Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

Widya Utama, Rista Fitri Indriani, Maman Hermana, Ira Mutiara Anjasmara, Sherly Ardhya Garini, Dhea Pratama Novian Putra


The management and monitoring of land use in geothermal fields are crucial for the sustainable utilization of water resources, as well as for striking a balance between the production of renewable energy and the preservation of the environment. This study primarily compared Support Vector Machine (SVM) and Random Forest (RF) machine learning methods, using satellite imagery from Landsat 8 and Sentinel 2 between 2021 and 2023, to monitor land use in the Patuha geothermal area. The objective is to improve sustainable water management practices by accurately categorizing different land cover types. This comparative analysis assessed the efficacy of these techniques in upholding water sustainability in geothermal regions. This study examined the application of SVM and RF machine learning techniques, with particular emphasis on parameter refinement and model assessment, to enhance land use classification accuracy. By employing Kernlab and e1071 for algorithm comparison, the research sought to produce a precise Land Use Model Map, which underscores the significance of advanced analytical techniques in environmental management. This approach was of utmost importance in improving land use monitoring and reinforcing sustainable practices. The comparative evaluation of SVM and RF methods for land use classification demonstrates the superiority of RF in terms of accuracy, stability, and precision, particularly in intricate urban settings, hence establishing it as the preferred model for tasks demanding high reliability. The application of SVM and RF for monitoring land use in geothermal areas is in alignment with Sustainable Development Goals (SDGs) 6 and 15, as it fosters sustainable water management and the conservation of ecosystems.


Doi: 10.28991/HEF-2024-05-02-06

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Geothermal; Land Use; Random Forest; Sustainability; Support Vector Machine; Water.


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DOI: 10.28991/HEF-2024-05-02-06


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