Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting

Chowdhury, Md. Sharafat

doi:10.1016/j.envc.2023.100800

Cited by 18 publications

(9 citation statements)

References 76 publications

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“…By ensuring accurate land cover classification, both models, mainly the RF, contribute to effective environmental surveillance and urban planning, which are critical in maintaining the ecological balance and supporting water sustainability in geothermal operations. These findings, detailed in Table 9, highlight the efficacy of these models and their practical implications for improving geothermal field management through precise land use monitoring [38,112,113].…”

Section: Model Evaluation and Accuracy Assessmentmentioning

confidence: 86%

“…The comparison between SVM and RF models across various metrics underscores the RF model's consistent outperformance, with higher ACC, KC, and Sensitivity scores throughout the study period. This superiority is pivotal for accurately identifying land use changes that directly influence water sustainability in geothermal operations [38,112,113]. In detecting water bodies and maintaining high UC and Specificity for agricultural and forested lands, RF's robust performance ensures precise targeting and implementation of water conservation measures [11,38].…”

Section: Discussionmentioning

confidence: 99%

“…Higher KC values indicate a more reliable model for predicting land cover changes directly affecting water runoff patterns, groundwater recharge, and overall water conservation strategies. The RF model's ability to precisely identify land use promotes better informed decisions in environmental planning and management, guaranteeing that actions to conserve water and preserve ecological balance in geothermal fields are based on exact and reliable data [11,38]. Table 10 illustrates that the SVM and RF models exhibit robust performance across various land use categories, though with distinct strengths.…”

Section: Model Evaluation and Accuracy Assessmentmentioning

confidence: 99%

“…Assessing land use models is essential for evaluating their effectiveness in geothermal field applications. Various metrics, such as the confusion matrix, Overall Accuracy (ACC), Kappa Coefficient (KC), Sensitivity, and Specificity, play key roles in this evaluation [37,38]. The confusion matrix offers a detailed view of the model's classification accuracy by showing true positive and negative classifications, facilitating the calculation of other important metrics like ACC, which indicates the overall proportion of correct classifications.…”

Section: Introductionmentioning

confidence: 99%

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

Utama,

Indriani,

Hermana

et al. 2024

J. Hum. Earth Future

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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

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Section: Model Evaluation and Accuracy Assessmentmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Model Evaluation and Accuracy Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Utama,

Indriani,

Hermana

et al. 2024

J. Hum. Earth Future

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“…In SVMs, the hyperplane is developed using a training dataset and validated using a testing dataset to prevent overfitting [55,57]. The efficiency of SVMs with other classification algorithms has been proven in the literature [58,59] In this study, we employed Support Vector Machine (SVM) classification due to its efficiency for LULC classification to delineate four distinct classes: vegetation, bare land, urbanization, and water bodies. Under the vegetation classification, we encompassed dense vegetation, open green spaces, and agricultural land marked by crops and trees.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Assessing the Impacts of Landuse-Landcover (LULC) Dynamics on Groundwater Depletion in Kabul, Afghanistan’s Capital (2000–2022): A Geospatial Technology-Driven Investigation

Ahmadi,

Popalzai,

Bekbotayeva

et al. 2024

Geosciences

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Land use/land cover (LULC) changes significantly impact spatiotemporal groundwater levels, posing a challenge for sustainable water resource management. This study investigates the long-term (2000–2022) influence of LULC dynamics, particularly urbanization, on groundwater depletion in Kabul, Afghanistan, using geospatial techniques. A time series of Landsat imagery (Landsat 5, 7 ETM+, and 8 OLI/TIRS) was employed to generate LULC maps for five key years (2000, 2005, 2010, 2015, and 2022) using a supervised classification algorithm based on Support Vector Machines (SVMs). Our analysis revealed a significant expansion of urban areas (70%) across Kabul City between 2000 and 2022, particularly concentrated in Districts 5, 6, 7, 11, 12, 13, 15, 17, and 22. Urbanization likely contributes to groundwater depletion through increased population growth, reduced infiltration of precipitation, and potential overexploitation of groundwater resources. The CA-Markov model further predicts continued expansion in built-up areas over the next two decades (2030s and 2040s), potentially leading to water scarcity, land subsidence, and environmental degradation in Kabul City. The periodic assessment of urbanization dynamics and prediction of future trends are considered the novelty of this study. The accuracy of the generated LULC maps was assessed for each year (2000, 2005, 2010, 2015, and 2022), achieving overall accuracy values of 95%, 93.8%, 85%, 95.6%, and 93%, respectively. These findings provide a valuable foundation for the development of sustainable management strategies for Kabul’s surface water and groundwater resources, while also guiding future research efforts.

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Assessing the impacts of climate and land cover change on groundwater recharge in a semi-arid region of Southern India

Chandra,

Sahoo

2024

Theor Appl Climatol

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Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting

Cited by 18 publications

References 76 publications

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

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

Assessing the Impacts of Landuse-Landcover (LULC) Dynamics on Groundwater Depletion in Kabul, Afghanistan’s Capital (2000–2022): A Geospatial Technology-Driven Investigation

Assessing the impacts of climate and land cover change on groundwater recharge in a semi-arid region of Southern India

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