Tropical Indian river basins are well-known for high and low discharges with high peaks of flood during the summer and the rest of the year, respectively. A high intensity of rainfall due to cyclonic and monsoon winds have caused the tropical Indian rivers to witness more runoff. These rivers are also known for carrying a significant amount of sediment load. The complex and non-linear nature of the sediment yield and runoff processes and the variability of these processes depend on precipitation patterns and river basin characteristics. There are a number of other elements that make it difficult to forecast with great precision. The present study attempts to model rainfall–runoff–sediment yield with the help of five machine learning (ML) algorithms—support vector regression (SVR), artificial neural network (ANN) with Elman network, artificial neural network with multilayer perceptron network, adaptive neuro-fuzzy inference system (ANFIS), and local linear regression, which are useful in river basins with scarce hydrological data. Daily, weekly, and monthly runoff and sediment yield (SY) time series of Vamsadhara river basin, India for a period from 1 June to 31 October for the years 1984 to 1995 were simulated using models based on these multiple machine learning algorithms. Simulated results were tested and compared by means of three evaluation criteria, namely Pearson correlation coefficient, Nash–Sutcliffe efficiency, and the difference of slope. The results suggested that daily and weekly predictions of runoff based on all the models can be successfully employed together with precipitation observations to predict future sediment yield in the study basin. The models prepared in the present study can be helpful in providing essential insight to the erosion–deposition dynamics of the river basin.

Scour hole that occurs downstream of the hydraulic structures threatens the safety and stability of the hydraulic structures. The scour around the structures is a complex and important hydraulic phenomenon; hence, it requires a data extensive research for the accurate estimation of scour depth. Although many analytical models are available for scour depth estimation, they suffer from huge limitations. In this research, the support vector regression (SVR) model and SVR ensemble with the metaheuristic algorithm of innovative gunner (SVR-AIG) models have been developed for accurate prediction of scour depth downstream of the ski-jump spillways. Field measurements including head and discharge intensity are used for developing the models. The performances of the models are compared using root mean square error (RMSE), mean average error (MAE), and correlation coefficient (CC) criteria and some statistical plots. The results showed that the hybrid SVR-AIG-based estimations (with CC = 0.987, 0.991, RMSE = 2.839, 1.987, and MAE = 2.247, 1.201) are more accurate than the SVR standalone model estimations (with CC = 0.942, 0.975, RMSE = 5.686, 4.040, and MAE = 4.114, 3.201) at the training and testing phases. This study is an important reference for analyzing the high capability of the AIG as an optimization tool in improving scour estimations of a standalone model. Also, this algorithm eliminates the trial-and-error procedure to optimize the internal parameters during the model development. Graphical abstract