2,477 research outputs found

    Regional flood frequency analysis using an artificial neural network model

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    This paper presents the results from a study on the application of an artificial neural network (ANN) model for regional flood frequency analysis (RFFA). The study was conducted using stream flow data from 88 gauging stations across New South Wales (NSW) in Australia. Five different models consisting of three to eight predictor variables (i.e., annual rainfall, drainage area, fraction forested area, potential evapotranspiration, rainfall intensity, river slope, shape factor and stream density) were tested. The results show that an ANN model with a higher number of predictor variables does not always improve the performance of RFFA models. For example, the model with three predictor variables performs considerably better than the models using a higher number of predictor variables, except for the one which contains all the eight predictor variables. The model with three predictor variables exhibits smaller median relative error values for 2- and 20-year return periods compared to the model containing eight predictor variables. However, for 5-, 10-, 50- and 100-year return periods, the model with eight predictor variables shows smaller median relative error values. The proposed ANN modelling framework can be adapted to other regions in Australia and abroad

    Development of generalized feed forward network for predicting annual flood (depth) of a tropical river

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    The modeling of rainfall-runoff relationship in a watershed is very important in designing hydraulic structures, controlling flood and managing storm water. Artificial Neural Networks (ANNs) are known as having the ability to model nonlinear mechanisms. This study aimed at developing a Generalized Feed Forward (GFF) network model for predicting annual flood (depth) of Johor River in Peninsular Malaysia. In order to avoid over training, cross-validation technique was performed for optimizing the model. In addition, predictive uncertainty index was used to protect of over parameterization. The governing training algorithm was back propagation with momentum term and tangent hyperbolic types was used as transfer function for hidden and output layers. The results showed that the optimum architecture was derived by linear tangent hyperbolic transfer function for both hidden and output layers. The values of Nash and Sutcliffe (NS) and Root mean square error (RMSE) obtained 0.98 and 5.92 for the test period. Cross validation evaluation showed 9 process elements is adequate in hidden layer for optimum generalization by considering the predictive uncertainty index obtained (0.14) for test period which is acceptable

    PREDICTION OF SOIL PORE WATER PRESSURE RESPONSES TO RAINFALL USING RADIAL BASIS KERNEL FUNCTION

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    Pore Water Pressure (PWP) prediction is important in analyzing the strength and effective stress of the soil. Increase of PWP will cause slope failure in areas susceptible to landslide. Stability is determined by the equalization of shear strength and shear stress analyses. Knowledge in pore water pressure is important in hydrological analysis, such as seepage slope strength analyses, engineered slope design and assessing slope reactions to rainfall. The main aim of this work is to forecast pore water pressure variations in response to rainfall utilizing Radial Basis Kernel Function and to evaluate model performance using statistical measures

    Rainfall-runoff and other modelling for ungauged/low-benefit locations: Operational Guidelines

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    Prediction of groundwater levels from lake levels and climate data using ann approach

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    There are many environmental concerns relating to the quality and quantity of surface and groundwater. It is very important to estimate the quantity of water by using readily available climate data for managing water resources of the natural environment. As a case study an artificial neural network (ANN) methodology is developed for estimating the groundwater levels (upper Floridan aquifer levels) as a function of monthly averaged precipitation, evaporation, and measured levels of Magnolia and Brooklyn Lakes in north-central Florida. Groundwater and surface water are highly interactive in the region due to the characteristics of the geological structure, which consists of a sandy surficial aquifer, and a highly transmissive limestoneconfined aquifer known as the Floridan aquifer system (FAS), which are separated by a leaky clayey confining unit. In a lake groundwater system that is typical of many karst lakes in Florida, a large part of the groundwater outflow occurs by means of vertical leakage through the underlying confining unit to a deeper highly transmissive upper Floridan aquifer. This providesa direct hydraulic connection between the lakes and the aquifer, which creates fast and dynamic surface water/groundwater interaction. Relationships among lake levels, groundwater levels, rainfall, and evapotranspiration were determined using ANN-based models and multiple-linear regression (MLR) and multiple-nonlinear regression (MNLR) models. All the models were fitted to the monthly data series and their performances were compared. ANN-based models performed better than MLR and MNLR models in predicting groundwater levels.Keywords: groundwater, surface water, interaction, artificial neural networ

    Data-driven models for monthly streamflow time series prediction

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    Author name used in this publication: K.W. Chau2010-2011 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

    Flood estimation at ungauged sites using artificial neural networks

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    Artificial neural networks (ANNs) have been applied within the field of hydrological modelling for over a decade but relatively little attention has been paid to the use of these tools for flood estimation in ungauged catchments. This paper uses data from the Centre for Ecology and Hydrology's Flood Estimation Handbook (FEH) to predict T-year flood events and the index flood (the median of the annual maximum series) for 850 catchments across the UK. When compared with multiple regression models, ANNs provide improved flood estimates that can be used by engineers and hydrologists. Comparisons are also made with the empirical model presented in the FEH and a preliminary study is made of the spatial distribution of ANN residuals, highlighting the influence that geographical factors have on model performance

    Exploding the myths: An introduction to artificial neural networks for prediction and forecasting

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    Artificial Neural Networks (ANNs), sometimes also called models for deep learning, are used extensively for the prediction of a range of environmental variables. While the potential of ANNs is unquestioned, they are surrounded by an air of mystery and intrigue, leading to a lack of understanding of their inner workings. This has led to the perpetuation of a number of myths, resulting in the misconception that applying ANNs primarily involves "throwing" a large amount of data at "black-box" software packages. While this is a convenient way to side-step the principles applied to the development of other types of models, this comes at significant cost in terms of the usefulness of the resulting models. To address these issues, this inroductory overview paper explodes a number of the common myths surrounding the use of ANNs and outlines state-of-the-art approaches to developing ANNs that enable them to be applied with confidence in practice
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