Developing Infiltration Model: Random Forest for Micro-Hydro Power Planning DOI Open Access

Annisa R. Varhana,

Widya Utama, Rista Fitri Indriani

et al.

IOP Conference Series Earth and Environmental Science, Journal Year: 2024, Volume and Issue: 1418(1), P. 012055 - 012055

Published: Dec. 1, 2024

Abstract The goal of this study is to determine the classification infiltration for Micro-Hydro Power Planning using Random Forest (RF) machine learning algorithm. Utilizing Landsat 8 satellite imagery, data provides a comprehensive basis analyzing various environmental factors relevant infiltration. RF algorithm models and classifies rates, ensuring precise reliable predictions essential effective micro-hydro power planning. model evaluation results demonstrate excellent performance, with an Overall Accuracy 0.97 Kappa Coefficient 0.96, indicating strong agreement between predicted actual classifications. High Sensitivity, Specificity (0.99 all classes), User values (all above 0.95) underscore model’s ability correctly identify categories maintain consistency in positive negative predictions. Feature importance analysis highlights that certain spectral bands significantly enhance predictive capability, Band 3 playing crucial role (importance score 100), followed by Bands 7 6. These capture specific signatures associated different improving performance reliability. research contributes Sustainable Development Goals (SDGs), supporting SDG 6 (clean water sanitation), (affordable clean energy), 9 (industry, innovation, infrastructure), 13 (climate action), 15 (life on land) through improved resource management stewardship.

Language: Английский

Leveraging Machine Learning for Analyzing the Nexus Between Land Use and Land Cover Change, Land Surface Temperature And Biophysical Indices in an Eco-Sensitive Region of Brahmani-Dwarka Interfluve DOI Creative Commons
Bhaskar Mandal

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 102854 - 102854

Published: Sept. 1, 2024

Language: Английский

Citations

6
Analysis of the spatiotemporal dynamics of grassland carbon sinks in Xinjiang via the improved CASA model DOI Creative Commons

Xue‐Wei Liu,

Renping Zhang, Jing Guo

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 170, P. 113062 - 113062

Published: Jan. 1, 2025

Language: Английский

Citations

0
The effectiveness analysis of traditional and new landscape indexes in indicating flood risk of watersheds from the perspective of source-sink landscapes: A case study of Changsha, China DOI Creative Commons

Lingxuan Zhang,

Sheng Jiao, Jie Lü

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 170, P. 113109 - 113109

Published: Jan. 1, 2025

Language: Английский

Citations

0
An innovative framework to assess the human-water relationship in complex pluvial flooding system at urban meso-scales DOI

Chenlei Ye,

Weihong Liao, Zongxue Xu

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132876 - 132876

Published: Feb. 1, 2025

Language: Английский

Citations

0
The application of geographic information systems and remote sensing technologies in urban ecology DOI
Mir Muhammad Nizamani, Muhammad Awais, Muhammad Qayyum

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 137 - 163

Published: Jan. 1, 2025

Language: Английский

Citations

0
Landslide Hazard Prediction Based on Small Baseline Subset–Interferometric Synthetic-Aperture Radar Technology Combined with Land-Use Dynamic Change and Hydrological Conditions (Sichuan, China) DOI Creative Commons
Hongyi Guo, Antonio Miguel Martínez Graña

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(15), P. 2715 - 2715

Published: July 24, 2024

Le’an Town, located in the southwest of Qingchuan County, Guangyuan City, Sichuan Province, boasts a unique geographical position. The town’s terrain is complex, and its geological environment fragile. Multiple phases tectonic movements have resulted numerous cracks faults, making area prone to landslides, debris flows, other disasters. Additionally, heavy rainfall fluctuating groundwater levels further exacerbate instability mountains. Human activities, such as overdevelopment deforestation, significantly increased risk Currently, methods for landslide prediction Town are limited; traditional techniques cannot provide precise forecasts, study largely covered by tall vegetation. Therefore, this paper proposes method that combines SBAS-InSAR technology with dynamic changes land use hydrological conditions. used obtain surface deformation information, while land-use condition data incorporated analyze characteristics potential influencing factors areas. innovation lies high-precision monitoring capability integration multi-source data, which can more comprehensively reveal environmental area, thereby achieving accurate predictions development. results indicate annual subsidence rate most areas ranges from −10 0 mm, indicating slow subsidence. In some areas, exceeds −50 mm per year, showing significant slope aspect differences, reflecting combined effects structures, climatic conditions, human activities. It evident conditions impact on occurrence development landslides. utilizing cross-verifying it techniques, consistency identified be enhanced, improving results. This provides scientific basis early warning disasters has important practical application value.

Language: Английский

Citations

2
The past and future dynamics of ecological resilience and its spatial response analysis to natural and anthropogenic factors in Southwest China with typical Karst DOI Creative Commons
Shuang Song, Shaohan Wang, Yue Gong

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 19, 2024

With the global land use/land cover (LULC) and climate change, ecological resilience (ER) in typical Karst areas has become focus of attention. Its future development trend its spatial response to natural anthropogenic factors are crucial for understanding changes ecologically fragile human behavior. However, there is still a lack relevant quantitative research. The study systematically analyzed characteristics LULC Southwest China with over past 20 years. Drawing on landscape ecology research paradigm, potential-elasticity-stability ER assessment model was constructed. Revealing heterogeneity distribution, annual evolution, under different scenarios shared socioeconomic pathways representative concentration (SSP-RCP) future. In addition, econometric utilized reveal effect mechanism ER, adaptive strategies were proposed promote sustainable China. found that : (1) years, showed an accelerated change trend, decreased declined general, significant heterogeneity, showing distribution pattern "west larger than east, south north, reduction west slower east." (2) Under same SSP scenario, increase RCP emission concentration, area lowest-resilience increased significantly, highest-resilience decreased. (3) woodland largest contributor per unit China, grassland main type, which had prominent impact area. (4) average precipitation normalized difference vegetation index (NDVI) drivers area, economic growth, innovation, optimization industrial structure contributed Overall, integration multi-scenario-based modeling not only provides new perspectives mechanisms, but also valuable references other regions around world achieve development.

Language: Английский

Citations

1
Assessment of erosion, sediment yield, and runoff generating areas in Dirima catchment, upper Blue Nile, Tana Basin, Ethiopia DOI
Simir B. Atanaw, Fasikaw A. Zimale,

Tenalem Ayenew

et al.

Sustainable Water Resources Management, Journal Year: 2024, Volume and Issue: 11(1)

Published: Dec. 11, 2024

Language: Английский

Citations

1
Relationships among vegetation restoration, drought and hydropower generation in the karst and non-karst regions of Southwest China over the past two decades DOI
Xuyang Guo, Dongdong Liu, Jun Zeng

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 958, P. 177917 - 177917

Published: Dec. 10, 2024

Language: Английский

Citations

0
Developing Infiltration Model: Random Forest for Micro-Hydro Power Planning DOI Open Access

Annisa R. Varhana,

Widya Utama, Rista Fitri Indriani

et al.

IOP Conference Series Earth and Environmental Science, Journal Year: 2024, Volume and Issue: 1418(1), P. 012055 - 012055

Published: Dec. 1, 2024

Abstract The goal of this study is to determine the classification infiltration for Micro-Hydro Power Planning using Random Forest (RF) machine learning algorithm. Utilizing Landsat 8 satellite imagery, data provides a comprehensive basis analyzing various environmental factors relevant infiltration. RF algorithm models and classifies rates, ensuring precise reliable predictions essential effective micro-hydro power planning. model evaluation results demonstrate excellent performance, with an Overall Accuracy 0.97 Kappa Coefficient 0.96, indicating strong agreement between predicted actual classifications. High Sensitivity, Specificity (0.99 all classes), User values (all above 0.95) underscore model’s ability correctly identify categories maintain consistency in positive negative predictions. Feature importance analysis highlights that certain spectral bands significantly enhance predictive capability, Band 3 playing crucial role (importance score 100), followed by Bands 7 6. These capture specific signatures associated different improving performance reliability. research contributes Sustainable Development Goals (SDGs), supporting SDG 6 (clean water sanitation), (affordable clean energy), 9 (industry, innovation, infrastructure), 13 (climate action), 15 (life on land) through improved resource management stewardship.

Language: Английский

Citations

0