Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA

被引：0
作者：
Zaher Mundher Yaseen
Mazen Ismaeel Ghareb
Isa Ebtehaj
Hossein Bonakdari
Ridwan Siddique
Salim Heddam
Ali A. Yusif
Ravinesh Deo
机构：
[1] Universiti Kebangsaan Malaysia,Civil and Structural Engineering Department, Faculty of Engineering and Built Environment
[2] University of Anbar,Dams and Water Resources Department, College of Engineering
[3] University of Human Development,Department of Computer Science, College of Science and Technology
[4] University of Huddersfield,Department of Informatic, School of Computing and Engineering
[5] Razi University,Department of Civil Engineering
[6] University of Massachusetts,Northeast Climate Science Center
[7] University of Massachusetts,Department of Civil and Environmental Engineering
[8] University 20 Août 1955,Faculty of Science, Agronomy Department, Hydraulics Division
[9] University of Duhok,Water Resources Engineering Department, College of Engineering
[10] University of Southern Queensland,School of Agricultural, Computational and Environmental Sciences, Institute of Agriculture and Environment (I Ag & E)
来源：
Water Resources Management | 2018年 / 32卷
关键词：
Rainfall forecasting; Tropical environment; Stochastic pattern; Hybrid ANFIS-FFA model;
D O I：
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中图分类号：
学科分类号：
摘要：
In this study, a new hybrid model integrated adaptive neuro fuzzy inference system with Firefly Optimization algorithm (ANFIS-FFA), is proposed for forecasting monthly rainfall with one-month lead time. The proposed ANFIS-FFA model is compared with standard ANFIS model, achieved using predictor-predictand data from the Pahang river catchment located in the Malaysian Peninsular. To develop the predictive models, a total of fifteen years of data were selected, split into nine years for training and six years for testing the accuracy of the proposed ANFIS-FFA model. To attain optimal models, several input combinations of antecedents’ rainfall data were used as predictor variables with sixteen different model combination considered for rainfall prediction. The performances of ANFIS-FFA models were evaluated using five statistical indices: the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), Willmott’s Index (WI), root mean square error (RMSE) and mean absolute error (MAE). The results attained show that, the ANFIS-FFA model performed better than the standard ANFIS model, with high values of R2, NSE and WI and low values of RMSE and MAE. In test phase, the monthly rainfall predictions using ANFIS-FFA yielded R2, NSE and WI of about 0.999, 0.998 and 0.999, respectively, while the RMSE and MAE values were found to be about 0.272 mm and 0.133 mm, respectively. It was also evident that the performances of the ANFIS-FFA and ANFIS models were very much governed by the input data size where the ANFIS-FFA model resulted in an increase in the value of R2, NSE and WI from 0.463, 0.207 and 0.548, using only one antecedent month of data as an input (t-1), to almost 0.999, 0.998 and 0.999, respectively, using five antecedent months of predictor data (t-1, t-2, t-3, t-6, t-12, t-24). We ascertain that the ANFIS-FFA is a prudent modelling approach that could be adopted for the simulation of monthly rainfall in the present study region.
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页码：105 / 122
页数：17
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