchment in China and then used for the model training and testing. The contribution of the runoff variables to the predictions and the phase lag of model outputs are analyzed. The results show that: (i) the SVR model has satisfactory predictive performances for one to three-hours ahead forecasting; (ii) the lagged runoff variables have a more significant effect on the predictions than the rainfall variables; and (iii) the phase lag (time difference) of prediction results significantly exists in both two- and three-hours-ahead forecasting models, however, the input rainfall information can assist in mitigating the phase lag of peak flow.
APA, Harvard, Vancouver, ISO, and other styles
6

Le, Tuan Hoang, and Dung Anh To. "A Modified Semi-parametric Regression Model For Flood Forecasting." Science and Technology Development Journal 18, no. 2 (June 30, 2015): 95–105. http://dx.doi.org/10.32508/stdj.v18i2.1078.

Full text
Abstract:
In recent years, inundation, one of natural calamities, occurs frequently and fiercely. We are sustained severe losses in the floods every year. Therefore, the development of control methods to determine, analyze, model and predict the floods is indispensable and urgent. In this paper, we propose a justified semiparametric regression model for flood water levels forecasting. The new model has three components. The first one is parametric elements of the model. They are water level, precipitation, evaporation, air-humidity and groundmoisture values, etc. There is a complex connection among these parametrics. Several innovated regression models have been offered and experimented for this complicated relationship. The second one is a non-parametric ingredient of our model. We use the Arnak S. Dalalyan et al.’s effective dimension-reduction subspace algorithm and some modified algorithms in neural networks to deal with it. They are altered back-propagation method and ameliorated cascade correlation algorithm. Besides, we also propose a new idea to modify the conjugate gradient one. These actions will help us to smooth the model’s non-parametric constituent easily and quickly. The last component is the model’s error. The whole elements are essential inputs to operational flood management. This work is usually very complex owing to the uncertain and unpredictable nature of underlying phenomena. Flood-waterlevels forecasting, with a lead time of one and more days, was made using a selected sequence of past water-level values observed at a specific location. Time-series analytical method is also utilized to build the model. The results obtained indicate that, with a new semiparametric regression one and the effective dimension-reduction subspace algorithm, together with some improved algorithms in neural network, the estimation power of the modern statistical model is reliable and auspicious, especially for flood forecasting/modeling.
APA, Harvard, Vancouver, ISO, and other styles
7

Chen, Xinchi, Liping Zhang, Christopher James Gippel, Lijie Shan, Shaodan Chen, and Wei Yang. "Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation." Advances in Meteorology 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2710457.

Full text
Abstract:
Precipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based measurement in the quantitative estimation of temporal and spatial aspects of precipitation, but errors inherent in this method will still act to reduce the performance. Using data from White Lotus River of Hubei Province, China, five methods were used to assimilate radar rainfall data transformed from the classifiedZ-Rrelationship, and the postassimilation data were compared with precipitation measured by rain gauges. The five sets of assimilated rainfall data were then used as input to the Xinanjiang model. The effect of precipitation data input error on runoff simulation was analyzed quantitatively by disturbing the input data using the Breeding of Growing Modes method. The results of practical application demonstrated that the statistical weight integration and variational assimilation methods were superior. The corresponding performance in flood hydrograph prediction was also better using the statistical weight integration and variational methods compared to the others. It was found that the errors of radar rainfall data disturbed by the Breeding of Growing Modes had a tendency to accumulate through the hydrological model.
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
9

Palash, Wahid, Yudan Jiang, Ali S. Akanda, David L. Small, Amin Nozari, and Shafiqul Islam. "A Streamflow and Water Level Forecasting Model for the Ganges, Brahmaputra, and Meghna Rivers with Requisite Simplicity." Journal of Hydrometeorology 19, no. 1 (January 2018): 201–25. http://dx.doi.org/10.1175/jhm-d-16-0202.1.

Full text
Abstract:
A forecasting lead time of 5–10 days is desired to increase the flood response and preparedness for large river basins. Large uncertainty in observed and forecasted rainfall appears to be a key bottleneck in providing reliable flood forecasting. Significant efforts continue to be devoted to developing mechanistic hydrological models and statistical and satellite-driven methods to increase the forecasting lead time without exploring the functional utility of these complicated methods. This paper examines the utility of a data-based modeling framework with requisite simplicity that identifies key variables and processes and develops ways to track their evolution and performance. Findings suggest that models with requisite simplicity—relying on flow persistence, aggregated upstream rainfall, and travel time—can provide reliable flood forecasts comparable to relatively more complicated methods for up to 10 days lead time for the Ganges, Brahmaputra, and upper Meghna (GBM) gauging locations inside Bangladesh. Forecasting accuracy improves further by including weather-model-generated forecasted rainfall into the forecasting scheme. The use of water level in the model provides equally good forecasting accuracy for these rivers. The findings of the study also suggest that large-scale rainfall patterns captured by the satellites or weather models and their “predictive ability” of future rainfall are useful in a data-driven model to obtain skillful flood forecasts up to 10 days for the GBM basins. Ease of operationalization and reliable forecasting accuracy of the proposed framework is of particular importance for large rivers, where access to upstream gauge-measured rainfall and flow data are limited, and detailed modeling approaches are operationally prohibitive and functionally ineffective.
APA, Harvard, Vancouver, ISO, and other styles
10

Le, Tuan Hoang, and Dung Anh To. "Short-term flood forecasting with an amended semi-parametric regression ensemble model." Science and Technology Development Journal 20, K2 (June 30, 2017): 117–25. http://dx.doi.org/10.32508/stdj.v20ik2.457.

Full text
Abstract:
Flood forecasting is very important research topic in disaster prevention and reduction. The characteristics of flood involve a rather complex systematic dynamic under the influence of different meteorological factors including linear and non-linear patterns. Recently there are many novel forecasting methods of improving the forecasting accuracy. This paper explores the potential and effect of the semiparametric regression to modelize flood water-level and to forecast the inundation of Mekong Delta in Vietnam. The semi-parametric regression technique is a combination of a parametric regression approach and a non-parametric regression concept. In the process of model building, three altered linear regression models are applied for the parametric component. They are stepwise multiple linear regression, partial least squares solution and multirecursive regression method. They are used to capture flood’s linear characteristics. The nonparametric part is solved by a modified estimation of a smooth function. Furthermore, some justified nonlinear regression models based on artificial neural network are also able to obtain flood’s non-linear characteristics. They help us to smooth the model's non-parametric constituent easily and quickly. The last element is the model's error. Then the semiparametric regression is used for ensemble model based on the principle component analysis technique. Flood water-level forecasting, with a lead time of one and more days, has been made by using a selected sequence of past water-level values and some relevant factors observed at a specific location. Time-series analytical method is utilized to build the model. Obtained empirical results indicate that the prediction by using the amended semi-parametric regression ensemble model is generally better than those obtained by using the other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the estimation power of the modern statistical model is reliable and auspicious. The proposed model here can be used as a promising alternative forecasting tool for flood to achieve better forecasting accuracy and to optimize prediction quality further.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Flood forecasting Statistical methods"

1

何添賢 and Tim Yin Timothy Ho. "Forecasting with smoothing techniques for inventory control." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B42574286.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Nanzad, Bolorchimeg. "EVALUATION OF STATISTICAL METHODS FOR MODELING HISTORICAL RESOURCE PRODUCTION AND FORECASTING." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2192.

Full text
Abstract:
This master’s thesis project consists of two parts. Part I of the project compares modeling of historical resource production and forecasting of future production trends using the logit/probit transform advocated by Rutledge (2011) with conventional Hubbert curve fitting, using global coal production as a case study. The conventional Hubbert/Gaussian method fits a curve to historical production data whereas a logit/probit transform uses a linear fit to a subset of transformed production data. Within the errors and limitations inherent in this type of statistical modeling, these methods provide comparable results. That is, despite that apparent goodness-of-fit achievable using the Logit/Probit methodology, neither approach provides a significant advantage over the other in either explaining the observed data or in making future projections. For mature production regions, those that have already substantially passed peak production, results obtained by either method are closely comparable and reasonable, and estimates of ultimately recoverable resources obtained by either method are consistent with geologically estimated reserves. In contrast, for immature regions, estimates of ultimately recoverable resources generated by either of these alternative methods are unstable and thus, need to be used with caution. Although the logit/probit transform generates high quality-of-fit correspondence with historical production data, this approach provides no new information compared to conventional Gaussian or Hubbert-type models and may have the effect of masking the noise and/or instability in the data and the derived fits. In particular, production forecasts for immature or marginally mature production systems based on either method need to be regarded with considerable caution. Part II of the project investigates the utility of a novel alternative method for multicyclic Hubbert modeling tentatively termed “cycle-jumping” wherein overlap of multiple cycles is limited. The model is designed in a way that each cycle is described by the same three parameters as conventional multicyclic Hubbert model and every two cycles are connected with a transition width. Transition width indicates the shift from one cycle to the next and is described as weighted coaddition of neighboring two cycles. It is determined by three parameters: transition year, transition width, and γ parameter for weighting. The cycle-jumping method provides superior model compared to the conventional multicyclic Hubbert model and reflects historical production behavior more reasonably and practically, by better modeling of the effects of technological transitions and socioeconomic factors that affect historical resource production behavior by explicitly considering the form of the transitions between production cycles.
APA, Harvard, Vancouver, ISO, and other styles
3

Leung, Caleb Chee Shan. "Time series modelling of birth data." Thesis, Canberra, ACT : The Australian National University, 1995. http://hdl.handle.net/1885/118134.

Full text
Abstract:
Three basic methods namely cohort component projection methods, statistical time series methods and structural modelling methods are discussed for the purpose of forecasting births, with the main focus on univariate time series methods. A general autoregressive integrated moving average model for birth time series is developed from the mathematical demographic renewal equation for births. The four-stage Box-Jenkins modelling method of model identification, estimation, diagnosis and forecasting is investigated in detail. This method is employed to model and forecast Australian birth time series. Finally, the comparison between time series forecasts and cohort component projections of births for Australia is made.
APA, Harvard, Vancouver, ISO, and other styles
4

Silva, Jesús, Naveda Alexa Senior, Guliany Jesús García, Núẽz William Niebles, and Palma Hugo Hernández. "Forecasting Electric Load Demand through Advanced Statistical Techniques." Institute of Physics Publishing, 2020. http://hdl.handle.net/10757/652142.

Full text
Abstract:
Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods.
APA, Harvard, Vancouver, ISO, and other styles
5

Kwok, Sai-man Simon, and 郭世民. "Statistical inference of some financial time series models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36885654.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Davydenko, Andrey. "Integration of judgmental and statistical approaches for demand forecasting : models and methods." Thesis, Lancaster University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.655734.

Full text
Abstract:
The need for the composite use of judgmental and statistical approaches in forecasting is caused by the fact that each of these approaches itself cannot ensure the desired quality of forecasts. The topic of integrating forecasting methods has been long addressed in literature. However, due to the specific nature of demand data existing solutions in this area often cannot be efficiently applied in demand forecasting. The aim of the research is to develop efficient models and methods which would better correspond to realistic problem definitions in the context of demand forecasting. The first question that requires resolution is measuring the quality of demand forecasts. A critical analysis of existing error measures has shown that they are not always suitable for demand data due to their statistical properties. To provide a more robust and interpretable indication of forecasting performance the use of an enhanced statistic is proposed. One area of the research relates to the correction of judgmental forecasts. Since judgmental forecasts are inherently affected by cognitive biases, special means are required for producing an adequate probabilistic representation of future demand. Alongside the analysis of independent judgmental forecasts the research has examined the statistical properties of judgmental adjustments to statistically generated forecasts. Empirical analysis with real-world datasets shows that classical assumptions do not hold true and therefore standard procedures and tests cannot be correctly applied. Therefore more flexible methods have been designed to ensure more efficient and reliable analysis of judgmental forecasts. The results from the proposed techniques make it possible i) to reveal and eliminate systematic errors, and ii) to adequately evaluate the uncertainty associated with judgmental forecasts. Another area of research has focused on using prior judgmental information as an input " into statistical modelling, thereby obtaining consistent forecasts using both expert knowledge and latest observations. The proposed approach here is based on constructing a model with a combined dataset where available actual values and expert forecasts are described by means of corresponding regression equations. This allows the use of judgmental information in order to derive the prior characteristics of a data generation process. Model estimation is done using Bayesian inference and iterative algorithms, which make it possible to use sufficiently flexible model specifications. Analysis based on real data has shown that the approach and the proposed models can be easil?, and efficiently applied in practice. In summary, the contribution of the thesis is as follows. i) A number of previously unstudied effects are identified that can potentially lead to misinterpretation of measurement results obtained with the use of various well-known accuracy measures including MAPE, MdAPE, GMRAE, and MASE. ii) A new general error measure with improved statistical properties is proposed to overcome some imperfections of existing error measures. iii) New models and methods for efficient processing of point independent judgmental forecasts and judgmental adjustments to statistical forecasts are proposed based on Bayesian numerical analysis, iv) A new approach is proposed for the efficient incorporation of judgment into a statistical model of process dynamic.
APA, Harvard, Vancouver, ISO, and other styles
7

Vasiljeva, Polina. "Combining Unsupervised and Supervised Statistical Learning Methods for Currency Exchange Rate Forecasting." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-190984.

Full text
Abstract:
In this thesis we revisit the challenging problem of forecasting currency exchange rate. We combine machine learning methods such as agglomerative hierarchical clustering and random forest to construct a two-step approach for predicting movements in currency exchange prices of the Swedish krona and the US dollar. We use a data set with over 200 predictors comprised of different financial and macro-economic time series and their transformations. We perform forecasting for one week ahead with different parameterizations and find a hit rate of on average 53%, with some of the parameterizations yielding hit rates as high as 60%. However, there is no clear indicator that there exists a combination of the methods and parameters that outperforms all of the tested cases. In addition, our results indicate that the two-step approach is sensitive to changes in the training set. This work has been conducted at the Third Swedish National Pension Fund (AP3) and KTH Royal Institute of Technology.
I denna uppsats analyserar vi det svårlösta problemet med att prognostisera utvecklingen för en valutakurs. Vi kombinerar maskininlärningsmetoder såsom agglomerativ hierarkisk klustring och Random Forest för att konstruera en modell i två steg med syfte att förutsäga utvecklingen av valutakursen mellan den svenska kronan och den amerikanska dollarn. Vi använder över 200 prediktorer bestående av olika finansiella och makroekonomiska tidsserier samt deras transformationer och utför prognoser för en vecka framåt med olika modellparametriseringar. En träffsäkerhet på i genomsnitt 53% erhålls, med några fall där en träffsäkerhet så hög som 60% kunde observeras. Det finns emellertid ingen tydlig indikation på att det existerar en kombination av de analyserade metoderna eller parametriseringarna som är optimal inom samtliga av de testade fallen. Vidare konstaterar vi att metoden är känslig för förändringar i underliggande träningsdata. Detta arbete har utförts på Tredje AP-fonden (AP3) och Kungliga Tekniska Högskolan (KTH).
APA, Harvard, Vancouver, ISO, and other styles
8

Du, Toit Cornel. "Non-parametric volatility measurements and volatility forecasting models." Thesis, Stellenbosch : Stellenbosch University, 2005. http://hdl.handle.net/10019.1/50401.

Full text
Abstract:
Assignment (MComm)--Stellenbosch University, 2005.
ENGLISH ABSTRACT: Volatilty was originally seen to be constant and deterministic, but it was later realised that return series are non-stationary. Owing to this non-stationarity nature of returns, there were no reliable ex-post volatility measurements. Subsequently, researchers focussed on ex-ante volatility models. It was only then realised that before good volatility models can be created, reliable ex-post volatility measuremetns need to be defined. In this study we examine non-parametric ex-post volatility measurements in order to obtain approximations of the variances of non-stationary return series. A detailed mathematical derivation and discussion of the already developed volatility measurements, in particular the realised volatility- and DST measurements, are given In theory, the higher the sample frequency of returns is, the more accurate the measurements are. These volatility measurements referred to above, however, all have short-comings in that the realised volatility fails if the sample frequency becomes to high owing to microstructure effects. On the other hand, the DST measurement cannot handle changing instantaneous volatility. In this study we introduce a new volatility measurement, termed microstructure realised volatility, that overcomes these shortcomings. This measurement, as with realised volatility, is based on quadratic variation theory, but the underlying return model is more realistic.
AFRIKAANSE OPSOMMING: Volatiliteit is oorspronklik as konstant en deterministies beskou, dit was eers later dat besef is dat opbrengste nie-stasionêr is. Betroubare volatiliteits metings was nie beskikbaar nie weens die nie-stasionêre aard van opbrengste. Daarom het navorsers gefokus op vooruitskattingvolatiliteits modelle. Dit was eers op hierdie stadium dat navorsers besef het dat die definieering van betroubare volatiliteit metings 'n voorvereiste is vir die skepping van goeie vooruitskattings modelle. Nie-parametriese volatiliteit metings word in hierdie studie ondersoek om sodoende benaderings van die variansies van die nie-stasionêre opbrengste reeks te beraam. 'n Gedetaileerde wiskundige afleiding en bespreking van bestaande volatiliteits metings, spesifiek gerealiseerde volatiliteit en DST- metings, word gegee. In teorie salopbrengste wat meer dikwels waargeneem word tot beter akkuraatheid lei. Bogenoemde volatilitieits metings het egter tekortkominge aangesien gerealiseerde volatiliteit faal wanneer dit te hoog raak, weens mikrostruktuur effekte. Aan die ander kant kan die DST meting nie veranderlike oombliklike volatilitiet hanteer nie. Ons stel in hierdie studie 'n nuwe volatilitieits meting bekend, naamlik mikro-struktuur gerealiseerde volatiliteit, wat nie hierdie tekortkominge het nie. Net soos met gerealiseerde volatiliteit sal hierdie meting gebaseer wees op kwadratiese variasie teorie, maar die onderliggende opbrengste model is meer realisties.
APA, Harvard, Vancouver, ISO, and other styles
9

莫正華 and Ching-wah Mok. "A comparison of two approaches to time series forecasting." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B31977431.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Pharasi, Sid. "Development of statistical downscaling methods for the daily precipitation process at a local site." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99786.

Full text
Abstract:
Over the past decade, statistical procedures have been employed to downscale the outputs from global climate models (GCM) to assess the potential impacts of climate change and variability on the hydrological regime. These procedures are based on the empirical relationships between large-scale atmospheric predictor variables and local surface parameters such as precipitation and temperature. This research is motivated by the recognized lack of a comprehensive yet physically and statistically significant downscaling methodology for daily precipitation at a local site. The primary objectives are to move beyond the 'black box' approaches currently employed within the downscaling community, and develop improved statistical downscaling models that could outperform both raw GCM output and the current standard: the SDSM method. In addition, the downscaling methods could provide a more robust physical interpretation of the relationships between large-scale predictor climate variables and the daily precipitation characteristics at a local site.
The first component of this thesis consists of developing linear regression based downscaling models to predict both the occurrence and intensity of daily precipitation at a local site using stepwise, weighted least squares, and robust regression methods. The performance of these models was assessed using daily precipitation and NCEP re-analysis climate data available at Dorval Airport in Quebec for the 1961-1990 period. It was found that the proposed models could describe more accurately the statistical and physical properties of the local daily precipitation process as compared to the CGCM1 model. Further, the stepwise model outperforms the SDSM model for seven months of the year and produces markedly fewer outliers than the latter, particularly for the winter and spring months. These results highlight the necessity of downscaling precipitation for a local site because of the unreliability of the large-scale raw CGCM1 output, and demonstrate the comparative performance of the proposed stepwise model as compared with the SDSM model in reproducing both the statistical and physical properties of the observed daily rainfall series at Dorval.
In the second part of the thesis, a new downscaling methodology based on the principal component regression is developed to predict both the occurrence and amounts of the daily precipitation series at a local site. The principal component analysis created statistically and physically meaningful groupings of the NCEP predictor variables which explained 90% of the total variance. All models formulated outperformed the SDSM model in the description of the statistical properties of the precipitation series, as well as reproduced 4 out of 6 physical indices more accurately than the SDSM model, except for the summer season. Most importantly, this analysis yields a single, parismonious model; a non-redundant model, not stratified by month or season, with a single set of parameters that can predict both precipitation occurrence and intensity for any season of the year.
The third component of the research uses covariance structural modeling to ascertain the best predictors within the principal components that were developed previously. Best fit models with significant paths are generated for the winter and summer seasons via an iterative process. The direct and indirect effects of the variables left in the final models indicate that for either season, three main predictors exhibit direct effects on the daily precipitation amounts: the meridional velocity at the 850 HPa level, the vorticity at the 500 HPa level, and the specific humidity at the 500 HPa level. Each of these variables is heavily loaded onto the first three principal components respectively. Further, a key fact emerges: From season to season, the same seven significant large-scale NCEP predictors exhibit a similar model structure when the daily precipitation amounts at Dorval Airport were used as a dependent variable. This fact indicated that the covariance structural model was physically more consistent than the stepwise regression one since different model structures with different sets of significant variables could be identified when a stepwise procedure is employed.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Flood forecasting Statistical methods"

1

Cunnane, C. Statistical distributions for flood frequency analysis. Geneva, Switzerland: Secretariat of the World Meteorological Organization, 1989.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Kumar, Rakesh. Regional flood frequency analysis for sub-Himalayan region. Roorkee: National Institute of Hydrology, 1994.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Parrett, Charles. Methods for estimating flood frequency in Montana based on data through water year 1998. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2004.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Holnbeck, Stephen R. Procedures for estimating unit hydrographs for large floods at ungaged sites in Montana. [Washington, D.C.]: U.S. G.P.O., 1996.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Parrett, Charles. Methods for estimating flood frequency in Montana based on data through water year 1998. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2004.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Parrett, Charles. Methods for estimating flood frequency in Montana based on data through water year 1998. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2004.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Parrett, Charles. Methods for estimating flood frequency in Montana based on data through water year 1998. Helena, Mont: U.S. Dept. of the Interior, U.S. Geological Survey, 2004.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Lang, Michel. Estimation de la crue centennale pour les plans de prévention des risques d'inondations. Versailles: Éditions Quæ, 2007.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Post, Claudia. Über die Anwendbarkeit geostatischer Verfahren und Optimierung von Daten zur Bewertung der hydraulischen und geologischen Gegebenheiten als Grundlage für Sanierungsmassnahmen am Beispiel des Ronneburger Erzreviers. Aachen: Lehrstuhl für Ingenieurgeologie und Hydrogeologie der RWTH, 2001.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Straub, T. D. Equations for estimating Clark unit-hydrograph parameters for small rural watersheds in Illinois. Urbana, Ill: U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Flood forecasting Statistical methods"

1

Hyndman, Rob J. "Business Forecasting Methods." In International Encyclopedia of Statistical Science, 185–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_156.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Şen, Zekâi. "Probability and Statistical Methods." In Flood Modeling, Prediction and Mitigation, 245–301. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52356-9_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Berlyand, M. E. "Statistical methods of air pollution forecasting." In Prediction and Regulation of Air Pollution, 159–201. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3768-3_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Ciepielowski, Andrzej. "Statistical Methods of Determining Typical Winter and Summer Hydrographs for Ungauged Watersheds." In Flood Hydrology, 117–24. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3957-8_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Wright, George, and Peter Ayton. "Psychological Aspects of Forecasting with Statistical Methods." In Operational Research and the Social Sciences, 617–23. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0789-1_93.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

McClung, D. M. "Computer Assisted Avalanche Forecasting." In Stochastic and Statistical Methods in Hydrology and Environmental Engineering, 347–58. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-017-3081-5_26.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Tao, Tao, and William C. Lennox. "Evaluation of Streamflow Forecasting Models." In Stochastic and Statistical Methods in Hydrology and Environmental Engineering, 77–85. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-017-3083-9_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Bianchi, Sergio, Fabrizio Di Sciorio, and Raffaele Mattera. "Forecasting VIX with Hurst Exponent." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 90–95. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99638-3_15.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Serrano-Guerrero, Xavier, Luis-Fernando Siavichay, Jean-Michel Clairand, and Guillermo Escrivá-Escrivá. "Forecasting Building Electric Consumption Patterns Through Statistical Methods." In Advances in Intelligent Systems and Computing, 164–75. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32033-1_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Březková, Lucie, Milan Šálek, Petr Novák, Hana Kyznarová, and Martin Jonov. "New Methods of Flash Flood Forecasting in the Czech Republic." In IFIP Advances in Information and Communication Technology, 550–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22285-6_59.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Flood forecasting Statistical methods"

1

Prohaska, Stevan, Aleksandra Ilić, and Pavla Pekarova. "ASSESSMENT OF STATISTICAL SIGNIFICANCE OF HISTORIC DANUBE FLOODS." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.05.

Full text
Abstract:
Data on historic floods along the Danube River exist since the year 1012. In the Middle Ages, floods were estimated based on historical documents, including original handwritten notes, newspaper articles, chronicles, formal letters, books, maps and photographs. From 1500 until the beginning of organized water regime observations, floods were hydraulically reconstructed based on water marks on old buildings in cities along the Danube (Passau, Melk, Emmersdorf an der Donau, Spilz, Schonbuhen and Bratislava). The paper presents a procedure for assessing the statistical significance of registered historic floods using a comprehensive method for defining theoretical flood hydrographs at hydrological stations. The approach is based on correlation analysis of two basic flood hydrograph parameters – maximum hydrograph ordinate (peak) and flood wave volume. The PROIL model is used to define the probability of simultaneous occurrence of these parameters. It defines the exceedance probability of two random variables, in the specific case two hydrograph parameters of the form: P{Qmax more equal to qmax,p)∩(Wmax more equal to wmax,p)} = P (1) where: Qmax – maximum hydrograph ordinate (peak); qmax,p – maximum discharge of the probability of occurrence p; Wmax – maximum hydrograph volume; wmax,p – maximum flood wave volume of the probability of occurrence p; P – exceedance probability. Spatial positions of the lines of exceedance of two flood hydrograph parameters and the empirical points of the corresponding parameters of the considered historic flood in the correlation field Qmax - Wmax, allow direct assessment of the exceedance probability of a historic flood, or its statistical significance. The proposed procedure was applied in practice to assess the statistical significance of the biggest floods registered along the Danube in the sector from its mouth to the Djerdap 1 Dam. The linear trend in the time-series of maximum annual flows at a representative hydrological station and the frequency of historic floods in the considered sector of the Danube are discussed at the end of the paper.
APA, Harvard, Vancouver, ISO, and other styles
2

Pekárová, Pavla, Pavol Miklánek, Veronika Bačová Mitková, Marcel Garaj, and Ján Pekár. "ASSESSMENT HARMONIZATION PROBLEMS OF THE LONG RETURN PERIOD FLOODS ON THE DANUBE RIVER." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.16.

Full text
Abstract:
One of the basic problems of the flood hydrology was (and still is) the solution of the relationship between peak discharges of the flood waves and probability of their return period. The assessment of the design values along the Danube channel is more complicated due to application of different estimation methods of design values in particular countries downstream the Danube. Therefore, it is necessary to commence the harmonization of the flood design values assessment methods. All methods of estimating floods with a very long return period are associated with great uncertainties. Determining of the specific value of the 500- or 1000-year floods for engineering practice is extremely complex. Nowadays hydrologists are required to determine not only the specific design value of the flood, but it is also necessary to specify confidence intervals in which the flow of a given 100-, 500-, or 1000-year flood may occur with probability, for example, 90 %. The assessment of the design values Qmax can be done by several methods. In this study we have applied the statistical methods based on the assessment of the distribution function of measured time series of the maximum annual discharge. In order to apply regionalization methods for the estimation of the distribution function in this study we used only one distribution - the Pearson Type III distribution with logarithmic transformation of the data (log Pearson Type III distribution - LP3 distribution). To estimate regional skew coefficient for the Danube River we use 20 Qmax measured time series from water gauges along the Danube River from Germany to Ukraine. We firstly analyzed the occurrence of historic floods in several stations along the Danube River. Then we search relationship between the parameter of skewness of the log Pearson type III distribution function and runoff depth, altitude, or basin area in all 20 water gauge. Skewness coefficients of the LP3 distribution in the stations along the Danube River vary between –0.4 and 0.86.
APA, Harvard, Vancouver, ISO, and other styles
3

Janál, Petr, and Tomáš Kozel. "FUZZY LOGIC BASED FLASH FLOOD FORECAST." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.10.

Full text
Abstract:
The flash flood forecasting remains one of the most difficult tasks in the operative hydrology worldwide. The torrential rainfalls bring high uncertainty included in both forecasted and measured part of the input rainfall data. The hydrological models must be capable to deal with such amount of uncertainty. The artificial intelligence methods work on the principles of adaptability and could represent a proper solution. The application of different methods, approaches, hydrological models and usage of various input data is necessary. The tool for real-time evaluation of the flash flood occurrence was assembled on the bases of the fuzzy logic. The model covers whole area of the Czech Republic and the nearest surroundings. The domain is divided into 3245 small catchments of the average size of 30 km2. Real flood episodes were used for the calibration and future flood events can be used for recalibration (principle of adaptability). The model consists of two fuzzy inference systems (FIS). The catchment predisposition for the flash flood occurrence is evaluated by the first FIS. The geomorphological characteristics and long-term meteorological statistics serve as the inputs. The second FIS evaluates real-time data. The inputs are: The predisposition for flash flood occurrence (gained from the first FIS), the rainfall intensity, the rainfall duration and the antecedent precipitation index. The meteorological radar measurement and the precipitation nowcasting serve as the precipitation data source. Various precipitation nowcasting methods are considered. The risk of the flash flood occurrence is evaluated for each small catchment every 5 or 10 minutes (the time step depends on the precipitation nowcasting method). The Fuzzy Flash Flood model is implemented in the Czech Hydrometeorological Institute (CHMI) – Brno Regional Office. The results are available for all forecasters at CHMI via web application for testing. The huge uncertainty inherent in the flash flood forecasting causes that fuzzy model outputs based on different nowcasting methods could vary significantly. The storms development is very dynamic and hydrological forecast could change a lot of every 5 minutes. That is why the fuzzy model estimates are intended to be used by experts only. The Fuzzy Flash Flood model is an alternative tool for the flash flood forecasting. It can provide the first hints of danger of flash flood occurrence within the whole territory of the Czech Republic. Its main advantage is very fast calculation and possibility of variant approach using various precipitation nowcasting inputs. However, the system produces large number of false alarms, therefore the long-term testing in operation is necessary and the warning releasing rules must be set.
APA, Harvard, Vancouver, ISO, and other styles
4

Al-Fawa'Reh, Mohammad, Alaa Hawamdeh, Rana Alrawashdeh, and Mousa Tayseer Jafar. "Intelligent Methods for flood forecasting in Wadi al Wala, Jordan." In 2021 International Congress of Advanced Technology and Engineering (ICOTEN). IEEE, 2021. http://dx.doi.org/10.1109/icoten52080.2021.9493425.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Alsultanny, Yas. "Successful Forecasting for Knowledge Discovery by Statistical Methods." In 2012 Ninth International Conference on Information Technology: New Generations (ITNG). IEEE, 2012. http://dx.doi.org/10.1109/itng.2012.160.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Lei Liu, Yujian An, Zeyang Fan, and Wei Sun. "Quality evaluation based on multivariate statistical forecasting methods." In 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE). IEEE, 2016. http://dx.doi.org/10.1109/isie.2016.7745033.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Russell, B. "Are Common Group Forecasting and Statistical Methods Valid?" In Canadian International Petroleum Conference. Petroleum Society of Canada, 2005. http://dx.doi.org/10.2118/2005-229.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Dominguez-Navarro, Jose Antonio, Iain Dinwoodie, and David McMillan. "Statistical forecasting for offshore wind helicopter operations." In 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2014. http://dx.doi.org/10.1109/pmaps.2014.6960636.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Hristozov, Daniel, Nedyalko Katrandzhiev, Borislav Milenkov, and Eva Dimitrova. "Statistical methods for forecasting the expense of electrical energy." In 2019 Second Balkan Junior Conference on Lighting (Balkan Light Junior). IEEE, 2019. http://dx.doi.org/10.1109/blj.2019.8883541.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Notaro, Vincenza, and Gabriele Freni. "Statistical analysis of the uncertainty related to flood hazard appraisal." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2015 (ICCMSE 2015). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4938959.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Flood forecasting Statistical methods"

1

Mendes, J., R. J. Bessa, H. Keko, J. Sumaili, V. Miranda, C. Ferreira, J. Gama, A. Botterud, Z. Zhou, and J. Wang. Development and testing of improved statistical wind power forecasting methods. Office of Scientific and Technical Information (OSTI), December 2011. http://dx.doi.org/10.2172/1031455.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Perera, Duminda, Ousmane Seidou, Jetal Agnihotri, Mohamed Rasmy, Vladimir Smakhtin, Paulin Coulibaly, and Hamid Mehmood. Flood Early Warning Systems: A Review Of Benefits, Challenges And Prospects. United Nations University Institute for Water, Environment and Health, August 2019. http://dx.doi.org/10.53328/mjfq3791.

Full text
Abstract:
Floods are major water-related disasters that affect millions of people resulting in thousands of mortalities and billiondollar losses globally every year. Flood Early Warning Systems (FEWS) - one of the floods risk management measures - are currently operational in many countries. The UN Office for Disaster Risk Reduction recognises their importance and strongly advocates for an increase in their availability under the targets of the Sendai Framework for Disaster Risk Reduction, and Sustainable Development Goals (SDGs). However, despite widespread recognition of the importance of FEWS for disaster risk reduction (DRR), there’s a lack of information on their availability and status around the world, their benefits and costs, challenges and trends associated with their development. This report contributes to bridging these gaps by analyzing the responses to a comprehensive online survey with over 80 questions on various components of FEWS (risk knowledge, monitoring and forecasting, warning dissemination and communication, and response capabilities), investments into FEWS, their operational effectiveness, benefits, and challenges. FEWS were classified as technologically “basic”, “intermediate” and “advanced” depending on the existence and sophistication of FEWS` components such as hydrological data = collection systems, data transfer systems, flood forecasting methods, and early warning communication methods. The survey questionnaire was distributed to flood forecasting and warning centers around the globe; the primary focus was developing and least-developed countries (LDCs). The questionnaire is available here: https://inweh.unu.edu/questionnaireevaluation-of-flood-early-warning-systems/ and can be useful in its own right for similar studies at national or regional scales, in its current form or with case-specific modifications. Survey responses were received from 47 developing (including LDCs) and six developed countries. Additional information for some countries was extracted from available literature. Analysis of these data suggests the existence of an equal number of “intermediate” and “advanced” FEWS in surveyed river basins. While developing countries overall appear to progress well in FEWS implementation, LDCs are still lagging behind since most of them have “basic” FEWS. The difference between types of operational systems in developing and developed countries appear to be insignificant; presence of basic, intermediate or advanced FEWS depends on available investments for system developments and continuous financing for their operations, and there is evidence of more financial support — on the order of USD 100 million — to FEWS in developing countries thanks to international aid. However, training the staff and maintaining the FEWS for long-term operations are challenging. About 75% of responses indicate that river basins have inadequate hydrological network coverage and back-up equipment. Almost half of the responders indicated that their models are not advanced and accurate enough to produce reliable forecasts. Lack of technical expertise and limited skilled manpower to perform forecasts was cited by 50% of respondents. The primary reason for establishing FEWS, based on the survey, is to avoid property damage; minimizing causalities and agricultural losses appear to be secondary reasons. The range of the community benefited by FEWS varies, but 55% of FEWS operate in the range between 100,000 to 1 million of population. The number of flood disasters and their causalities has declined since the year 2000, while 50% of currently operating FEWS were established over the same period. This decline may be attributed to the combined DRR efforts, of which FEWS are an integral part. In lower-middle-income and low-income countries, economic losses due to flood disasters may be smaller in absolute terms, but they represent a higher percentage of such countries’ GDP. In high-income countries, higher flood-related losses accounted for a small percentage of their GDP. To improve global knowledge on FEWS status and implementation in the context of Sendai Framework and SDGs, the report’s recommendations include: i) coordinate global investments in FEWS development and standardise investment reporting; ii) establish an international hub to monitor the status of FEWS in collaboration with the national responsible agencies. This will support the sharing of FEWS-related information for accelerated global progress in DRR; iii) develop a comprehensive, index-based ranking system for FEWS according to their effectiveness in flood disaster mitigation. This will provide clear standards and a roadmap for improving FEWS’ effectiveness, and iv) improve coordination between institutions responsible for flood forecasting and those responsible for communicating warnings and community preparedness and awareness.
APA, Harvard, Vancouver, ISO, and other styles
3

Weissinger, Rebecca. Evaluation of hanging-garden endemic-plant monitoring at Southeast Utah Group national parks, 2013–2020. Edited by Alice Wondrak Biel. National Park Service, October 2022. http://dx.doi.org/10.36967/2294868.

Full text
Abstract:
Hanging gardens are the most common type of spring at Arches National Park (NP) and Natural Bridges National Monument (NM). They are also present at Canyonlands National Park, but hanging gardens are rare off the Colorado Plateau. Their cliffside setting provides stable access to water without flood disturbance. This combination provides unique habitat that is rich in endemic plant species. The diffuse, seeping emergence of water makes measuring springflow impossible at most sites. Park managers have an interest in monitoring hanging gardens—especially as the climate warms and aridity and water demand both increase. The Northern Colorado Plateau Net-work (NCPN) proposed methods for monitoring seven perennial endemic-plant species at hanging gardens as indicators of spring health and proxies for water availability. Because hanging gardens occur on bedrock outcrops, systematic or random sampling was not possible due to safety concerns and potential resource damage on steep, wet slopes. Examining eight years (2013–2020) of data, this report evaluates the suitability of endemic-plant count data at hanging gardens as a monitoring indicator. It also provides our first evaluation of status and trends at NCPN hanging gardens. The seven species included in monitoring were Rydberg’s thistle (Cirsium rydbergii), Kachina daisy (Erigeron kachinensis), alcove death camas (Zigadenus vaginatus), alcove bog orchid (Habenaria zothecina), cave primrose (Primula specuicola), alcove columbine (Aquilegia micrantha), and Eastwood’s monkeyflower (Mimulus eastwoodiae). Six of the seven species were found at each park. Up to 500 individuals of each species were counted at 42 hanging gardens in Arches NP, 14 hanging gardens in Natural Bridges NM, and 3 hanging gardens in Canyonlands NP. Larger populations were divided into count classes of 501–1,000, 1,001–10,000, and more than 10,000 individuals. Counts from two independent observers and from back-to-back years of sampling were compared for repeatability. Repeatability in count classes was less than 50% for Kachina daisy and Eastwood’s monkeyflower, which both propagate vegetatively via ramets and/or stolons. Repeatability was greater than 90% for only one species, Rydberg’s thistle. The remaining species were categorized in different classes between 15–40% of the time. Independent-observer comparisons were only available for 6.6% of the dataset, but these observations suggested that (1) observer bias was present and (2) the observer with more experience working in hanging gardens generally had higher counts than the observer with less experience in this system. Although repeatability was variable, it was within the range reported by other studies for most species. The NCPN, in discussion with park staff, has elected to make some modifications to the protocol but will continue using endemic plant counts as an indicator of hanging-garden health to maintain a biological variable as a complement to our physical-response data. This is due to their high value to park biodiversity and the difficulty of developing a more robust approach to monitoring in these sites. Endemic-plant monitoring will continue for the five species with the highest repeatability during pilot monitoring and will focus on detecting changes in smaller populations. Most hanging gardens have more than one endemic species present, so several populations can be tracked at each site. Our period of record is relatively brief, and the distribution of endemic-plant populations in different count classes at these sites has not yet shown any statistical trends over time. Be-cause of the large count classes, our methods are more sensitive to showing change in smaller populations (fewer than 500 individuals). Small populations are also of greatest concern to park managers because of their vulnerability to declines or extirpation due to drought. Over-all, more sites had endemic-plant populations of fewer than 100 individuals at the end...
APA, Harvard, Vancouver, ISO, and other styles
4

Annual Mekong Flood Report 2017. Vientiane, Lao PDR: Mekong River Commission Secretariat, September 2019. http://dx.doi.org/10.52107/mrc.ajg54i.

Full text
Abstract:
The report elaborates on methods to assess conditions in the Lower Mekong River Basin, tools for forecasting flood and drought, and explains the regional flood and drought situation in the current year by means of one overarching and four country-related sections.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Flood forecasting Statistical methods' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography