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

Journal of Human Earth and Future, Год журнала: 2024, Номер 5(2), С. 216 - 242

Опубликована: Июнь 1, 2024

The management and monitoring of land use in geothermal fields are crucial for the sustainable utilization water resources, as well striking a balance between production renewable energy preservation environment. This study primarily compared Support Vector Machine (SVM) Random Forest (RF) machine learning methods, using satellite imagery from Landsat 8 Sentinel 2 2021 2023, to monitor Patuha area. objective is improve practices by accurately categorizing different cover types. comparative analysis assessed efficacy these techniques upholding sustainability regions. examined application SVM RF techniques, with particular emphasis on parameter refinement model assessment, enhance classification accuracy. By employing Kernlab e1071 algorithm comparison, research sought produce precise Land Use Model Map, which underscores significance advanced analytical environmental management. approach was utmost importance improving reinforcing practices. evaluation methods demonstrates superiority terms accuracy, stability, precision, particularly intricate urban settings, hence establishing it preferred tasks demanding high reliability. areas alignment Sustainable Development Goals (SDGs) 6 15, fosters conservation ecosystems. Doi: 10.28991/HEF-2024-05-02-06 Full Text: PDF

Язык: Английский

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Spatial assessment of utility-scale solar photovoltaic siting potential using machine learning approaches: A case study in Aichi prefecture, Japan

Applied Energy, Год журнала: 2025, Номер 383, С. 125329 - 125329

Опубликована: Янв. 16, 2025

Язык: Английский

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Methods for analysing renewable energy potentials in energy system modelling: A review

Renewable and Sustainable Energy Reviews, Год журнала: 2025, Номер 215, С. 115559 - 115559

Опубликована: Март 12, 2025

Язык: Английский

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Optimizing onshore wind power installation within China via geographical multi-objective decision-making

Energy, Год журнала: 2024, Номер 307, С. 132431 - 132431

Опубликована: Июль 25, 2024

Язык: Английский

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Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

International Journal of Electrical Power & Energy Systems, Год журнала: 2025, Номер 167, С. 110643 - 110643

Опубликована: Март 31, 2025

Язык: Английский

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Prediction of product yields and heating value of bio-oil from biomass fast pyrolysis: Explainable predictive modeling and evaluation

Energy, Год журнала: 2025, Номер unknown, С. 136087 - 136087

Опубликована: Апрель 1, 2025

Язык: Английский

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Context-dependent carbon mitigation potential using long-term onshore wind turbine datasets in China

Resources Conservation and Recycling, Год журнала: 2025, Номер 220, С. 108350 - 108350

Опубликована: Май 5, 2025

Язык: Английский

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Renewable energy transition in Europe in the context of renewable energy transition processes in the world. A review

Heliyon, Год журнала: 2024, Номер 10(24), С. e40997 - e40997

Опубликована: Дек. 1, 2024

Язык: Английский

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Noise-like-Signal-Based Sub-Synchronous Oscillation Prediction for a Wind Farm with Doubly-Fed Induction Generators

Electronics, Год журнала: 2024, Номер 13(11), С. 2200 - 2200

Опубликована: Июнь 5, 2024

The DFIG-based wind farm faces sub-synchronous oscillation (SSO) when it is integrated with a series-compensated transmission system. equivalent SSO damping influenced by both speed and compensation level. However, hard for the to obtain level in time predict risk. In this paper, an risk prediction method DFIG proposed based on characteristics identified from noise-like signals. First, SSO-related parameters are analyzed. Then, potential frequency signals at normal working points integration using variational mode decomposition Prony analysis. Finally, fuzzy inference system established of farm. effectiveness verified simulation. can risks caused variation speed, while line undetectable

Язык: Английский

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Revealing the theoretical wind potential of the Qinghai-Tibet Plateau: A novel Bayesian Monte-Carlo framework for the Weibull bivariate distribution

Energy Conversion and Management, Год журнала: 2024, Номер 325, С. 119375 - 119375

Опубликована: Дек. 13, 2024

Язык: Английский

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