A Comparison of Fuzzy Inference Method and Neural Network Method for Runoff Prediction

Zhu, Mu-Lan; Fujita, Mutuhiro

doi:10.2208/prohe.37.75

Cited by 10 publications

(11 citation statements)

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“…Owing to the difficulties associated with the non-linear model structure identification and parameter estimation, ANNs have been used, in recent years, for forecasting/ predicting in many areas of science and engineering, including water resource engineering (Maier and Dandy, 1998;ASCE, 2000b). In surface hydrology, ANNs have been largely applied to rainfall-runoff modelling, flood forecasting, and water quality modelling (e.g., Zhu and Fujita, 1994;Hsu et al, 1995;Jain et al, 2004;Keskin and Terzi, 2006;Khoob, 2008;Tiwari and Chatterjee, 2010). However, ANN-based studies are somewhat limited in the field of subsurface hydrology compared with surface hydrology.…”

Section: Introductionmentioning

confidence: 99%

Efficacy of neural network and genetic algorithm techniques in simulating spatio‐temporal fluctuations of groundwater

Jha

Sahoo

2014

Hydrological Processes

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Groundwater modelling has emerged as a powerful tool to develop a sustainable management plan for efficient groundwater utilization and protection of this vital resource. This study deals with the development of five hybrid artificial neural network (ANN) models and their critical assessment for simulating spatio‐temporal fluctuations of groundwater in an alluvial aquifer system. Unlike past studies, in this study, all the relevant input variables having significant influence on groundwater have been considered, and the hybrid ANN technique [ANN‐cum‐Genetic Algorithm (GA)] has been used to simulate groundwater levels at 17 sites over the study area. The parameters of the ANN models were optimized using a GA optimization technique. The predictive ability of the five hybrid ANN models developed for each of the 17 sites was evaluated using six goodness‐of‐fit criteria and graphical indicators, together with adequate uncertainty analyses. The analysis of the results of this study revealed that the multilayer perceptron Levenberg–Marquardt model is the most efficient in predicting monthly groundwater levels at almost all of the 17 sites, while the radial basis function model is the least efficient. The GA technique was found to be superior to the commonly used trial‐and‐error method for determining optimal ANN architecture and internal parameters. Of the goodness‐of‐fit statistics used in this study, only root‐mean‐squared error, r2 and Nash–Sutcliffe efficiency were found to be more powerful and useful in assessing the performance of the ANN models. It can be concluded that the hybrid ANN modelling approach can be effectively used for predicting spatio‐temporal fluctuations of groundwater at basin or subbasin scales. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Efficacy of neural network and genetic algorithm techniques in simulating spatio‐temporal fluctuations of groundwater

Jha

Sahoo

2014

Hydrological Processes

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“…Previous studies have shown the potential of ANNs for modelling hydrological processes (Zhu and Fujita, 1994;Hsu et al, 1995;Minns and Hall, 1996;Govindaraju and Rao, 2000). The available rainfall forecasting models built by ANNs are based on training the current spatial rainfall information from nearby rain gauges in the study catchments (French et al, 1992;Luk et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Application of an artificial neural network to typhoon rainfall forecasting

Lin

Chen

2005

Hydrol. Process.

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Abstract:A neural network with two hidden layers is developed to forecast typhoon rainfall. First, the model configuration is evaluated using eight typhoon characteristics. The forecasts for two typhoons based on only the typhoon characteristics are capable of showing the trend of rainfall when a typhoon is nearby. Furthermore, the influence of spatial rainfall information on rainfall forecasting is considered for improving the model design. A semivariogram is also applied to determine the required number of nearby rain gauges whose rainfall information will be used as input to the model. With the typhoon characteristics and the spatial rainfall information as input to the model, the forecasting model can produce reasonable forecasts. It is also found that too much spatial rainfall information cannot improve the generalization ability of the model, because the inclusion of irrelevant information adds noise to the network and undermines the performance of the network.

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“…Sugeno and Yasukawa (1993) developed a fuzzy logic based approach to qualitative modeling and have proposed the use of fuzzy clustering method for structure identification of models. The past decade has witnessed a few applications of fuzzy logic approach in water resources (Takagi and Sugeno, 1985;Zhu and Fujita, 1994;Zadeh and Kacprzyk, 1992;Sen, 1998;See and Openshaw, 1999;Mohan and Jothiprakash, 2002;Yu et al, 2000;Hundecha et al, 2001;Titmant et al, 2002 andCheng et al, 2002).· However, the applicability of ANN and fuzzy models suffered from several weaknesses of the individual models. Therefore, combinations of neural networks with fuzzy systems have been proposed, where both models complement each other (Nayak et al, 2004).…”

Section: Neuro-fuzzy Modelsmentioning

confidence: 99%

Soft Computing Tools in Rainfall-Runoff Modeling

Jothiprakash¹,

M.Ish²

2009

ISH Journal of Hydraulic Engineering

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The use of rainfall-runoff models in the decision making process of water resources planning and management has become increasingly indispensable. Rainfall-runoff modeling in the broad sense started at the end of l9 1 h century and till today there are various types of models based on their mechanism, input data and other modeling requirements. These type of models range from physical, conceptual, empirical models and more sophisticated models like Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), Genetic Programming (GP), Model Tree (MT), Support Vector Machine (SVM) and recently Chaos theory. The primary aim of this paper is to review the recent works on Rainfall-Runoff modeling using soft computing techniques.

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A Comparison of Fuzzy Inference Method and Neural Network Method for Runoff Prediction

Cited by 10 publications

References 0 publications

Efficacy of neural network and genetic algorithm techniques in simulating spatio‐temporal fluctuations of groundwater

Efficacy of neural network and genetic algorithm techniques in simulating spatio‐temporal fluctuations of groundwater

Application of an artificial neural network to typhoon rainfall forecasting

Soft Computing Tools in Rainfall-Runoff Modeling

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