Long-Term Prediction of Discharges in Manwan Hydropower Using Adaptive-Network-Based Fuzzy Inference Systems Models

Cheng, Chuntian; Lin, Jianyi; Sun, Yaqi; Chau, Kwok-wing

doi:10.1007/11539902_145

Cited by 83 publications

(34 citation statements)

References 19 publications

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“…Long-term hydrological prediction is of significance for water resource activities, such as reservoir operation [1][2][3][4][5], water resource planning [6][7][8][9], risk management [10][11][12][13], and urbanization [14,15]. Hence, hydrologic time-series forecasting, especially monthly inflow, has triggered great interest in hydrology and water resources fields [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Cheng

Feng

Niu

et al. 2015

Water

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Abstract:Reservoir monthly inflow is rather important for the security of long-term reservoir operation and water resource management. The main goal of the present research is to develop forecasting models for the reservoir monthly inflow. In this paper, artificial neural networks (ANN) and support vector machine (SVM) are two basic heuristic forecasting methods, and genetic algorithm (GA) is employed to choose the parameters of the SVM. When forecasting the monthly inflow data series, both approaches are inclined to acquire relatively poor performances. Thus, based on the thought of refined prediction by model combination, a hybrid forecasting method involving a two-stage process is proposed to improve the forecast accuracy. In the hybrid method, the ANN and SVM are, first, respectively implemented to forecast the reservoir monthly inflow data. Then, the processed predictive values of both ANN and SVM are selected as the input variables of a newly-built ANN model for refined forecasting. Three models, ANN, SVM, and the hybrid method, are developed for the monthly inflow forecasting in Xinfengjiang reservoir with 71-year discharges from 1944 to 2014. The comparison of results reveal that three models have satisfactory performances in the Xinfengjiang reservoir monthly inflow prediction, and the hybrid method performs better than ANN and SVM in terms of five statistical indicators. Thus, the hybrid method is an efficient tool for the long-term operation and dispatching of Xinfengjiang reservoir. OPEN ACCESSWater 2015, 7 4478

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Section: Introductionmentioning

confidence: 99%

“…Moreover, the research can be promoted rapidly on the basis of our early works on ANN and SVM [8,40,41]. Hence, we choose ANN and SVM for reservoir monthly inflow forecasting.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Cheng

Feng

Niu

et al. 2015

Water

Self Cite

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“…In addition to the above-mentioned points, the neuro-fuzzy and neural network approaches can also predict suspended sediment concentration in rivers (Rajaee et al 2009). Other researchers reported good results in applying ANFIS in hydrological prediction (Cheng et al 2005;Firat & Gungor 2008;Zounemat-Kermani & Teshnehlab 2008;Wang et al 2009). Another study developed the ANN model used to forecast groundwater level changes in an aquifer (Tsanis et al 2008) and ANNs have also been shown to give useful results in many fields of hydrology and water resources research (Campolo et al 2003;Alvisi et al 2006;.…”

Section: Introductionmentioning

confidence: 93%

Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran

Jalalkamali

Sedghi

Manshouri

2010

Journal of Hydroinformatics

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The prediction of groundwater levels in a well has immense importance in the management of groundwater resources, especially in arid regions. This paper investigates the abilities of neurofuzzy (NF) and artificial neural network (ANN) techniques to predict the groundwater levels. Two different NF and ANN models comprise various combinations of monthly variablities, that is, air temperature, rainfall and groundwater levels in neighboring wells. The result suggests that the NF and ANN techniques are a good choice for the prediction of groundwater levels in individual wells.Also based on comparisons, it is found that the NF computing techniques have better performance than the ANN models in this case.

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“…The basic rules can be constructed either from a human or automatic generation, where the searching rules using input-output data numerically. There are several types of FIS, namely Takagi-Sugeno, Mamdani, and Tsukamoto (Cheng et al 2005). A FIS of Takagi-Sugeno model was found to be widely used in the application of ANFIS method.…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Interference System

Suparta

Alhasa

2015

SpringerBriefs in Meteorology

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This chapter explains in detail the theoretical background of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The detailed explanation of this method will highlight its importance in the estimation of ZTD model.Keywords Artificial neural network Á ANFIS Á Fuzzy inference system Á Hybrid learning algorithm Á Backpropagation Artificial Neural NetworksGenerally, an artificial neural network (ANN) is a system developed for information processing, where it has a similar way with the characteristics of biological neural systems. It was developed based on the human brain, which is capable of processing information, which are complex, nonlinear, and being able to work in parallel, distributed, and local processing and adaptation. ANN is designed to resemble the brain systems such as the construction of architectural structures, learning techniques, and operating techniques. This is the reason that ANN has been widely adopted by scientists because of its accuracy and its ability to develop complex nonlinear models and is used to solve a wide variety of tasks, especially in the field of climate and weather. This section will discuss the capabilities of ANN such as neurons modeling, architecture, and its learning process. Neuron ModelingIn the human brain, there are neurons that are interconnected to one another. These neurons act as a tool that can perform processing of information of human senses. Haykin (2009) described that a biological neuron consists of a cell body, where

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Long-Term Prediction of Discharges in Manwan Hydropower Using Adaptive-Network-Based Fuzzy Inference Systems Models

Cited by 83 publications

References 19 publications

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran

Adaptive Neuro-Fuzzy Interference System

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