Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification ApproachReport as inadecuate




Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach - Download this document for free, or read online. Document in PDF available to download.

The Scientific World Journal - Volume 2014 2014, Article ID 432976, 9 pages -

Research Article

Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia UKM, 43000 Bangi, Selangor, Malaysia

Civil Engineering Department, Faculty of Engineering, Universiti Putra Malaysia UPM, 43300 Serdang, Selangor, Malaysia

Received 3 December 2013; Accepted 6 February 2014; Published 24 March 2014

Academic Editors: Y.-S. Cho and P. Fuschi

Copyright © 2014 Nariman Valizadeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system ANFIS is one of the most accurate models used in water resource management. Because the membership functions MFs possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.





Author: Nariman Valizadeh, Ahmed El-Shafie, Majid Mirzaei, Hadi Galavi, Muhammad Mukhlisin, and Othman Jaafar

Source: https://www.hindawi.com/



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