Rainfall-induced landslide susceptibility assessment using random forest weight at basin scale

Lai, Chengguang; Chen, Xiaohong; Xu, Chong‐Yu; Yang, Bing

doi:10.2166/nh.2017.044

Cited by 18 publications

(7 citation statements)

References 42 publications

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“…Chen et al indicates that the MARS and RF models are good estimators for mapping [36]. Lai et al indicated that the RF model has significant potential for weight determination on landslide modelling [62]. Kuhnert et al stated that RF with AUC = 97.0 is suitable for landslide susceptibility [27].…”

Section: Discussionmentioning

confidence: 99%

Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

et al. 2018

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Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.

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Section: Discussionmentioning

confidence: 99%

Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

et al. 2018

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“…In the classification, a novel hierarchical object-based Random Forest classification approach can be used to distinguish different land cover types, which have accuracy rates over 90% [ 44 ]. RF could also establish a basin hydrological evaluation model by determining the weight of the index, and the accuracy rate is higher than the entropy weight method [ 45 ]. In addition, RF in hydrological data prediction has a strong advantage, and its prediction results are better than Poisson regression [ 46 ], which could also effectively divide flood-prone areas [ 47 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

Assessing and Predicting the Water Resources Vulnerability under Various Climate-Change Scenarios: A Case Study of Huang-Huai-Hai River Basin, China

Chen

Feng

Zhang

et al. 2020

Entropy

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The Huang-Huai-Hai River Basin plays an important strategic role in China’s economic development, but severe water resources problems restrict the development of the three basins. Most of the existing research is focused on the trends of single hydrological and meteorological indicators. However, there is a lack of research on the cause analysis and scenario prediction of water resources vulnerability (WRV) in the three basins, which is the very important foundation for the management of water resources. First of all, based on the analysis of the causes of water resources vulnerability, this article set up the evaluation index system of water resource vulnerability from three aspects: water quantity, water quality and disaster. Then, we use the Improved Blind Deletion Rough Set (IBDRS) method to reduce the dimension of the index system, and we reduce the original 24 indexes to 12 evaluation indexes. Third, by comparing the accuracy of random forest (RF) and artificial neural network (ANN) models, we use the RF model with high fitting accuracy as the evaluation and prediction model. Finally, we use 12 evaluation indexes and an RF model to analyze the trend and causes of water resources vulnerability in three basins during 2000–2015, and further predict the scenarios in 2020 and 2030. The results show that the vulnerability level of water resources in the three basins has been improved during 2000–2015, and the three river basins should follow the development of scenario 1 to ensure the safety of water resources. The research proved that the combination of IBDRS and an RF model is a very effective method to evaluate and forecast the vulnerability of water resources in the Huang-Huai-Hai River Basin.

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“…Elevation (EL, m): Most landslides occur in mountainous areas with a large drop with elevation reflecting characteristics of a discontinuous terrain [80][81][82] Slope angle (SL, degree): Slope angle is frequently applied as an index reflecting the degree of topographic change in landslide susceptibility studies as landslides are directly related to slope angle [78][79][80][81][82]. SL was generated by DEM using the "Slope" tool of Arc.GIS9.3 and it meets (Degree of slope = θ, tan θ = rise/run).…”

Section: Data and Pre-processingmentioning

confidence: 99%

GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China

Wang

Bai

et al. 2018

Water

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Landslide susceptibility assessment is presently considered an effective tool for landslide warning and forecasting. Under the assessment procedure, a credible index weight can greatly increase the rationality of the assessment result. Using the Beijiang River Basin, China, as a case study, this paper proposes a new weight-determining method based on random forest (RF) and used the weighted linear combination (WLC) to evaluate the landslide susceptibility. The RF weight and eight indices were used to construct the assessment model. As a comparison, the entropy weight (EW) and weight determined by analytic hierarchy process (AHP) were also used, respectively, to demonstrate the rationality of the proposed weight-determining method. The results show that: (1) the average error rates of training and testing based on RF are 18.12% and 15.83%, respectively, suggesting that the RF model can be considered rational and credible; (2) RF ranks the indices elevation (EL), slope (SL), maximum one-day precipitation (M1DP) and distance to fault (DF) as the Top 4 most important of the eight indices, occupying 73.24% of the total, while the indices runoff coefficient (RC), normalized difference vegetation index (NDVI), shear resistance capacity (SRC) and available water capacity (AWC) are less consequential, with an index importance degree of only 26.76% of the total; and (3) the verification of landslide susceptibility indicates that the accuracy rate based on the RF weight reaches 75.41% but are only 59.02% and 72.13% for the other two weights (EW and AHP), respectively. This paper shows the potential to provide a new weight-determining method for landslide susceptibility assessment. Evaluation results are expected to provide a reference for landslide management, prevention and reduction in the studied basin.

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Rainfall-induced landslide susceptibility assessment using random forest weight at basin scale

Cited by 18 publications

References 42 publications

Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

Assessing and Predicting the Water Resources Vulnerability under Various Climate-Change Scenarios: A Case Study of Huang-Huai-Hai River Basin, China

GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China

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