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 Full Text: PDF

The estimation of lithology from multiple geophysical survey methods needs to be addressed to develop advanced tomographic methods. An initial requirement for lithology discrimination is that lithology should be discriminable from the media properties physically related to the geophysical observations. To test this condition for different combinations of the most common crustal rocks, we performed several lithology discrimination exercises on rock samples under laboratory conditions. The physical properties included mass density, compressional velocity, shear velocity, electric conductivity, thermal conductivity, and magnetic susceptibility. A categorical description of the sample lithology was followed; hence, the inference consisted of predicting the sample rock category (lithotype) membership. The joint information provided by the physical properties of the rocks allowed us to discriminate the sample lithotype correctly, with an overall success rate of 100% in the most favorable situation and over 85% in the least favorable situation. We obtained successful classification results for a variety of common lithotypes (granite, gabbro, limestone, tuff, marble, basalt, and gneiss) using three common classification methods: clustering analysis, Gaussian classification, and discriminant analysis. Although discrimination was positive with each of these multivariate classification techniques, discriminant analysis showed some advantages for the classification and graphic analysis of the data. These results support our postulate that lithology can be estimated reliably if multiple geophysical observations are considered jointly.

Initial research has been carried out to determine the potential of SBE as an adsorbent material through chemical and surface area characterization. Several analyses were performed, including oil content, BET, SEM-EDS, XRD, FTIR, and adsorption capacity. The oil content of the SBE samples were...

This article presents measurement data using environmental magnetism method on the bulk surface sediments related to the research article entitled "Heavy metal contents and magnetic properties of surface sediments in volcanic and tropical environment from Brantas River, Jawa Timur Province, Indonesia" Mariyanto et al., 2019. Surface sediments were taken from 20 different locations along Brantas River. In the laboratory, a series of magnetic measurements was conducted on sediment samples i.e. magnetic susceptibility, ARM (Anhysteretic Remanent Magnetization) and IRM (Isothermal Remanent Magnetization). These environmental magnetism data were used to characterize bulk surface sediments in the study area.