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Kwokwing Chau - One of the best experts on this subject based on the ideXlab platform.
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Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling
Water, 2017Co-Authors: Kwokwing ChauAbstract:Each year, extreme floods, which appear to be occurring more frequently in recent years (owing to climate change), lead to enormous economic damage and human suffering around the world. It is therefore imperative to be able to accurately predict both the occurrence time and magnitude of peak discharge in advance of an impending flood event. The use of meta-heuristic techniques in Rainfall-Runoff Modeling is a growing field of endeavor in water resources management. These techniques can be used to calibrate data-driven Rainfall-Runoff models to improve forecasting accuracies. This Special Issue of the journal Water is designed to fill the analytical void by including papers concerning advances in the contemporary use of meta-heuristic techniques in Rainfall-Runoff Modeling. The information and analyses can contribute to the development and implementation of effective hydrological predictions, and thus, of appropriate precautionary measures.
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data driven input variable selection for rainfall runoff Modeling using binary coded particle swarm optimization and extreme learning machines
Journal of Hydrology, 2015Co-Authors: Riccardo Taormina, Kwokwing ChauAbstract:Summary Selecting an adequate set of inputs is a critical step for successful data-driven streamflow prediction. In this study, we present a novel approach for Input Variable Selection (IVS) that employs Binary-coded discrete Fully Informed Particle Swarm optimization (BFIPS) and Extreme Learning Machines (ELM) to develop fast and accurate IVS algorithms. A scheme is employed to encode the subset of selected inputs and ELM specifications into the binary particles, which are evolved using single objective and multi-objective BFIPS optimization (MBFIPS). The performances of these ELM-based methods are assessed using the evaluation criteria and the datasets included in the comprehensive IVS evaluation framework proposed by Galelli et al. (2014). From a comparison with 4 major IVS techniques used in their original study it emerges that the proposed methods compare very well in terms of selection accuracy. The best performers were found to be (1) a MBFIPS–ELM algorithm based on the concurrent minimization of an error function and the number of selected inputs, and (2) a BFIPS–ELM algorithm based on the minimization of a variant of the Akaike Information Criterion (AIC). The first technique is arguably the most accurate overall, and is able to reach an almost perfect specification of the optimal input subset for a partially synthetic rainfall–runoff experiment devised for the Kentucky River basin. In addition, MBFIPS–ELM allows for the determination of the relative importance of the selected inputs. On the other hand, the BFIPS–ELM is found to consistently reach high accuracy scores while being considerably faster. By extrapolating the results obtained on the IVS test-bed, it can be concluded that the proposed techniques are particularly suited for rainfall–runoff Modeling applications characterized by high nonlinearity in the catchment dynamics.
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rainfall runoff Modeling using artificial neural network coupled with singular spectrum analysis
Journal of Hydrology, 2011Co-Authors: C L Wu, Kwokwing ChauAbstract:Summary Accurately Modeling rainfall–runoff (R–R) transform remains a challenging task despite that a wide range of Modeling techniques, either knowledge-driven or data-driven, have been developed in the past several decades. Amongst data-driven models, artificial neural network (ANN)-based R–R models have received great attentions in hydrology community owing to their capability to reproduce the highly nonlinear nature of the relationship between hydrological variables. However, a lagged prediction effect often appears in the ANN Modeling process. This paper attempts to eliminate the lag effect from two aspects: modular artificial neural network (MANN) and data preprocessing by singular spectrum analysis (SSA). Two watersheds from China are explored with daily collected data. Results show that MANN does not exhibit significant advantages over ANN. However, it is demonstrated that SSA can considerably improve the performance of prediction model and eliminate the lag effect. Moreover, ANN or MANN with antecedent runoff only as model input is also developed and compared with the ANN (or MANN) R–R model. At all three prediction horizons, the latter outperforms the former regardless of being coupled with/without SSA. It is recommended from the present study that the ANN R–R model coupled with SSA is more promisings.
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particle swarm optimization training algorithm for anns in stage prediction of shing mun river
Journal of Hydrology, 2006Co-Authors: Kwokwing ChauAbstract:An accurate water stage prediction allows the pertinent authority to issue a forewarning of the impending flood and to implement early evacuation measures when required. Existing methods including Rainfall-Runoff Modeling or statistical techniques entail exogenous input together with a number of assumptions. The use of artificial neural networks (ANN) has been shown to be a cost-effective technique. But their training, usually with back-propagation algorithm or other gradient algorithms, is featured with certain drawbacks such as very slow convergence and easy entrapment in a local minimum. In this paper, a particle swarm optimization model is adopted to train perceptrons. The approach is applied to predict water levels in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging stations or stage/time history at the specific station. It is shown that the PSO technique can act as an alternative training algorithm for ANNs.
Gert A. Schultz - One of the best experts on this subject based on the ideXlab platform.
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Application of a geographic information system for conceptual rainfall runoff Modeling
Journal of Hydrology, 2000Co-Authors: Andreas Schumann, R. Funke, Gert A. SchultzAbstract:Abstract Geographic information systems (GIS) offer many new opportunities for hydrological Modeling. They can be used to form spatially distributed models of watershed. However, some problems of this approach, e.g. the parameterization of physically based models, are not resolved yet. Conceptual models of the meso-scale still have a great practical importance. In this paper one approach is presented: how statistical descriptions of distributed catchment characteristics could be used to consider spatial heterogeneity within conceptual models. Three semi-distributed modules are presented. The three components are combined to a hydrological model including feedback components between surface flow and infiltration and between subsurface return flow and surface flow in saturated areas. The model was set up to use spatially distributed information about catchment characteristics for the estimation of its parameters. By a direct estimation of some model parameters from a GIS-based analysis of the catchment characteristics, the number of calibration parameters can be reduced. In the second part it is shown how the application of this model to different catchments within a region can benefit from boundary conditions for optimization, which are derived from a GIS considering the differences of catchment characteristics.
Vahid Nourani - One of the best experts on this subject based on the ideXlab platform.
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using self organizing maps and wavelet transforms for space time pre processing of satellite precipitation and runoff data in neural network based rainfall runoff Modeling
Journal of Hydrology, 2013Co-Authors: Vahid Nourani, Aida Hosseini Baghanam, Jan Adamowski, Mekonnen GebremichaelAbstract:Summary In this paper, a two-level self-organizing map (SOM) clustering technique was used to identify spatially homogeneous clusters of precipitation satellite data, and to choose the most operative and effective data for a feed-forward neural network (FFNN) to model rainfall–runoff process on a daily and multi-step ahead time scale. The wavelet transform (WT) was also used to extract dynamic and multi-scale features of the non-stationary runoff time series and to remove noise. The performance of the coupled SOM–FFNN model was compared to the newly proposed combined SOM–WT–FFNN model. The performance of these two models was also compared to that of a conventional forecasting method, namely the auto regressive integrated moving average with exogenous input (ARIMAX) model. Daily precipitation data from two satellites and four rain gauges, as well as runoff values recorded from January 2003 to December 2007 in the Gilgel Abay watershed in Ethiopia were used to calibrate and validate the models. Runoff predictions via all of the above methods were investigated for both single-step-ahead and multi-step-ahead lead times. The results indicated that the use of spatial and temporal pre-processed data in the FFNN model led to a promising improvement in its performance for rainfall–runoff forecasting. In the validation phase of single and multi-step-ahead forecasting, it was determined that the SOM–WT–FFNN models provide more accurate forecasts than the SOM–FFNN models (the determination coefficients for validation of the SOM–FFNN and SOM–WT–FFNN models were 0.80 and 0.93, respectively). The proposed FFNN model coupled with the SOM clustering method decreased the dimensionality of the input variables and consequently the complexity of the FFNN models. On the other hand, the application of the wavelet transform to the runoff data increased the performance of the FFNN rainfall–runoff models in predicting runoff peak values by removing noise and revealing the dominant periods.
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A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling
Water Resources Management, 2009Co-Authors: Vahid Nourani, Mehdi Komasi, Akira ManoAbstract:Without a doubt the first step in any water resources management is the rainfall–runoff Modeling over the watershed. However considering high stochastic property of the process, many models are being still developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently Artificial Neural Network (ANN) as a non-linear inter-extrapolator is extensively used by hydrologists for rainfall–runoff Modeling as well as other fields of hydrology. In the current research, the wavelet analysis was linked to the ANN concept for Modeling Ligvanchai watershed rainfall–runoff process at Tabriz, Iran. For this purpose the main time series of two variables, rainfall and runoff, were decomposed to some multi-frequently time series by wavelet theory, then these time series were imposed as input data to the ANN to predict the runoff discharge 1 day ahead. The obtained results show the proposed model can predict both short and long term runoff discharges because of using multi-scale time series of rainfall and runoff data as the ANN input layer.
Claudio Paniconi - One of the best experts on this subject based on the ideXlab platform.
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Algorithm for Delineating and Extracting Hillslopes and Hillslope Width Functions from Gridded Elevation Data
Journal of Hydrologic Engineering, 2014Co-Authors: P. Noel, Claudio Paniconi, Alain N. Rousseau, Daniel F. NadeauAbstract:AbstractThe subdivision of catchments into appropriate topography-based hydrologic units is an essential step in Rainfall-Runoff Modeling, with the hillslope serving as a common fundamental unit for this purpose. Hillslope-based Modeling approaches can utilize, for instance, the hillslope width function as a one-dimensional representation of three-dimensional landscapes by introducing profile curvatures and plan shapes. In this work, an algorithm was developed to delineate and extract hillslopes and hillslope width functions based on a new approach to calculate average profile curvatures and plan shapes from digital terrain data. The proposed method uses fuzzy logic rules and provides a quick and reliable assessment of hillslope characteristics, classifying hillslopes according to nine elementary landscapes (the so-called Dikau shapes). The algorithm was first tested on two contrasting (flat and steep) catchments in Quebec, Canada. The hillslope width functions obtained with the proposed method were able ...
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An algorithm for delineating and extracting hillslopes and hillslope width functions from gridded elevation data
Hydrology and Earth System Sciences Discussions, 2011Co-Authors: P. Noel, Alain N. Rousseau, Claudio PaniconiAbstract:The subdivision of catchments into appropriate topography-based hydrologic units is an essential step in Rainfall-Runoff Modeling, with the hillslope serving as a common fundamental unit for this purpose. Hillslope-based Modeling approaches can utilize, for instance, the hillslope width function as a one-dimensional representation of three-dimensional landscapes by introducing profile curvatures and plan shapes. In this work, an algorithm was developed to delineate and extract hillslopes and hillslope width functions based on a new approach to calculate average profile curvatures and plan shapes from digital terrain data. The proposed method uses fuzzy logic rules and provides a quick and reliable assessment of hillslope characteristics, classifying hillslopes according to nine elementary landscapes (the so-called Dikau shapes). The algorithm was first tested on two contrasting (flat and steep) catchments in Quebec, Canada. The hillslope width functions obtained with the proposed method were able to preserve the modeled hillslope surface areas within approximately 1% while preserving monotonicity. The algorithm was then applied to the Plynlimon catchments in the United Kingdom for comparison with a pre- viously published scheme. Despite fundamental differences in slope metrics calculations, the proposed method produced qualitatively similar large-scale features. DOI: 10.1061/(ASCE)HE.1943-5584.0000783. © 2014 American Society of Civil Engineers. Author keywords: Distributed hydrologic Modeling; Geographic information systems; Hillslope delineation; Topography-based partitioning algorithms.
Paulin Coulibaly - One of the best experts on this subject based on the ideXlab platform.
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assessing hydrologic impact of climate change with uncertainty estimates bayesian neural network approach
Journal of Hydrometeorology, 2010Co-Authors: Mohammad Sajjad Khan, Paulin CoulibalyAbstract:Abstract A major challenge in assessing the hydrologic effect of climate change remains the estimation of uncertainties associated with different sources, such as the global climate models, emission scenarios, downscaling methods, and hydrologic models. There is a demand for an efficient and easy-to-use rainfall–runoff Modeling tool that can capture the different sources of uncertainties to generate future flow simulations that can be used for decision making. To manage the large range of uncertainties in the climate change impact study on water resources, a neural network–based rainfall–runoff model—namely, Bayesian neural network (BNN)—is proposed. The BNN model is used with Canadian Centre for Climate Modelling and Analysis Coupled GCM, versions 1 and 2 (CGCM1 and CGCM2, respectively) with two emission scenarios, Intergovernmental Panel on Climate Change (IPCC) IS92a and Special Report on Emissions Scenarios (SRES) B2. One widely used statistical downscaling model (SDSM) is used in the analysis. The st...
