TY - JOUR ID - 133932 TI - Modelling of Daily Suspended Sediment Concentration Using FFBPNN and SVM Algorithms JO - Journal of Soft Computing in Civil Engineering JA - SCCE LA - en SN - AU - Rahul, Atul Kumar AU - Shivhare, Nikita AU - Kumar, Shashi AU - Dwivedi, Shyam Bihari AU - Dikshit, Prabhat Kumar Singh AD - Assistant Professor, Faculty of Civil Engineering, MIT, Muzaffarpur, Bihar, Research Scholar IIT (BHU), Varanasi, India AD - Assistant Professor, Faculty of Electronics and Communication Engineering, Oriental Group of Institutions, India AD - Assistant Professor, Faculty of Civil Engineering, SIT, Sitamarhi, Bihar, India AD - Professor, Faculty of Civil Engineering, IIT (BHU), Varanasi, India Y1 - 2021 PY - 2021 VL - 5 IS - 2 SP - 120 EP - 134 KW - FFBPNN KW - ANN KW - SVM KW - SSC DO - 10.22115/scce.2021.283137.1305 N2 - The river is an essential resource of fresh water on the earth, and its management is very challenging. Sedimentation and erosion is a very complex process of the river system. Suspended sediment concentration (SSC) plays a key role in this process. Therefore, water resources planning and management are essential for this. Generally, the sediment concentration estimated by direct measurement, but this process is costly and cannot apply in all rivers. It is essential to develop some technology that can predict the suspended sediment concentration. So, in this study, a feed forward back propagation neural network (FFBPNN) and support vector machine (SVM) were used to predict the suspended sediment concentration. One year of daily data was collected from the river Ganga at Varanasi cross-section. The performance of the model estimated for training and validation stages based on root mean square error (RSME), Coefficient of correlation (R) and Nash–Sutcliffe model efficiency (NSE). The performance of applied model indicated that FFBPNN (RSME = 176.2, R = 0.955 and NSE = 0.912) for validation is more precise for suspended sediment load prediction than SVM (RSME = 222.1, R = 0.930 and NSE = 0.864). This study shows that the soft computing technique is a robust tool for SSC prediction. UR - https://www.jsoftcivil.com/article_133932.html L1 - https://www.jsoftcivil.com/article_133932_afb5867d83a7bbc9eac02a19f58aea9f.pdf ER -