Comprehensive analysis of land use and cover dynamics in djibouti using machine learning technique: A multi-temporal assessment from 1990 to 2023

Pandit, Santa; Shimada, Sawahiko; Dube, Timothy

doi:10.1016/j.envc.2024.100920

Cited by 1 publication

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, Rana et al's (2020) research reported ACC values of 0.64 for SVM and 0.7 for RF, whereas the current findings show a significant increase to 0.94 and 0.95, respectively [41]. Similarly, Pandit et al, 2024, highlight the robustness of RF with a top ACC of 0.9 and a Kappa Coefficient of 0.9, which the authors have found to increase to 0.95 and 0.93, respectively, in this study [122]. Zhao et al (2024) also noted a range in User Accuracy from 0.6 to 0.9 for both algorithms, which the data further refines to 0.84 to 0.99.…”

Section: Discussionsupporting

confidence: 57%

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

Utama,

Indriani,

Hermana

et al. 2024

J. Hum. Earth Future

View full text Add to dashboard Cite

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

show abstract

Section: Discussionsupporting

confidence: 57%