Application and analysis of support vector machine based simulation for runoff and sediment yield

The objective of the study was to use Support Vector Machines (SVM) to simulate runoff and sediment yield from watersheds. Recently, pattern-recognition algorithms such as artificial neural networks (ANN) have gained popularity in simulating rainfall-runoff-sediment yield processes producing comparable accuracy to physics-based models. We have simulated daily, weekly, and monthly runoff and sediment yield from an Indian watershed, with monsoon period data, using SVM, a relatively new pattern-recognition algorithm. Model performance was evaluated using correlation coefficient for evaluating variability, coefficient of efficiency for evaluating efficiency, and the difference of slope of a best-fit line from observed-estimated scatter plots to 1:1 line for evaluating predictability. Time-series data were split into training, calibration and validation sets. The results of SVM were compared to those of ANN. An alternate method, the Multiple Regressive Pattern Recognition Technique (MRPRT), was used for runoff estimation only. The MRPRT did not improve the results significantly compared to SVM, hence, it was not used to simulate sediment yield. We concluded that SVM provided significant improvement in training, calibration and validation as compared to ANN. SVM could be an efficient alternative to ANN, a computationally intensive method, for runoff and sediment yield predictions providing at least comparable accuracy.