Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction

被引：0
作者：
Sarita Gajbhiye Meshram
Mir Jafar Sadegh Safari
Khabat Khosravi
Chandrashekhar Meshram
机构：
[1] Ton Duc Thang University,Department for Management of Science and Technology Development
[2] Ton Duc Thang University,Faculty of Environment and Labour Safety
[3] Yaşar University,Department of Civil Engineering
[4] Sari Agricultural Science and Natural Resources University,Department of Watershed Management Engineering
[5] College of Chhindwara University,Department of Post Graduate Studies and Research in Mathematics, Jayawanti Haksar Government Post Graduation College
[6] Chhindwara,undefined
来源：
Environmental Science and Pollution Research | 2021年 / 28卷
关键词：
Hybrid technique; Iterative classifier optimizer; Pace regression; Random forest; River; Suspended sediment load;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the service life of the downstream dam. To this end, estimation models are needed to compute suspended sediment load in rivers. The application of artificial intelligence (AI) techniques has become popular in water resources engineering for solving complex problems such as sediment transport modeling. In this study, novel integrative intelligence models coupled with iterative classifier optimizer (ICO) are proposed to compute suspended sediment load in Simga station in Seonath river basin, Chhattisgarh State, India. The proposed models are hybridization of the random forest (RF) and pace regression (PR) models with the iterative classifier optimizer (ICO) algorithm to develop ICO-RF and ICO-PR hybrid models. The recommended models are established using the discharge and sediment daily data spanning a 35-year period (1980–2015). The accuracy of the developed models is examined in terms of error; by root mean square error (RMSE) and mean absolute error (MAE); and based on a correlation index of determination coefficient (R2). The proposed novel hybrid models of ICO-RF and ICO-PR have been found to be more precise than their stand-alone counterparts of RF and PR. Overall, ICO-RF models delivered better accuracy than their alternatives. The results of this analysis tend to claim the appropriateness of the implemented methodology for precise modeling of the suspended sediment load in rivers.
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页码：11637 / 11649
页数：12
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