Comparing potential risk of soil erosion using RUSLE and MCDA techniques in Central Ethiopia

Tadesse, Teshome Betru; Tefera, Setiye Abebaw

doi:10.1007/s40808-020-00881-z

Cited by 16 publications

(4 citation statements)

References 33 publications

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“…Increasing the slope length, also increases erosion rates due to flow conditions (flow velocity, volume, and shear stress). These findings are consistent with studies by Atoma et al (2020) and Tadesse and Tefera (2020).…”

Section: Driving Factors Of Soil Erosionsupporting

confidence: 93%

A New Approach For Smart Soil Erosion Modelling: Integration of Empirical And Machine Learning Models

Avand

Mohammadi

Mirchooli

et al. 2021

Preprint

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Despite advances in artificial intelligence modelling, the lack of soil erosion data and other watershed information is still one of the important factors limiting soil-erosion modelling. Additionally, the limited number of parameters and the lack of evaluation criteria are major disadvantages of empirical soil-erosion models. To overcome these limitations, we introduce a new approach that integrates empirical and artificial intelligence models. Erosion-prone locations (erosion ≥16 tons/ha/year) are identified using RUSLE model and a soil-erosion map is prepared using random forest (RF), artificial neural network (ANN), classification tree analysis (CTA), and generalized linear model (GLM). This study uses 13 factors affecting soil erosion in the Talar watershed, Iran, to increase prediction accuracy. The results reveal that the RF model has the highest prediction performance (AUC=0.95, Kappa=0.87, Accuracy=0.93, and Bias=0.88), outperforming the three machine-learning models. The results show that slope angle, land use/land cover, elevation, and rainfall erosivity are the factors that contribute the most to soil erosion propensity in the watershed. Curvature and topography position index (TPI) were removed from the analysis due to multicollinearity with other factors. The results can be used to improve the identification of hot spots of soil erosion, especially in watersheds for which soil-erosion data are limited.

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Section: Driving Factors Of Soil Erosionsupporting

confidence: 93%

A New Approach For Smart Soil Erosion Modelling: Integration of Empirical And Machine Learning Models

Avand

Mohammadi

Mirchooli

et al. 2021

Preprint

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“…(6) developed by Hurni [ 76 ], considering the Ethiopian highland arid and semi-arid region where rainfall is concentrated in a few months while the rest of the year experience deficient rainfall. Previously the equation has been used by several studies in Ethiopia [ 18 , 25 , 35 , 77 ].

…”

Section: Methods and Datasetsmentioning

confidence: 99%

Soil erosion assessment and identification of erosion hotspot areas in the upper Tekeze Basin, Northern Ethiopia

Fentaw,

Abegaz

2024

Heliyon

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“…Multi-Criteria Decision Analysis (MCDA) is a fundamental knowledge-driven method, recognized as an essential tool for environmental decision-making, enabling the visualization and resolution of competitive decision problems [36][37][38][39]. By integrating qualitative and quantitative criteria, MCDA has become a cornerstone in integrated problem-solving solutions [40].…”

Section: Introductionmentioning

confidence: 99%

Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China

Huang,

Wu,

et al. 2023

Remote Sensing

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Geohazards pose significant risks to communities and infrastructure, emphasizing the need for accurate susceptibility assessments to guide land-use planning and hazard management. This study presents a comprehensive method that combines Variable Weight Theory (VWT) with Analytic Hierarchy Process (AHP) to assess geo-environment vulnerability based on susceptibility to various geohazards. The method was applied to the Pearl River Delta in China, resulting in the classification of areas into high vulnerability (5961.85 km2), medium vulnerability (19,227.93 km2), low vulnerability (14,892.02 km2), and stable areas (1616.19 km2). The findings demonstrate improved accuracy and reliability compared to using AHP alone. ROC curve analysis confirms the enhanced performance of the integrated method, highlighting its effectiveness in discerning susceptibility levels and making informed decisions in hazard preparedness and risk reduction. Additionally, this study assessed the risks posed by geohazards to critical infrastructures, roads, and artificial surfaces, while discussing prevention strategies. However, this study acknowledges certain limitations, including the subjective determination of its judgment matrix and data constraints. Future research could explore the integration of alternative methods to enhance the objectivity of factor weighting. In practical applications, this study contributes to the understanding of geo-environment vulnerability assessments, providing insight into the intricate interplay among geological processes, human activities, and disaster resilience.

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Comparing potential risk of soil erosion using RUSLE and MCDA techniques in Central Ethiopia

Cited by 16 publications

References 33 publications

A New Approach For Smart Soil Erosion Modelling: Integration of Empirical And Machine Learning Models

A New Approach For Smart Soil Erosion Modelling: Integration of Empirical And Machine Learning Models

Soil erosion assessment and identification of erosion hotspot areas in the upper Tekeze Basin, Northern Ethiopia

Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China

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