WITHDRAWN: Coupling of analytical hierarchy process and frequency ratio based spatial prediction of soil erosion susceptibility in Keleghai river basin, India

Sar, Nityananda; Khan, Ansar; Chatterjee, Soumendu; Das, Arosikha; Mipun, Bijay Singh

doi:10.1016/j.iswcr.2016.09.004

Cited by 15 publications

(5 citation statements)

References 64 publications

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“…1). Drainage network affects the slope stability and instability which can in uence the soil erosion and degradation processes [40]. The distance from the drainage network has been selected as an effective factor of gully erosion, assuming that the areas near existing streams are more sensitive to gully occurrence and development.…”

Section: Datasetmentioning

confidence: 99%

Gully Erosion Susceptibility Assessment Using Different Machine Learning Algorithms: A Case Study of Shazand Watershed in Iran

Mohammady

Davudirad

2023

Environ Model Assess

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Soil, as a valuable natural resource, provides a large number of services and plays an important role in the environment and world economy. Soil degradation and erosion reduce the quality and quantity the soil and are important natural and anthropogenic processes that affect many countries. Water erosion is the most common type of soil degradation in the world and Asia has about fty percent of the total water erosion area of the world. Gullies are a typical erosion type and gully formation is an important process of soil erosion and degradation in semi-arid and arid areas, especially areas impacted by human activities and land uses. Because of arid and semi-arid climate, piping and gully erosion is an active phenomenon in the agricultural lands, bare land and rangeland areas of the Shazand watershed, Markazi Province, central Iran. The goal of this research was to identify the priority conditioning factors of gully erosion, map the susceptibility of the Shazand watershed to gully erosion and compare some of the applied machine learning techniques based on their accuracy. Prioritization of conditioning factors using a random forest (RF) algorithm demonstrated that distance from the roads, altitude, and rainfall have the greatest impact on gully occurrence in the Shazand watershed. The RF, boosted regression tree (BRT), functional discriminant analysis (FDA), generalized linear model (GLM), and mixture discriminant analysis (MDA) algorithms were applied to create gully erosion susceptibility maps in the study area. The receiver operating characteristic curve (ROC) and area under the curve (AUC) performance metrics were used to validate susceptibility maps. The AUC values of 0.850, 0.831, 0.760, 0.751, and 0.758 were achieved for the RF, BRT, FDA, GLM, and MDA algorithms, respectively. Due to the negative and destructive effects of gully erosion, its management and control is a critical component in the management of natural resources and land uses. The susceptibility maps of gully erosion prepared in this study are a substantial information resource for decision makers, planners, and engineers concerned with human impacts on natural resources and land uses. The areas identi ed with high and very high erosion susceptibility in the Shazand watershed need more care to mitigate the consequences of gully erosion and soil degradation.

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Section: Datasetmentioning

confidence: 99%

Gully Erosion Susceptibility Assessment Using Different Machine Learning Algorithms: A Case Study of Shazand Watershed in Iran

Mohammady

Davudirad

2023

Environ Model Assess

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“…Where is the determinant coe cient of the regression for predisposing factor, k, on all the other predisposing factors 41 .…”

Section: Landslide Predisposing Factor Analysismentioning

confidence: 99%

Assessment of the earthquake-triggered landslide susceptibility using machine learning and grey wolf optimizer (GWO): A case study of Jiuzhaigou

Wei

Gou

et al. 2022

Preprint

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Landslide susceptibility assessment plays a critical role in disaster management and post-disaster planning. Machine learning-based approaches have recently attracted a lot of attention. However, the parameters tuning in this category of methods has not been accurately determined and is even considered as a weak point. The main objective of this study is to develop two machine learning-based landslide susceptibility models that optimized using a metaheuristic optimization algorithm, the grey wolf optimizer (GWO), for assessing the probability of landslide occurrence without artificial tuning. The selected machine learning algorithm were random forests (RF) and support vector machines (SVM). We apply the optimized models to Jiuzhaigou County on the eastern margin of Qinghai-Tibet Plateau. A total of 270 earthquake-triggered landslides were identified by remote sensing interpretation and filed surveys. Sixteen predisposing factors involving geology, human activity, and hydrology were extracted from the available materials. Then thirteen factors suitable for the study area were selected using multicollinearity diagnosis methods. Two meta-optimization models, GWO-RF, GWO-SVM, were con-structed after GWO's automated search for model parameters. Finally, the Receiver Operating Characteristic (ROC) curve and related statistics, including Accuracy, Sensitivity, and Specificity, were chosen to evaluate and compare the performance of the optimized landslide susceptibility models. Both models were constructed with ROCs higher than 0.95 on the training dataset and validation dataset as well as high accuracy. GWO-RF obtained the best both of accuracy and AUC values of 0.9198 and 0.972 on the validation dataset, respectively. Furthermore, we performed a weighting analysis of the factors and speculated on the relationship between the raw data distribution and accuracy. The results of this study show that the construction of the landslide susceptibility model optimized using a metaheuristic optimization algorithm is a feasible approach.

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“…(1) are selected as the hydrological factors for this landslide susceptibility analysis. Proximity to the hydrological features is an important factor when considering the landslide susceptibility analyses (Sar et al, 2016;Shahabi and Hashim, 2015). TWI is a solid index that is capable of predicting areas susceptible to saturation or wetness of land surfaces, as well as the areas that have the potential to produce an overland flow.…”

Section: Hydrological Factorsmentioning

confidence: 99%

Efficacy of using radar-derived factors in landslide susceptibility analysis: case study of Koslanda, Sri Lanka

Ranasinghe

Bandara

Puswewala

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. Through the recent technological developments of radar and optical remote sensing in (i) the areas of temporal, spectral, spatial, and global coverage; (ii) the availability of such images either at a low cost or free of charge; and (iii) the advancement of tools developed in image analysis techniques and GIS for spatial data analysis, there is a vast potential for landslide studies using remote sensing and GIS as tools. Hence, this study aimed to assess the efficacy of using radar-derived factors (RDFs) in identifying landslide susceptibility using the bivariate information value method (InfoVal method) and the multivariate multi-criteria decision analysis based on the analytic hierarchy process statistical analysis. Using identified landslide causative factors, four landslide prediction models – bivariate with and without RDFs as well as multivariate with and without RDFs – were generated. Twelve factors such as topographical, hydrological, geological, land cover and soil plus three RDFs are considered. The weight of index for landslide susceptibility is calculated by using the landslide failure map, and susceptibility regions are categorized into four classes as very low, low, moderate, and high susceptibility to landslides. With the integration of RDFs, boundary detection between high- and very-low-susceptibility regions are increased by 7 % and 4 % respectively.

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WITHDRAWN: Coupling of analytical hierarchy process and frequency ratio based spatial prediction of soil erosion susceptibility in Keleghai river basin, India

Cited by 15 publications

References 64 publications

Gully Erosion Susceptibility Assessment Using Different Machine Learning Algorithms: A Case Study of Shazand Watershed in Iran

Gully Erosion Susceptibility Assessment Using Different Machine Learning Algorithms: A Case Study of Shazand Watershed in Iran

Assessment of the earthquake-triggered landslide susceptibility using machine learning and grey wolf optimizer (GWO): A case study of Jiuzhaigou

Efficacy of using radar-derived factors in landslide susceptibility analysis: case study of Koslanda, Sri Lanka

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