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

Uzodigwe Emmanuel Nnanwuba, Shengwu Qin (), Oluwafemi Adewole Adeyeye, Ndichie Chinemelu Cosmas, Jingyu Yao, Shuangshuang Qiao, Sun Jingbo and Ekene Mathew Egwuonwu
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Uzodigwe Emmanuel Nnanwuba: College of Construction Engineering, Jilin University, Changchun 130026, China
Shengwu Qin: College of Construction Engineering, Jilin University, Changchun 130026, China
Oluwafemi Adewole Adeyeye: College of New Energy and Environment, Jilin University, Changchun 130021, China
Ndichie Chinemelu Cosmas: Department of Geography, University of Nigeria, Nsukka 410001, Nigeria
Jingyu Yao: College of Construction Engineering, Jilin University, Changchun 130026, China
Shuangshuang Qiao: College of Construction Engineering, Jilin University, Changchun 130026, China
Sun Jingbo: College of Construction Engineering, Jilin University, Changchun 130026, China
Ekene Mathew Egwuonwu: College of Construction Engineering, Jilin University, Changchun 130026, China

Sustainability, 2022, vol. 14, issue 19, 1-20

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.

Keywords: landslide susceptibility; Awgu; Southeast Nigeria; random forest; extreme gradient boosting; Naïve Bayes; validation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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