Browsing by Author "Pravendra Kumar"

Now showing 1 - 2 of 2

Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand
(Springer, 2024) Paramjeet Singh Tulla; Pravendra Kumar; Dinesh Kumar Vishwakarma; Rohitashw Kumar; Alban Kuriqi; Nand Lal Kushwaha; Jitendra Rajput; Aman Srivastava; Quoc Bao Pham; Kanhu Charan Panda; Ozgur Kisi
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. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.
Rainfall-runoff modeling using artificial neural networks (ANNs) and multiple linear regression (MLR) techniques
(Ecological Society of India, 2016) Vijay Kumar Singh; Pravendra Kumar; Bhaskar Pratap Singh
Runoff prediction has an important role in hydrology, water management, flood prediction and socio-economical concern. The effective flood management is always of great apprehension in the field of hydrology and water resources engineering. The present study shows the comparison of various training algorithms available for training multi-layer perceptron (MLP) in artificial neural networks (ANNs) and multiple linear regressions (MLR) for modeling the rainfall-runoff process. Gamma test (GT) is one of the non-linear modeling tools whereby an appropriate combination from input parameters can be investigated for modeling the output data as well as establishing a smooth model, to develop and evaluate the applicability of the MLP and MLR models by way of training and testing of developed models during monsoon period (June to September). The ANN models were trained using multi-layer perceptron with various types of algorithm namely Momentum, Quickprop, Delta-Bar-Delta, Conjugate Gradient and Levenberg Marquardt. The performance of the models were evaluated qualitatively by visual observation and quantitatively using different performance indices viz. root mean square error (RMSE), correlation coefficient (r), coefficient of efficiency (CE).