Indian Journal of Science and Technology
Year: 2019, Volume: 12, Issue: 39, Pages: 1-8
W. A. Shaikh1,*, S. F. Shah2, M. A. Solangi2 and Siraj Muhammed Pandhiani3
1 Department of Mathematics and Statistics, Quaid-e-Awam University of Engineering, Science & Technology, Nawabshah, Sindh, Pakistan; [email protected]
2 Department of Basic Sciences & Related Studies, Mehran University of Engineering & Technology, Jamshoro, Sindh, Pakistan; [email protected] , [email protected]
3 Department of General Studies, Jubail University College, Kingdom of Saudi
Objectives: To forecast hydrological datasets using time-series forecasting model, namely, Group Method and Data Handling (GMDH). Methods/statistical analysis: The monthly streamflow datasets covering a period of 485 and 550 months have been collected from two well-known rivers of Pakistan, the Indus and the Chenab, respectively, for the endorsement of the GMDH model. Computed results are compared with two other forecasting models: Least Square Support Vector Machine (LSSVM) and Multivariate Adaptive Regression Splines (MARS). The accuracy of the model has been verified by the following three statistical estimations: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Correlation Coefficient (CE). Findings: The GMDH model has the potential to estimate with high precision the forecast real value of the hydrological datasets compared to the other models discussed in the present article. Findings show that the GMDH forecasting model is more robust than the other models discussed here. Applications/improvements: The novelty of this study is that it provides a trustable forecast of streamflow of the rivers.
Keywords: GMDH, LSSVM, MARS, MAE, RMSE, CE
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