Application of Hybrid Support Vector Machine model for Streamflow Prediction in Barak valley, India

Abstract

Forecasting streamflow (Qflow) is vital in flood and water management, determining potential of river water flow, agricultural practices, hydropower generation, and environmental flow study. This research aims to explore capability of hybrid support vector machines (SVM) with Whale Optimisation Algorithm (WOA) model for forecasting streamflow at Badarpur Ghat gauging station of Barak river basin and evaluate its enactment with the conventional SVM model. Root mean squared error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) statistical measures are considered as evaluating standards. Assessment of outcomes indicates that the optimization algorithm could enhance the accurateness of standalone SVM model in monthly streamflow forecasting. Compared to conventional artificial intelligence methods without a data pre-processing system, the comparatively good performance of applied hybrid model gives an effective alternate to achieve better precision in streamflow forecasting. Results confirm that enhanced SVM model can better process a multifaceted hydrogeological data set, have higher prediction accuracy, and possess better generalisation capability.

Impact of economic indicators on rice production: A machine learning approach in Sri Lanka

“…Machine learning algorithms have been applied to various aspects of agriculture, including agricultural data classification, flood prediction [21][22][23][24][25] and crop yield prediction [26,27]. These studies demonstrate the versatility and effectiveness of machine learning in addressing agricultural challenges.…”

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“…Machine learning algorithms have been applied to various aspects of agriculture, including agricultural data classification, flood prediction [21][22][23][24][25] and crop yield prediction [26,27]. These studies demonstrate the versatility and effectiveness of machine learning in addressing agricultural challenges.…”