Long Lead Time Forecast of Runoff Using Fuzzy Reasoning Method

Zhu, Mu-Lan; Fujita, Mutsuhiro; Hashimoto, Norihide; Kudo, Mutsunobu

doi:10.3178/jjshwr.7.2_83

Cited by 10 publications

(5 citation statements)

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“…Fuzzy rule based modeling is a qualitative modeling scheme where the system behavior is described using a natural language [ Sugeno and Yasukawa , 1993]. The last decade has witnessed a few applications of fuzzy logic in water resource forecasting [ Fujita et al , 1992; Zhu and Fujita , 1994; Zhu et al , 1994; Stuber et al , 2000; See and Openshaw , 2000; Hundecha et al , 2001; Xiong et al , 2001]. A number of research papers in the past few years [ Minns and Hall , 1996; Khondker et al , 1998; Solomatine et al , 2000; Sudheer and Jain , 2004] have shown that using these data‐driven techniques to model hydrologic processes, such as rainfall‐runoff forecasting, flash flood forecasting and prediction of surge water levels, are promising.…”

Section: Introductionmentioning

confidence: 99%

Short‐term flood forecasting with a neurofuzzy model

Nayak

Sudheer

Rangan

et al. 2005

Water Resources Research

254

115

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[1] This study explores the potential of the neurofuzzy computing paradigm to model the rainfall-runoff process for forecasting the river flow of Kolar basin in India. The neurofuzzy computing technique is a combination of a fuzzy computing approach and an artificial neural network technique. Parameter optimization in the model was performed by a combination of backpropagation and least squares error methods. Performance of the neurofuzzy model was comprehensively evaluated with that of independent fuzzy and neural network models developed for the same basin. The values of three performance evaluation criteria, namely, the coefficient of efficiency, the root-mean-square error, and the coefficient of correlation, were found to be very good and consistent for flows forecasted 1 hour in advance by the neurofuzzy model. The value of the relative error in peak flow prediction was within reasonable limits for the neurofuzzy model. The neurofuzzy model forecasted 47.95% of the total number of flow values 1 hour in advance with less than 1% relative error, while for the neural network and fuzzy models the corresponding values were 36.96 and 18.89%, respectively. The forecasts by the neurofuzzy model at higher lead times (up to 6 hours) are found to be better than those from the neural network model or the fuzzy model, implying that the neurofuzzy model seems to be well suited to exploit the information to model the nonlinear dynamics of the rainfall-runoff process.

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Section: Introductionmentioning

confidence: 99%

Short‐term flood forecasting with a neurofuzzy model

Nayak

Sudheer

Rangan

et al. 2005

Water Resources Research

254

115

View full text Add to dashboard Cite

show abstract

“…FIS are qualitative modelling schemes inspired by the human reasoning ability where the system behaviour is described using a natural language (Takagi and Sugeno, ). There have been a growing number of applications of FIS in water resources engineering (Zhu et al ., ; See and Openshaw, ; Xiong et al ., 2001). Recently, ANN and FIS have been integrated into a single framework called neuro‐FIS (NFIS).…”

Section: Introductionmentioning

confidence: 99%

The data‐driven approach as an operational real‐time flood forecasting model

Nguyen

Chua

2011

Hydrological Processes

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Accurate water level forecasts are essential for flood warning. This study adopts a data-driven approach based on the adaptive network-based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four-and five-lead-day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto-and cross-correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi-step-ahead error prediction was superior to the fully recursive procedure. The RAR-based partial recursive updating procedure significantly improved three-, four-and five-lead-day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR-based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. À Á T is the regression vector that consists of previous values of measured outputs, where p is the order of the recursive regressive model. The gain, K t , determines how much the current prediction error Figure 1. Schematic of ANFIS architecture (Jang, 1993) Figure 8. A comparison of performance between Naïve, URBS and ANFIS: (a) COE, (b) MPAE, (c) MAE and (d) RMSE Figure 9. A comparison of AR a , RAR a , AR b and RAR b updating procedures

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“…Data driven modeling is based on modeling the response of a system to a specific input to develop a relation (or function) that relates input and output variables. It is referred as "data driven" since its parameters are identified by a training process using a set of input-output data (Maier and Dandy, 2000;ASCE 2000a and2000b) including river flood forecasting problem where water levels or discharges at a location of a river is forecasted based on rainfall and/or water levels, discharges at other locations (Halff et al 1993, Zhu et al,1994, Minns and Hall, 1996. In order to compare the performance of ANNs with conventional models had been usually used before, Hsu et al (1995) showed that a MLP neural network was superior than the linear ARMAX time series model and the conceptual SACSMA model (Sacramento Soil Moisture Accounting Model) in both during calibration and validation.…”

Section: Data-driven Modelling Approachmentioning

confidence: 99%

“…Fuzzy logic appears to be effective at handling dynamic, non-linear and noisy data, especially when the underlying physical relationships are not fully understood. Since the past decade there were some initial applications using fuzzy logic approach in hydrological modelling (Fujita et al, 1992;Zhu and Fujita, 1994;Zhu et al, 1994;See and Openshaw, 1999;Stuber et al, 2000;Hundecha et al, 2001) which demonstrates that fuzzy systems can be applied efficiently in hydrological engineering applications.…”

Section: Fuzzy Logic Fuzzy Rules and Fuzzy Inference Systemsmentioning

confidence: 99%

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Adaptive neuro-fuzzy inference system for flood forecasting in a large river system

Nguyen¹

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Large river systems provide essential water resources and economic benefits for a significant number of people. However, large floods that occur less frequently (e.g., 25 to 100-year floods) can cause significant loss of lives and property. Therefore, flow forecasting, which aims to provide accurate forecasts of flow and/or level of a river is an essential tool for flood plain management and flood mitigation purposes. New approaches in computational intelligence tools are now being applied to build effective forecasting models. This research investigated the application of the data driven modeling approach for flood forecasting in the Lower Mekong, which was chosen as a case study. The Adaptive Neuron-Fuzzy Inference System or ANFIS was chosen for this purpose. ANFIS is a modern approach in Computational Intelligence which combines the learning and reasoning capabilities of the Artificial Neural Network and Fuzzy Inference System techniques, respectively. In order to develop a flood forecasting model based on ANFIS, it was important to clarify the link between hydraulic/hydrologic factors (such as flood travel times and the catchment response to rainfall) and ANFIS model performance, as this would provide important information for model input selection. River flows at various stations along a large river can vary in terms of the flow contributions from upstream stations, rainfall and lateral inflows from sub-basins immediately upstream of the station of interest. Therefore, the present study provided an analysis on how these factors influence ANFIS model performance by developing a series of ANFIS models for three stations along the Lower Mekong where the flows at the stations selected differed in terms of the contributions by flow from upstream stations along the main river, laterals and rainfall from the local sub-basin. In such a scenario, the choice of input variables can significantly affect ANFIS model performance. The results obtain showed that the inclusion of water levels from the station of interest and the immediate upstream station were sufficient for 1-and 3lead day water level forecast. The addition of rainfall information beside water levels was not necessary. The ANFIS models developed in the study performed well for 1-and 3-lead-day water level forecasts when compared to the benchmark adopted by Mekong River Commission (MRC). In addition, ANFIS model results CHAPTER 1: INTRODUCTION 1 CHAPTER 1 INTRODUCTION 1.1. Flood Forecasting for Large River Systems Floods in rivers are natural phenomena that occur when water levels in river channels lead to overspill of natural banks or artificial embankments and subsequently inundate the surrounding flood plains (White, 2000). River floods usually occur after heavy rainfalls over river basins during the wet season. This causes increased runoff into upstream reaches of rivers, and when downstream water levels are increased to very high levels, the rivers may overspill the natural banks or artificial embankments or even break dike systems at some locations, re...

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Long Lead Time Forecast of Runoff Using Fuzzy Reasoning Method

Cited by 10 publications

References 0 publications

Short‐term flood forecasting with a neurofuzzy model

Short‐term flood forecasting with a neurofuzzy model

The data‐driven approach as an operational real‐time flood forecasting model

Adaptive neuro-fuzzy inference system for flood forecasting in a large river system

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