Assessment of Land Use Land Cover Classification using Support Vector Machine and Random Forest Techniques in the Agusan River Basin through Geospatial Techniques

Abstract

The Agusan River basin is a lifeline for residents in Agusan del Norte, Agusan del Sur, and Davao del Norte. However, human activities have caused water contamination and siltation, leading to significant structural and physical changes in the river. This study utilized two machine learning classifiers, Support Vector Machine (SVM) and Random Forest (RF), within the Google Earth Engine (GEE) platform to assess the land use and land cover (LULC) changes from 2000 to 2020. The results unequivocally favored SVM, with higher accuracies of 95.53%, 95.61%, and 92.21% in 2000, 2010, and 2020, respectively. Notably, the study unveiled the substantial impact of LULC changes on critical water quality parameters, including turbidity, total suspended solids, and pH. These findings bear profound implications for the conservation and management of the Agusan River Basin, providing policymakers with invaluable insights for crafting interventions to preserve this invaluable natural resource