An Evaluation of Landslide Susceptibility Mapping Using Remote Sensing Data and Machine Learning Algorithms in Iran

Abstract

<p><strong>Abstract.</strong> Landslide is painstaking as one of the most prevalent and devastating forms of mass movement that affects man and his environment. The specific objective of this research paper is to investigate the application and performances of some selected machine learning algorithms (MLA) in landslide susceptibility mapping, in Dodangeh watershed, Iran. A 112 sample point of the past landslide, occurrence or inventory data was generated from the existing and field observations. In addition, fourteen landslide-conditioning parameters were derived from DEM and other topographic databases for the modelling process. These conditioning parameters include total curvature, profile curvature, plan curvature, slope, aspect, altitude, topographic wetness index (TWI), topographic roughness index (TRI), stream transport index (STI), stream power index (SPI), lithology, land use, distance to stream, distance to the fault. Meanwhile, factor analysis was employed to optimize the landslide conditioning parameters and the inventory data, by assessing the multi-collinearity effects and outlier detections respectively. The inventory data is divided into 70% (78) training dataset and 30% (34) test dataset for model validation. The receiver operating characteristics (ROC) curve or area under curve (AUC) value was used for assessing the model's performance. The findings reveal that TRI has 0.89 collinearity effect based on variance-inflated factor (VIF) and based on Gini factor optimization total curvature is not significant in the model development, therefore the two parameters are excluded from the modelling. All the selected MLAs (RF, BRT, and DT) shown promising performances on landslide susceptibility mapping in Dodangeh watershed, Iran. The ROC curve for training and validation for RF are 86% success rate and 83% prediction rate implies the best model performance compared to BRT and DT, with ROC curve of 72% and 70% prediction rate, respectively. In conclusion, RF could be the best algorithm for producing landslide susceptibility map, and such results could be adopted for the decision-making process to support land use planner for improving landslide risk assessment in similar environmental settings.</p>

Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms

“…Moreover, there were strong correlation between profile curvature and curvature, and curvature and plan curvature with a value of 0.82 and 0.80, respectively. In this case, the simple method to overcome with high Pearson correlation between conditioning factor is to remove one of the factors from the dataset and rebuild analtysis 1 . On the other hand, factor optimization using Chi-Square indicated that higher Chi-square values with p-value less than 0.05 ranks the significance of each factor for landslide prediction.…”

“…Whereas, TRI indicates the variation in the elevation of neighboring cells in a digital elevation cells network. We classified the SPI, TWI, TRI (Figure 1h,i,j) based on 5 classes as per reference 1 , which further definitions are available there.…”

“…Natural disasters such as landslides or mass movements have recently been reflecting important attention around the globe. That is due to their destructive impacts on economy, social and environment particularly in urbanized area 1 . Moreover, a large number of landside events and their consequences have been boosted due to urban sprawls in risky areas 2 .…”

Geospatial Multicriteria Analysis for Earthquake Risk Assessment: Case Study of Fujairah City in the UAE

“…The rules are worked out based on the structure of the data. The tree starts with a highly influential attribute as the root node and successive rules are applied to move to the next attributes until a leaf node or terminal node is reached [60,61]. The data set is split repeatedly from the coarsest attributes to the finest attributes.…”