## 6 Discussion  
Determining the LCFs, generation of LSMs and selection of the best-fitted model is the initial stage of landslide hazard assessment and the present research was intended to attempt that. As the landslide is the uncertainty of spatial association among the several factors, different models can be applied for the prediction. During the last decades, various empirical and numerical models have been attempted to forecast geohazards [14; 79; 82]. However, those models have some limitations and assumptions [15; 24]. Recently, data mining models have been largely popularised in the research community, especially in modelling different environmental risks due to their ability to analyse complex connection between predictors (LCFs) and responses (landslide or non-landslide). Besides this, the ensemble of different data mining models along with statistical models also has been used effectively to produce LSMs [13; 63; 83]. This work was carried out through four phases of ensemble approaches that made the work novel compared to other works. In resolving the over-fitting problem, an ensemble of these machine learning models has more capability because these models automate the task of selecting multiple datasets without pre-assumption and can handle massive data [77]. Furthermore, the evaluation of the prediction ability of the models is a fundamental phase in any modelling work and it could be judged using both threshold dependent and independent methods [14; 83]. Therefore, the AUC, precision, efficiency, MAE, and RMSE of each modelling approach were assessed considering both the training sub-set GLIs and testing or validation sub-set GLIs. Measurement of AUC including training sub-set represents success rate while the prediction rate was computed using testing sub-set of GLIs [14; 73]. Considering the training sub-set GLIs, in the individual modelling, the AUC was found highest (85.12%) in ANN model followed by RF (84.59%), SVM (83.39%) and LR (82.17%) model (Table 4). In terms of precision and efficiency values, ANN leads the highest performance followed by SVM, RF and LR model (Table 4). However, both the reliability measures MAE and RMSE showed best results for RF model followed by ANN, SVM and LR model (Table 4). LR-RF showed the best result in terms of precision and efficiency when two models were combined and all the two-model ensembles have shown greater accuracy than the individual model in term of AUC (Figure 10). When the three models were combined, ANN-LR-RF revealed the best result under each measure of precision (0.871), efficiency (0.878), AUC (87.83%), MAE (0.016) and RMSE (0.019) (Table 4). At the last phase, to produce LSM, all four models were combined and the performances of the ensemble ANN-RF-SVM-LR model were: precision = 0.784, efficiency = 0.804, AUC = 87.76%, MAE = 0.151, and RMSE = 0.195. Correspondingly, the performances (reliability and discrimination ability) of the models were assessed considering testing sub-set of GLIs as well. Individually, the LR model regarded as the best in terms of precision (0.825) and efficiency (0.829) but AUC defined the RF model as the best fit for predicting landslide susceptibility. The RF model has greater acceptability in spatial risk mapping as found in various research works [30; 54; 78]. Similarly, the validation results of two-model ensemble, three-model ensemble and four-model ensemble have been listed in Table 5.