Transformation of Geospatial Modelling of Soil Erosion Susceptibility Using Machine Learning DOI
Muhammad Ramdhan Olii,

Sartan Nento,

Nurhayati Doda

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 18, 2024

Abstract Soil erosion creates substantial environmental and economic challenges, especially in areas vulnerable to land degradation. This study investigates the use of machine learning (ML) techniques—namely Support Vector Machines (SVM) Generalized Linear Models (GLM)—for geospatial modeling soil susceptibility (SES). By leveraging data incorporating a range factors including hydrological, topographical, variables, research aims improve accuracy reliability SES predictions. Results show that SVM model predominantly identifies as having moderate (40.59%) or low (38.50%) susceptibility, whereas GLM allocates higher proportion very (24.55%) (38.59%) susceptibility. Both models exhibit high performance, with achieving accuracies 87.4% 87.2%, respectively, though slightly surpasses AUC (0.939 vs. 0.916). places greater emphasis on hydrological such distance rivers drainage density, while provides more balanced assessment across various variables. demonstrates ML-based can significantly enhance assessments, offering nuanced accurate approach than traditional methods. The findings highlight value adopting innovative, data-driven techniques offer practical insights for management conservation practices.

Language: Английский

Application of MCDA-GIS Methods for Soil Erosion Susceptibility Mapping in the Upper Blue Nile River Basin, Ethiopia DOI
Muralitharan Jothimani, Prafulla Kumar Panda,

Leulalem Shano

et al.

Environmental science and engineering, Journal Year: 2025, Volume and Issue: unknown, P. 115 - 144

Published: Jan. 1, 2025

Language: Английский

Citations

0
Application of Analytic Hierarchy Process in Mineral Prospecting Prediction Based on an Integrated Geology-Aerogeophysics-Geochemistry Model DOI Open Access
Yongzai Xi, Yongbo Li, Junjie Liu

et al.

Minerals, Journal Year: 2023, Volume and Issue: 13(7), P. 978 - 978

Published: July 23, 2023

Determining mineral prospecting targets is crucial for prediction and evaluation. In this study, an evaluation index system solid exploration metallogenic target assessment was established using the Analytic Hierarchy Process (AHP) Naoniushan area (China). Furthermore, integrated model combining geology–aerogeophysics–geochemistry developed copper, lead, zinc, silver, other polymetallic deposits. The information content of each in reasonably assigned, central southern parts Daxinganling were recommended. By focusing on copper area, paper demonstrates that AHP method can comprehensively consider various influencing factors their interactions, realize a reasonable division optimal target, reflect key affecting to certain extent. Importantly, approach reduces influence human subjective factors, optimization results are objective scientifically grounded.

Language: Английский

Citations

7
Erosion susceptibility mapping of a loess-covered region using Analytic Hierarchy Process – A case study: Kalat-e-Naderi, northeast Iran DOI Creative Commons

Fatemeh Nooshin Nokhandan,

Kaveh Ghahraman, Erzsébet Horváth

et al.

Hungarian Geographical Bulletin, Journal Year: 2024, Volume and Issue: 72(4), P. 339 - 364

Published: Jan. 12, 2024

In this study, the Analytic Hierarchy Process (AHP) is applied to generate erosion susceptibility maps in four basins of Kalat-e-Naderi county, namely Archangan, Kalat, Qaratigan, and Chahchaheh basins, situated northeast Iran. The region characterized by a partial coverage loess. Given agricultural significance loess its erosion, research focuses specifically on regions covered Geographic Information System (GIS) tools, including ArcMap Quantum (QGIS), were utilized facilitate creation maps. Seven factors, slope, aspect, elevation, drainage density, lithology, Normalized Difference Vegetation Index (NDVI), precipitation selected for consideration. Recognizing variability vegetation cover across different seasons, seasonal data specified factors employed. Consequently, generated basis. Pairwise comparison tables revealed that precipitation, slope emerged as dominant contributing region. resultant distinctly delineate with higher values, unresistant lithology (such loess, high porosity permeability), steeper slopes, exhibiting heightened (Archangan Kalat basins). credibility findings was examined through on-site observations. outcomes study may provide pertinent insights decision-makers planners. This information can be effectively employed formulating strategies aimed at conserving soil quality areas vulnerable hazards.

Language: Английский

Citations

2
Spatial modeling of soil erosion risk: a multi-criteria decision-making (MCDM) approach in the paguyaman watershed, gorontalo, Indonesia DOI
Muhammad Ramdhan Olii,

Abdul Kadir Zailani Olii,

Aleks Olii

et al.

Arabian Journal of Geosciences, Journal Year: 2024, Volume and Issue: 17(7)

Published: July 1, 2024

Language: Английский

Citations

2
Hazards profile of the Shigar Valley, Central Karakoram, Pakistan: Multicriteria hazard susceptibility assessment DOI Creative Commons

Munazza Afreen,

Fazlul Haq, Bryan G. Mark

et al.

AUC GEOGRAPHICA, Journal Year: 2024, Volume and Issue: 59(1), P. 77 - 92

Published: May 21, 2024

The rapid deglaciation in the Upper Indus Basin (UIB) significantly impacts local landscapes, watersheds, and basin-wide hydrology. While creating new opportunities, such as emerging landscapes hydrological changes, simultaneously heightens risk of glacio-hydrological hazards adjacent downstream regions. With limited available land for agriculture settlements, communities around glaciers expand human activities toward newly formed floodplains deglaciating valleys, necessitating a comprehensive understanding associated risks vulnerabilities. This study employs Geographical Information System (GIS) Remote Sensing products multicriteria susceptibility assessment Shigar Valley, located major Himalayan – Baltoro (63 km) Biafo (67 glaciers. research reveals that 28.3% valley is highly susceptible to multiple hazards, emphasizing urgency informed decision-making region. Only 0.03% area lies very low category, 9.7% susceptible, 60.6% moderately 1.04% categories. These findings highlight need proactive measures, adaptive strategies, sustainable development Valley mitigate escalating posed by changing patterns.

Language: Английский

Citations

1
How Effective Are Palm-Fiber-Based Erosion Control Blankets (ECB) against Natural Rainfall? DOI Open Access
Mohamad Jahja,

Ali Mudatstsir,

Idawati Supu

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(4), P. 1655 - 1655

Published: Feb. 17, 2024

Rainfall-induced soil erosion is a significant environmental issue that can lead to degradation and loss of vegetation. The estimated global annual increased by 2.5% over 11 years, from 35 billion tons in 2001 35.9 2012, mainly due spatial changes. Indonesia predicted be among the largest most intensively eroded regions countries with higher erosion, regarded as hot-spots than 20 Mg yr−1 ha−1. Due climate change, natural rainfall patterns tropical have been subject lower number rainy days intensity precipitation. Such changes trigger more heavier kicking up dried particles are exposed bare embankments. Unfortunately, there no prevention available developing lack availability high prices mitigation techniques such terraces covering areas geotextiles or blankets. Erosion control blankets (ECBs) emerged potential solution mitigate erosion. This research article aims evaluate effectiveness sugar-palm-fiber-based ECB reducing caused rainfall. study investigates sugar-palm-based protecting against at designated embankment. During three months typical seasons (February April 2023), total mass (kg) was collected measured two adjacent microplots (10 m2 each), one covered other left uncovered (bare soil). results indicate proportional rainfall, coefficients 0.4 0.04 for ECB-covered embankments, respectively. recorded during monitoring period 154.6 kg 16.7 soil, significantly efficiency 90% reduction losses achieved slope ECB. reason this may attributed intrinsic surface properties sugar palm fiber ropes characteristics plot area. Sugar (Arenga pinnata) has lignocellulosic contents produce perfect combination strong mechanical (higher tensile strength young modulus) resistance weathering processes. Although cost production handmade now 4 EUR, further reductions introducing machinery. Compared ECBs which smaller openings, larger openings allow vegetation grow provide it density. As such, we recommend improvements quality palm-fiber-based via introduction automation process, so price reduced line commercially fibers jute coir.

Language: Английский

Citations

0
Spatio-Temporal Analysis of Erosion Risk Assessment Using GIS-Based AHP Method: A Case Study of Doğancı Dam Watershed in Bursa (Türkiye) DOI Open Access
Esin Erdoğan Yüksel, Ömer Faruk Karan, Abdullah E. Akay

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(7), P. 1135 - 1135

Published: June 29, 2024

Soil erosion, one of the most serious phenomena in watershed management, can be estimated based on various criteria. Land use change is important factors affecting susceptibility soil erosion. In this study, effect land erosion risk two plan periods (2005 and 2017) was investigated using Analytical Hierarchy Process (AHP) Geographic Information Systems (GIS) for forest planning units Doğancı Dam Watershed, located Bursa, Türkiye. Eight criteria were evaluated including erosion-related slope, bedrock type, use/land cover, precipitation, relative relief, aspect, drainage frequency, density. According to results, effective factor slope (0.29), while type cover ranked second with 0.19. It found that full closure forests characterized by high resistance (0.3), bare as sensitive area (0.39). terms spatio-temporal changes a 12-year period, areas medium decreased, low very low-risk increased. The ROC method showed satisfactory accuracy 72.8% 80.2% 2005 2017 maps, respectively.

Language: Английский

Citations

0
Transformation of Geospatial Modelling of Soil Erosion Susceptibility Using Machine Learning DOI
Muhammad Ramdhan Olii,

Sartan Nento,

Nurhayati Doda

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 18, 2024

Abstract Soil erosion creates substantial environmental and economic challenges, especially in areas vulnerable to land degradation. This study investigates the use of machine learning (ML) techniques—namely Support Vector Machines (SVM) Generalized Linear Models (GLM)—for geospatial modeling soil susceptibility (SES). By leveraging data incorporating a range factors including hydrological, topographical, variables, research aims improve accuracy reliability SES predictions. Results show that SVM model predominantly identifies as having moderate (40.59%) or low (38.50%) susceptibility, whereas GLM allocates higher proportion very (24.55%) (38.59%) susceptibility. Both models exhibit high performance, with achieving accuracies 87.4% 87.2%, respectively, though slightly surpasses AUC (0.939 vs. 0.916). places greater emphasis on hydrological such distance rivers drainage density, while provides more balanced assessment across various variables. demonstrates ML-based can significantly enhance assessments, offering nuanced accurate approach than traditional methods. The findings highlight value adopting innovative, data-driven techniques offer practical insights for management conservation practices.

Language: Английский

Citations

0