Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand

• Published in: Theoretical and applied climatology Vol. 155; no. 5; pp. 4023 - 4047
• Main Authors: Tulla, Paramjeet Singh, Kumar, Pravendra, Vishwakarma, Dinesh Kumar, Kumar, Rohitashw, Kuriqi, Alban, Kushwaha, Nand Lal, Rajput, Jitendra, Srivastava, Aman, Pham, Quoc Bao, Panda, Kanhu Charan, Kisi, Ozgur
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01-05-2024; Springer Nature B.V
• Subjects: Adaptive systems; Agricultural production; Algorithms; Aquatic Pollution; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Climatology; Earth and Environmental Science; Earth Sciences; Estimation; Fuzzy logic; Hydrologic data; Machine learning; Regression models; Runoff; Sediment; Sediment concentration; Sediment load; Sediment yield; Sediments; Soil erosion; Suspended load; Suspended sediments; Waste Water Technology; Water erosion; Water Management; Water Pollution Control; Water quality; Watersheds; Yield forecasting; Uttarakhand India; India
• ISSN: 0177-798X; 1434-4483
• DOI: 10.1007/s00704-024-04862-5

Water erosion creates adverse impacts on agricultural production, infrastructure, and water quality across the world, especially in hilly areas. Regional-scale water erosion assessment is essential, but existing models could have been more efficient in predicting the suspended sediment load. Further, data scarcity is a common problem in predicting sediment load. Thus, the current study aimed at modeling the suspended sediment yield of a hilly watershed (
i.e.,
Bino watershed, Uttarakhand-India) using machine learning (ML) algorithms for a data-scarce situation. For this purpose, the ML models, viz., adaptive neuro-fuzzy inference system (ANFIS) and fuzzy logic (FL) were developed using data from ten years (2000–2009) only. Further, runoff and suspended sediment concentration (SSC) were obtained as the primary influencing factors. Varying combinations of lagged SSC and runoff data were considered as model inputs. The ANFIS and FL models were compared with the conventional multiple linear regression (MLR) model. Results indicated that the ANFIS model performed better than the FL and MLR models. Thus, it was concluded that the ANFIS model could be used as a benchmark for sediment yield prediction in hilly terrain in data-scarce situations. The research work would help field investigators in selecting the proper tool for estimating suspended sediment yield/load and policymakers to make appropriate decisions to reduce the devastating impact of soil erosion in hilly terrains.