Forecasting the Level of Reservoirs Using Multiple Input Fuzzification in ANFIS

Valizadeh, Nariman; El-Shafie, Ahmed

doi:10.1007/s11269-013-0349-5

Cited by 38 publications

(11 citation statements)

References 24 publications

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“…The Sugeno-fuzzy structure of ANFIS comprised a five-layer network ( Figure 3). More comprehensive information about ANFIS can be found in the literature [11,[43][44][45]. …”

Section: Adaptive Neuro Fuzzy Inference Systemmentioning

confidence: 99%

A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition

Gong

Wang

et al. 2018

Water

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Abstract:The reliable and accurate prediction of groundwater levels is important to improve water-use efficiency in the development and management of water resources. Three nonlinear time-series intelligence hybrid models were proposed to predict groundwater level fluctuations through a combination of ensemble empirical mode decomposition (EEMD) and data-driven models (i.e., artificial neural networks (ANN), support vector machines (SVM) and adaptive neuro fuzzy inference systems (ANFIS)), respectively. The prediction capability of EEMD-ANN, EEMD-SVM, and EEMD-ANFIS hybrid models was investigated using a monthly groundwater level time series collected from two observation wells near Lake Okeechobee in Florida. The statistical parameters correlation coefficient (R), normalized mean square error (NMSE), root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NS), and Akaike information criteria (AIC) were used to assess the performance of the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models. The results achieved from the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models were compared with those from the ANN, SVM and ANFIS models. The three hybrid models (i.e., EEMD-ANN, EEMD-SVM, and EEMD-ANFIS) proved to be applicable to forecast the groundwater level fluctuations. The values of the statistical parameters indicated that the EEMD-ANFIS and EEMD-SVM models achieved better prediction results than the EEMD-ANN model. Meanwhile, the three models coupled with EEMD were found have better prediction results than the models that were not. The findings from this study indicate that the proposed nonlinear time-series intelligence hybrid models could improve the prediction capability in forecasting groundwater level fluctuations, and serve as useful and helpful guidelines for the management of sustainable water resources.

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“…The Sugeno-fuzzy structure of ANFIS comprised a five-layer network ( Figure 3). More comprehensive information about ANFIS can be found in the literature [11,[43][44][45]. …”

Section: Adaptive Neuro Fuzzy Inference Systemmentioning

confidence: 99%

A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition

Gong

Wang

et al. 2018

Water

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“…It uses data from the daily water level in the reservoir from 1999 to 2006. According to Valizadeh and El-Shafie [9], methods such as linear regression and the ARIMA model and its variants were the tools that were used until neuronal networks were imposed on these types of studies. Combinations of neural networks with other methods such as diffuse logic (Chaves and Kojiri [10]; El-Shafie et al [11]) and SVM (Wieland et al [12]; Kisi et al [13]), have tried to influence this area to improve predictions of models based on the predominant neural networks.…”

Section: State Of the Artmentioning

confidence: 99%

Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs

2019

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Reservoirs are natural or artificial lakes used as a source of water supply for society daily applications. In addition, hydroelectric power plants produce electricity while water flows through the reservoir. However, reservoirs are limited natural resources since water levels vary according to annual rainfalls and other natural events, and consequently, the energy generation. Therefore, forecasting techniques are helpful to predict water level, and thus, electricity production. This paper examines state-of-the-art methods to predict the water level in Catalan reservoirs comparing two approaches: using the water level uniquely, uni-variant; and adding meteorological data, multi-variant. With respect to relating works, our contribution includes a longer times series prediction keeping a high precision. The results return that combining Support Vector Machine and the multi-variant approach provides the highest precision with an R 2 value of 0.99.

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“…Hydrology and hydraulics, and basin management studies, have received increasing attention in the surveyed literature. Most hydrology and hydraulic studies are related to the use of models, examples being artificial neural networks, GIS and statistical models for hydrological forecasting and reservoir operation (Fayaed et al 2013;Valizadeh & El-Shafie 2013). The recent increase in hydrological forecasting research is critical, because hydrological variability is changing and threatening water uses (Montanari et al 2013).…”

Section: Lake Research In Malaysiamentioning

confidence: 99%

A national research and development blueprint for sustainable lake basin management in Malaysia

Sharip¹,

Suratman²,

Shaaban³

2016

Lakes & Reservoirs

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The deterioration of water quality in lakes resulting from unsustainable catchment practices requires concerted efforts to address them, in the form of cooperation among government agencies and institutions, private bodies and the community‐at‐large. Research that provides an understanding of a lake's ecosystem is an important aspect of making informed decisions for sustainable management and governance of lakes. The present study describes the findings of literature reviews on Malaysian lakes, and the national efforts made to establish an agenda for lake research and development in Malaysia directed towards integrated lake basin management (ILBM). The main findings and outcomes of the National Lake Research Blueprint workshop in April 2014 on the issues facing lakes and reservoirs are highlighted. Seven research themes were identified, namely (i) ecosystem services and socio‐economic development, (ii) governance, (iii) water quality and pollution, (iv) eco‐hydrology and basin management, (v) biodiversity and natural products, (vi) physical limnology and hydrodynamics and (vii) technology. The goal is to enhance research and development on sustainable lake basin management directed to conservation and development targets. The findings of this study provide an integrated research framework that can support ILBM governance elements (pillars) elsewhere, and to call for researchers to participate in much‐needed research areas.

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Forecasting the Level of Reservoirs Using Multiple Input Fuzzification in ANFIS

Cited by 38 publications

References 24 publications

A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition

A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition

Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs

A national research and development blueprint for sustainable lake basin management in Malaysia

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