Prediction of Spatial Likelihood of Shallow Landslide Using GIS-Based Machine Learning in Awgu, Southeast/Nigeria

Abstract

A landslide is a typical geomorphological phenomenon associated with the regular cycles of erosion in tropical climates occurring in hilly and mountainous terrain. Awgu, Southeast Nigeria, has suffered a severe landslide disaster, and no one has studied the landslide susceptibility in the study area using an advanced model. This study evaluated and compared the application of three machine learning algorithms, namely, extreme gradient boosting (Xgboost), Random Forest (RF), and Naïve Bayes (NB), for a landslide susceptibility assessment in Awgu, Southeast Nigeria. A hazard assessment was conducted through a field investigation, remote sensing, and a consultation of past literature reviews, and 56 previous landslide locations were prepared from various data sources. A total of 10 conditioning factors were extracted from various databases and converted into a raster. Before modeling the landslide susceptibility, the information gain ratio (IGR) was used to select and quantitatively describe the predictive ability of the conditioning factors. The Pearson correlation coefficient was used to judge the correlation between 10 conditioning factors. In this study, rainfall is the most significant factor with respect to landslide distribution and occurrence. The confusion matrix, the area under the receiver operating characteristic curve (AUROC), was used to validate and compare the models. According to the AUROC results, the prediction accuracy for the RF, NB, and XGBOOST models are 0.918, 0.916, and 0.902, respectively. This current study can support the landslide susceptibility assessment of Awgu, Southeast Nigeria, and can provide a reference for other areas with the same conditions.

Impact of Parameter Tuning with Genetic Algorithm, Particle Swarm Optimization, and Bat Algorithm on Accuracy of the SVM Model in Landslide Susceptibility Evaluation

“…In the susceptibility evaluation of geological hazards, conditional factors represent the environmental information of a specifc location. Hence, it is necessary to check the collinearity of factors in the landslide sensitivity modeling process to avoid poor prediction performance [22]. Tis article used the Pearson correlation coefcient to calculate the conditional factors and obtain the Pearson correlation of conditional factors (Table 1).…”

Research on the assessment of landslide risk on highways based on remote sensing data and Xgboost algorithm

“…In addition, some scholars have also used other machine learning algorithms such as the Naive Bayes algorithm, Decision Tree algorithm, and Random Forest algorithm to establish landslide susceptibility models. For example, Lee et al (2020) used the Naive Bayes algorithm to predict landslide risk in a region of Korea [4][5][6][7][8][9][10][11][12][13][14].…”

Incorporating Rainfall Forecast Data in X-SLIP Platform to Predict the Triggering of Rainfall-Induced Shallow Landslides in Real Time

“…Most regional LEWS provide space-time information via data-driven methods, e.g., based on rainfall thresholds (empirical relationships between rainfall and landslide occur-rence derived from past events) [25][26][27][28] or on artificial intelligence [29][30][31][32][33]. The mentioned methods are applied for real-time predictions thanks to the rain forecast.…”