{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T11:02:11Z","timestamp":1762254131422,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T00:00:00Z","timestamp":1650326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Water"],"abstract":"<jats:p>This study was undertaken with the primary objective of modeling grain velocity based on experimental data obtained under the controlled conditions of a laboratory using a rectangular hydraulic tilting channel. Soft computing approaches, i.e., support vector machine (SVM), artificial neural network (ANN), and multiple linear regression (MLR), were applied to simulate grain velocity using four input variables; shear velocity, exposed area to base area ratio (EATBAR), relative depth, and sediment particle weight. Quantitative performance evaluation of predicted values was performed with the help of three different standard statistical indices, such as the root mean square error (RMSE), Pearson\u2019s correlation coefficient (PCC), and Wilmot index (WI). The results during the testing phase revealed that the SVM model has RMSE (m\/s), PCC, and WI values obtained as 0.1195, 0.8877, and 0.7243, respectively, providing more accurate predictions than the MLR and ANN models during the testing phase.<\/jats:p>","DOI":"10.3390\/w14091325","type":"journal-article","created":{"date-parts":[[2022,4,20]],"date-time":"2022-04-20T02:04:22Z","timestamp":1650420262000},"page":"1325","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Modeling Average Grain Velocity for Rectangular Channel Using Soft Computing Techniques"],"prefix":"10.3390","volume":"14","author":[{"given":"Anuradha","family":"Kumari","sequence":"first","affiliation":[{"name":"Department of SWCE, G. B. Pant University of Agriculture and Technology, Pantnagar 263145, India"}]},{"given":"Akhilesh","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of SWCE, G. B. Pant University of Agriculture and Technology, Pantnagar 263145, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5063-9588","authenticated-orcid":false,"given":"Manish","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of SWCE, G. B. Pant University of Agriculture and Technology, Pantnagar 263145, India"},{"name":"College of Agricultural Engineering and Technology, Dr. Rajendra Prasad Central Agricultural University, Pusa 848125, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7464-8377","authenticated-orcid":false,"given":"Alban","family":"Kuriqi","sequence":"additional","affiliation":[{"name":"CERIS, Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chien, N., and Wan, Z. (1999). 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