Runoff and sediment yield modeling using ANN and support vector machines: a case study from Nepal watershed

Abstract

Physics-based models for simulation of runoff and sediment yield from watersheds are relatively composite model based on learning algorithm. Physics-based is complex model due to involvement of tremendous spatial variability of watershed characteristics and precipitation patterns. Recently, pattern-learning algorithms such as the artificial neural networks (ANNs) have gained recognition in simulating the rainfall-runoff-sediment yield processes producing a comparable accuracy. We have simulated daily runoff and sediment yield from a Nepal watershed, Kankaimai (area = 1180 km 2), with data from 1995 to 1999 for runoff prediction and 2001-2003 of the wet season for sediment yield prediction, using support vector machines (SVMs), a statistical learning theory based pattern-learning algorithm. The performance of the model was evaluated using the root mean square error, correlation coefficient and coefficient of efficiency. The results of SVM were compared to those of ANN and simple regression. ANN being a computationally intensive method, SVM could be used as an efficient alternative for runoff and sediment yield predictions under comparable accuracy in predictions.

Simultaneous modelling and forecasting of hourly dissolved oxygen concentration (DO) using radial basis function neural network (RBFNN) based approach: a case study from the Klamath River, Oregon, USA

“…Some different applications are as follows. Prediction of uniaxial compressive strength of travertine rocks ; temperature variations and generate missing temperature data in Iran (Salami and Ehteshami 2016); river flow forecasting (Kasiviswanathan and Sudheer 2016); prediction of water quality index in groundwater systems (Sakizadeh 2016 (Javan et al 2015); runoff and sediment yield modeling (Sharma et al 2015); modeling Secchi disk depth (SD) in river (Heddam 2016b); and predicting phycocyanin (PC) pigment concentration in river (Heddam 2016c).…”

A Comparison of Three Soft Computing Techniques, Bayesian Regression, Support Vector Regression, and Wavelet Regression, for Monthly Rainfall Forecast

“…Determining the appropriate value of these parameters is often a heuristic trial-and-error process [25]. It is shown that an exponentially growing sequence of parameters works better [29].…”

“…A comparison of the WR model and ANN has shown that the WR model provided better monthly rainfall forecast [9]. Many studies have shown SVR models to be better than ANN models [1,7,20,29]. The WR model decomposes the monthly rainfall time series into detail and approximation components using DWT, and a new time series is generated by adding an approximation component and effective detail components.…”

Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches

“…Findings from their study presented that the hybrid model helps in making run time substantially quicker with greater accurateness in runoff prediction. Misra et al (2009) and Sharma et al (2015) applied SVM and ANN models for predicting both runoff and sediment concentration of different watersheds. An outcome of SVM was assessed against ANN and simple regression, and it was observed that the SVM model was more competent for predicting runoff and sediment concentration under equivalent prediction accurateness.…”