Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya, Uttarakhand

Abstract

The estimation of stream discharge is an essential component of planning and decision-making. It is highly correlated with many development activities involving water resources. The study of transportation of sediments in the rivers will help us to develop policies and plans for soil conservation, flood control, irrigation, navigation, and aquatic biodiversity problems. Using data-driven models such as Artificial Neural Networks (ANNs), modeling of streamflow and sediment transport is frequently adopted due to their applicability and problem-solving ability. This study has used three training algorithms such as Scaled Conjugate Gradient (SCG), Bayesian Regularization (BR), and Levenberg-Marquardt (LM) to simulate the streamflow and Suspended Sediments Concentration (SSC). After optimizing the best training algorithm based on the model efficiency parameters, L-M based-ANN model has been used to predict streamflow for two years and the modeling of suspended sediments was validated with the help of observed data. The result shows that the simulated results tracked the streamflow as well as SSC with the desired accuracy based on the model efficiency parameters such as coefficient of Determination (R2), Nash Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Root Mean Square Deviation (RMSD). The study's outcomes reveal that in the streamflow the concentration of suspended sediments is significantly affected by the base rock material, glaciers covered by debris, and moraine-laden ice. The transportation of the sediments is high in the Alaknanda basin as compared to the other basins and the previous studies. This might happen due to the severe anthropogenic activities in the surrounding basin.

Long-term hydrological simulation for the estimation of snowmelt contribution of Alaknanda River Basin, Uttarakhand using SWAT

“…The main tributaries of the Alaknanda river are Saraswati (meets at Mana), western Dhauliganga (meets at Vishnuprayag), Nandakini (meets at Nandprayag), Pinder (meets at Karnaprayag), and Mandakini (meets at Rudraprayad). The combination of intense neotectonic activities and extreme rainfall has shaped the geomorphology of the Alaknanda basin into steep slopes, high relief, and a high drainage density (Chopra et al 2012;Rautela et al 2022c). Mountainous terrain makes the basin subject to microclimates, and temperatures vary seasonally and spatially (from river valleys to higher altitude regions).…”

Quantitative analysis of hydropower potential in the upper Beas basin using geographical information system and MIKE 11 Nedbor Afrstromnings Model (NAM)

“…Therefore, determining the river discharge is crucial for assessing the energy generation. There are various direct and indirect methods for assessing stream discharge (Rautela, Kumar, Gandhi, et al, 2022). Direct methods, though easier, are more time-consuming and expensive compared to indirect methods.…”

“…This need arises as hydropower contributes significantly to clean energy production and plays a vital role in meeting the escalating global energy demand. Traditionally, the identification of such high‐potential sites relied on conventional methods and on‐site inspections (Rautela, Kumar, Gandhi, et al, 2022). However, these sites are often situated in remote mountain regions with rugged terrain, posing challenges for traditional identification approaches that are not only time‐consuming but also technically demanding.…”