Sustainable landslide mitigation requires appropriate approaches to predict susceptible zones. This study compared the performance of Logistic Model Tree (LMT), Random Forest (RF) and Naïve-Bayes Tree (NBT) in predicting landslide susceptibility for the upper Nyabarongo catchment (Rwanda). 196 past landslides were mapped using field investigations. Thus, the inventory map was split into training and testing datasets. Fifteen predisposing factors were analysed and information gain (IG) technique was used to analyse the correlation between factors and observed landslides. Therefore, the area under receiver operating characteristic (AUROC) with other statistical estimators including accuracy, precision, and root mean square error (RMSE) were employed to compare the models. The AUC values were 78.7%, 80.9% and 82.4% for RF, LMT and NBT models, respectively. Additionally, the NBT produced the highest accuracy and precision values (0.799 and 0.745, respectively). Regarding RMSE values, the NBT model achieved an optimized prediction than RF and LMT models (0.301; 0.428 and 0.364, respectively). The results of the current study may inform further studies and appropriate landslide risk reduction and mitigation measures. They can also be instrumental for policy and decision making in regards with natural risk management.

Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment / Nsengiyumva, J. B.; Valentino, R.. - In: GEOMATICS, NATURAL HAZARDS & RISK. - ISSN 1947-5705. - 11:1(2020), pp. 1250-1277. [10.1080/19475705.2020.1785555]

Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment

Valentino R.
2020

Abstract

Sustainable landslide mitigation requires appropriate approaches to predict susceptible zones. This study compared the performance of Logistic Model Tree (LMT), Random Forest (RF) and Naïve-Bayes Tree (NBT) in predicting landslide susceptibility for the upper Nyabarongo catchment (Rwanda). 196 past landslides were mapped using field investigations. Thus, the inventory map was split into training and testing datasets. Fifteen predisposing factors were analysed and information gain (IG) technique was used to analyse the correlation between factors and observed landslides. Therefore, the area under receiver operating characteristic (AUROC) with other statistical estimators including accuracy, precision, and root mean square error (RMSE) were employed to compare the models. The AUC values were 78.7%, 80.9% and 82.4% for RF, LMT and NBT models, respectively. Additionally, the NBT produced the highest accuracy and precision values (0.799 and 0.745, respectively). Regarding RMSE values, the NBT model achieved an optimized prediction than RF and LMT models (0.301; 0.428 and 0.364, respectively). The results of the current study may inform further studies and appropriate landslide risk reduction and mitigation measures. They can also be instrumental for policy and decision making in regards with natural risk management.
Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment / Nsengiyumva, J. B.; Valentino, R.. - In: GEOMATICS, NATURAL HAZARDS & RISK. - ISSN 1947-5705. - 11:1(2020), pp. 1250-1277. [10.1080/19475705.2020.1785555]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2881774
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