International Journal of Agricultural Engineering

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Suspended Sediment Load Estimation Using Neuro-Fuzzy and Multiple Linear Regression:Vamsadhara River Basin, India


Affiliations
1 Department of Soil and Water Conservation Engineering, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), India
     

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Soil erosion by water is the most serious form of land degradation resulting in loss of crop productivity by 0.2-10.9 q/ha (66% total production loss) for cereals, 0.1-6.3 q/ha for oilseeds (21% total production loss) and 0.04-4.4 q/ha for pulses (13% total production loss) estimated across states, which has a direct bearing on food security of the country. Therefore, a major challenge still remaining is the accurate prediction of the catchment sediment yield responses to the rainfall-runoff events. One viable approach to this challenge is the use of suitable statistical and soft-computing techniques for the efficient management of watersheds and ecosystems. The present study deals with the development of adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) models to estimate the suspended sediment load from Vamsadhara river catchment comprising of 7820 km2, situated between Mahanadi and Godavari river basins in south India. Considering the active monsoon period, 70% data were used for model calibration and remaining 30% data were used for model validation. Results revealed that the Neuro-Fuzzy models are in good agreement with the observed values and present better performance in comparison to the statistical models.

Keywords

Adaptive Neuro-Fuzzy Inference System, Multiple Linear Regression, Calibration, Validation, Soft-Computing.
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  • Suspended Sediment Load Estimation Using Neuro-Fuzzy and Multiple Linear Regression:Vamsadhara River Basin, India

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Authors

Shreya Nivesh
Department of Soil and Water Conservation Engineering, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), India
Pravendra Kumar
Department of Soil and Water Conservation Engineering, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), India

Abstract


Soil erosion by water is the most serious form of land degradation resulting in loss of crop productivity by 0.2-10.9 q/ha (66% total production loss) for cereals, 0.1-6.3 q/ha for oilseeds (21% total production loss) and 0.04-4.4 q/ha for pulses (13% total production loss) estimated across states, which has a direct bearing on food security of the country. Therefore, a major challenge still remaining is the accurate prediction of the catchment sediment yield responses to the rainfall-runoff events. One viable approach to this challenge is the use of suitable statistical and soft-computing techniques for the efficient management of watersheds and ecosystems. The present study deals with the development of adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) models to estimate the suspended sediment load from Vamsadhara river catchment comprising of 7820 km2, situated between Mahanadi and Godavari river basins in south India. Considering the active monsoon period, 70% data were used for model calibration and remaining 30% data were used for model validation. Results revealed that the Neuro-Fuzzy models are in good agreement with the observed values and present better performance in comparison to the statistical models.

Keywords


Adaptive Neuro-Fuzzy Inference System, Multiple Linear Regression, Calibration, Validation, Soft-Computing.

References