Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)

被引:145
作者
Hipni, Afiq [1 ]
El-shafie, Ahmed [1 ]
Najah, Ali [1 ]
Karim, Othman Abdul [1 ]
Hussain, Aini [1 ]
Mukhlisin, Muhammad [1 ,2 ]
机构
[1] Univ Kebangsaan Malaysia, Fac Engn & Built Environm, Dept Civil & Struct Engn, Bangi 43600, Selangor, Malaysia
[2] Polytech Negeri Semarang, Dept Civil Engn, Semarang, Indonesia
关键词
Support vector machine; Dam water levels; Klang gate; CROSS-VALIDATION; MANAGEMENT; PERFORMANCE; RESERVOIR; NETWORKS;
D O I
10.1007/s11269-013-0382-4
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Reservoir planning and management are critical to the development of the hydrological field and necessary to Integrated Water Resources Management. The growth of forecasting models has resulted in an excellent model known as the Support Vector Machine (SVM). This model uses linearly separable patterns based on an optimal hyperplane, which are extended to non-linearly separable patterns by transforming the raw data to map into a new space. SVM can find a global optimal solution equipped with Kernel functions. These Kernel functions have high flexibility in the forecasting computation, enabling data to be mapped at a higher and infinite-dimensional space in an implicit manner. This paper presents a new solution to the expert system, using SVM to forecast the daily dam water level of the Klang gate. Four categories are identified to determine the best model: the input scenario, the type of SVM regression, the number of V-fold cross-validation and the time lag. The best input scenario employs both the rainfall R(t-i) and the dam water level L(t-i). Type 2 SVM regression is selected as the best regression type, and 5-fold cross-validation produces the most accurate results. The results are compared with those obtained using ANFIS: all the RMSE, MAE and MAPE values prove that SVM is a superior model to ANFIS. Finally, all the results are combined to determine the best time lag, resulting in R(t-2) L(t-2) for the best model with only 1.64 % error.
引用
收藏
页码:3803 / 3823
页数:21
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