Reservoir Inflow Prediction by Employing Response Surface-Based Models Conjunction with Wavelet and Bootstrap Techniques

• Published in: Mathematical problems in engineering Vol. 2021; pp. 1 - 9
• Main Authors: Shehzad, Muhammad Ahmed, Bashir, Adnan, Noor Ul Amin, Muhammad, Khosa, Saima Khan, Aslam, Muhammad, Ahmad, Zubair
• Format: Journal Article
• Language: English
• Published: New York Hindawi 10.11.2021; John Wiley & Sons, Inc
• Subjects: Decomposition; Flood damage; Flood frequency; Flood predictions; Hydrology; Inflow; Mean square errors; Neural networks; Performance indices; Regression analysis; Regression models; Reservoirs; Response surface methodology; River basins; Stream flow; Support vector machines; Time series; Variables; Wavelet transforms
• ISSN: 1024-123X; 1026-7077; 1563-5147; 1563-5147
• DOI: 10.1155/2021/4086918

Reservoir inflow prediction is a vital subject in the field of hydrology because it determines the flood event. The negative impact of the floods could be minimized greatly if the flood frequency is predicted accurately in advance. In the present study, a novel hybrid model, bootstrap quadratic response surface is developed to test daily streamflow prediction. The developed bootstrap quadratic response surface model is compared with multiple linear regression model, first-order response surface model, quadratic response surface model, wavelet first-order response surface model, wavelet quadratic response surface model, and bootstrap first-order response surface model. Time series data of monsoon season (1 July to 30 September) for the year 2010 of the Chenab river basin are analyzed. The studied models are tested by using performance indices: Nash–Sutcliffe coefficient of efficiency, mean absolute error, persistence index, and root mean square error. Results reveal that the proposed model, i.e., bootstrap quadratic response surface shows good performance and produces optimum results for daily reservoir inflow prediction than other models used in the study.