Machine learning models for gully erosion susceptibility assessment in the Tensift catchment, Haouz Plain, Morocco for sustainable development

Journal of African Earth Sciences, Journal Year: 2024, Volume and Issue: 213, P. 105229 - 105229

Published: March 11, 2024

Gully erosion is a widespread environmental danger, threatening global socio-economic stability and sustainable development. This study comprehensively applied seven machine learning (ML) models including SVM, KNN, RF, XGBoost, ANN, DT, LR, evaluated gully susceptibility in the Tensift catchment predict it within Haouz plain, Morocco. To ensure reliability of findings, employed robust combination inventory, sentinel images, Digital Surface Model. Eighteen predictors, encompassing topographical, geomorphological, environmental, hydrological factors, were selected after multicollinearity analyses. The revealed that approximately 28.18% at very high risk erosion. Furthermore, 15.13% 31.28% are categorized as low respectively. These findings extend to where 7.84% surface area highly risking erosion, while 18.25% 55.18% characterized areas. gauge performance ML models, an array metrics specificity, precision, sensitivity, accuracy employed. highlights XGBoost KNN most promising achieving AUC ROC values 0.96 0.93 test phase. remaining namely RF (AUC = 0.89), LR 0.80), SVM 0.81), DT 0.86), ANN 0.78), also displayed commendable performance. novelty this research its innovative approach combat through cutting edge offering practical solutions for watershed conservation, management, prevention land degradation. insights invaluable addressing challenges posed by region, beyond geographical boundaries can be used defining appropriate mitigation strategies local national scale.

Language: Английский

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Evaluation of future wetland changes under optimal scenarios and land degradation neutrality analysis in the Guangdong-Hong Kong-Macao Greater Bay Area

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 879, P. 163111 - 163111

Published: March 24, 2023

Language: Английский

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Towards land degradation neutrality: Does green energy and green human capital matter?

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 197, P. 114396 - 114396

Published: April 1, 2024

Language: Английский

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An advanced global soil erodibility (K) assessment including the effects of saturated hydraulic conductivity

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 908, P. 168249 - 168249

Published: Oct. 31, 2023

USLE-type models are widely used to estimate average annual soil loss at large scales, with the erodibility factor (K) being sole component that accounts for soil's susceptibility erosion. The includes information on permeability in equation, however, most definitions of K consider hydrological influence only very crudely and indirectly. Thus, direct impact surface runoff infiltration drainage erosion is largely neglected. objective this study incorporate hydraulic properties map by merging available global-scale measured saturated conductivity (Ksat) data texture organic carbon into a modified factor. To achieve this, Wischmeier Smith (1978) texture- permeability-based equation (KWischmeier factor) was include Ksat, called Kksat Using Random Forest machine learning algorithm, KWischmeier were each correlated remote sensing covariates spatial extrapolation two independent maps 1 km resolution. We noted clear decrease mean value (0.023 t ha h ha-1 MJ-1 mm-1) compared (0.027 mm-1). reduction values pronounced tropical regions reflecting difference (e.g., clay iron), whereas other climate showed relatively minor changes comparison as well recent global modeling Borrelli et al. (2017) (KGloSEM maps). As many studies discussed an overall overestimation (R)USLE based rates measurements, might improve modeled right direction. marks important initial step integrating can prove their significance future studies.

Language: Английский

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Ecological network design based on optimizing ecosystem services:case study in the Huang-Huai-Hai region, China

Ecological Indicators, Journal Year: 2023, Volume and Issue: 150, P. 110264 - 110264

Published: April 22, 2023

In modern agricultural landscapes, constructing 'ecological networks' is regarded as an efficient way to conserve biodiversity and maintain ecosystem services. Here we aimed develop approach design ecological corridor by employing the source - resistance surface framework in combination with semi-natural habitat planning service trade-off assessment. 'Ecological patches' were identified based on a 'Remote Sensing Ecological Index' (RSEI) objectively classify environmental conditions. Our resulting spatial was further modified used 'Cultivated Land Use Intensity' index, derive high accuracy rationality of extraction agriculture landscape. While network, key nodes (SNH) distribution using Linkage Mapper tools circuit theory. We constructed network scenarios different amounts habitats calculated regional values (ESV) equivalence factor method explore optimal layouts. The results showed, while generally increased line area contained within networks forests covering 10% total predicted scenario balancing services yield study region. Networks mixed forest grassland cover totaling 20% represented alternative choice that strongly enhanced may still allowing for productivity. corridors, identifying, restoring protecting targeted management restoration, existing wetlands other water bodies support cycle supply services, should be prioritized. Regional policy measures furthermore need promote help improve overall sustainability production.

Language: Английский

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Contribution and behavioral assessment of physical and anthropogenic factors for soil erosion using integrated deep learning and game theory

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 416, P. 137689 - 137689

Published: June 26, 2023

Language: Английский

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Dynamic simulation of the water-land-food nexus for the sustainable agricultural development in the North China Plain

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 912, P. 168771 - 168771

Published: Nov. 28, 2023

Language: Английский

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Assessment of land degradation risks in the Loess Plateau

Land Degradation and Development, Journal Year: 2024, Volume and Issue: 35(7), P. 2409 - 2424

Published: Feb. 29, 2024

Abstract Human activity and climate change are degrading the environmentally fragile Loess Plateau in dry semiarid regions. Land deterioration threatens human ecological existence. To prevent additional land degradation ensure development quality of arable region, China launched “Grain for Green” late 1990s. This effort greatly boosted vegetation. However, is complex, so we must also examine natural social variables to degradation. Thus, this study presents a comprehensive index system quantify on uses machine learning anticipate high‐risk locations. The project improved degradation, spatial distribution risk high northern low eastern southern regions Plateau. Gross Domestic Product population density main drivers Industrialization urbanization have raised which now accounts 1%–2% area. emphasizes sustainable management Plateau, critical area China. integrated assessment indicator random forest modeling help grasp current status future preventive measures. outcome advances research management. findings possess significant scientific reference value terms mitigating managing vulnerable worldwide.

Language: Английский

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Ecological Environment Dynamic Monitoring and Driving Force Analysis of Karst World Heritage Sites Based on Remote-Sensing: A Case Study of Shibing Karst

Land, Journal Year: 2023, Volume and Issue: 12(1), P. 184 - 184

Published: Jan. 6, 2023

The evaluation and monitoring of the ecological environment quality heritage sites can help provide sustainable healthy development strategies for management organizations. In this study, an model based on remote sensing index (RSEI) was used to measure Shibing Karst World Heritage Site its buffer zone Moran geographic probe were combined quantify environment. results show that, (1) from 2013 2020, site moderate high mean RSEI values in three periods studied 0.720, 0.723 0.742, showing overall upward improving trend; (2) grades good accounted more than 70% area, distribution pattern is significantly better at southwest northeast; (3) increased 0.600 0.661 residing first third quadrants, respectively, with spatial aggregation; (4) greenness humidity shown play a positive feedback role influence ability dryness greater. Overall, effective method evaluating sites, steady state improvement relevant departments conservation need further coordinate relationship between promote solutions other sites.

Language: Английский

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Assessing the extent of land degradation in the eThekwini municipality using land cover change and soil organic carbon

International Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: 45(4), P. 1339 - 1367

Published: Feb. 2, 2024

More than 75% of the global land has already suffered degradation, leading to recognition degradation as one foremost challenges society faces. This stems from its profound adverse impacts on natural ecosystem functioning, biodiversity, soil productivity, and food availability. Consequently, understanding spatial distribution across all scales becomes imperative. study employed cover change organic carbon (SOC) stock assessments analyse within eThekwini Municipality beyond baseline period (2000–2015). Utilizing remote sensing machine learning techniques, this research examined over spanning 2000 2022. Landsat 7 (Enhanced Thematic Mapper Plus – ETM+), 8 (Operational Land Imager 1 - OLI1), 9 2 OLI2) images were extract variables for both SOC prediction through XGBoost, LightGBM, Random Forest (RF), Support Vector Machine (SVM) models. Among these models, LightGBM demonstrates superior performance, achieving an overall accuracy 80.646 in predictions 77.869 predictions. Analysis unveiled a shift forests shrubland landscapes cropland built-up areas. results municipality encountering losses between 2015 The model predicted that most occur at 20–50 cm depth (9.27%), comparison 7.21% loss 0–20 depth. These findings underscore pivotal role aiding policymakers assess implement pertinent measures enhance landscape.

Language: Английский

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