Accuracy enhancement for forecasting water levels of reservoirs and river streams using a multiple-input-pattern fuzzification approach.
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.
Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia (UKM), 43000 Bangi, Selangor, Malaysia.
Nariman
0000-0002-8576-1460
N
Valizadeh
Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia (UKM), 43000 Bangi, Selangor, Malaysia.
Ahmed
0000-0001-5018-8505
A
El-Shafie
Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia (UKM), 43000 Bangi, Selangor, Malaysia.
Majid
M
Mirzaei
Civil Engineering Department, Faculty of Engineering, Universiti Putra Malaysia (UPM), 43300 Serdang, Selangor, Malaysia.
Hadi
0000-0001-5311-1116
H
Galavi
Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia (UKM), 43000 Bangi, Selangor, Malaysia.
Muhammad
0000-0002-5139-7058
M
Mukhlisin
Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia (UKM), 43000 Bangi, Selangor, Malaysia.
Othman
O
Jaafar
D000465:Algorithms; D005544:Forecasting; D008962:Models, Theoretical; D045483:Rivers; D014867:Water
D014867:Water