# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 3 Results
### Landslide Susceptibility
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **No of** & \multirow{2}{*}{**No of Factors**} & **Test Site Area/Pixel** & \multirow{2}{*}{**AUC/Accuracy**} & **Articles** \\  & **landslides** & & & & \\ \hline ANN & 154 & 7 & 600 km\({}^{2}\)/5 m \(\times\) 5 m & 0.84 & [PERSON] et al. [101] \\ \hline Back propagation neural & \multirow{2}{*}{63} & \multirow{2}{*}{6} & \multirow{2}{*}{\(-\)116 km\({}^{2}\)/30 m \(\times\) 30 m} & \multirow{2}{*}{0.8} & [PERSON] et al. [97] \\ network & & & & & \\ \hline SVM, Proximal SVM, & \multirow{2}{*}{2009 pixels} & \multirow{2}{*}{8} & \multirow{2}{*}{1625 km\({}^{2}\)/30 m \(\times\) 30 m} & \multirow{2}{*}{0.807} & [PERSON] et al. [102] \\  & [PERSON] & & & & & \\ \hline Multilayer Perceptron (MLP) Neural Networks & \multirow{2}{*}{930} & \multirow{2}{*}{15} & \multirow{2}{*}{1325.47 km\({}^{2}\)/20 m \(\times\) 20 m} & \multirow{2}{*}{0.886} & [PERSON] et al. [94] \\ \hline sequential minimal & \multirow{2}{*}{430} & \multirow{2}{*}{11} & \multirow{2}{*}{323.82 km\({}^{2}\)/20 m \(\times\) 20 m} & \multirow{2}{*}{0.891} & [PERSON] et al. [98] \\  & optimization (SMO) & & & & \\ SVM, vote feature interval (VFI), Logistic & \multirow{2}{*}{430} & \multirow{2}{*}{11} & \multirow{2}{*}{323.82 km\({}^{2}\)/20 m \(\times\) 20 m} & \multirow{2}{*}{0.891} & [PERSON] et al. [98] \\ Regression (LR) & & & & & \\ \hline Functional Trees (FT), & \multirow{2}{*}{430} & \multirow{2}{*}{11} & \multirow{2}{*}{0.32 km\({}^{2}\)/20 m \(\times\) 20 m} & \multirow{2}{*}{0.850} & [PERSON] et al. [99] \\ Multilayer Perceptron (MLP) Neural Networks, & \multirow{2}{*}{Naive Bayes (NBI)} & \multirow{2}{*}{930} & \multirow{2}{*}{15} & \multirow{2}{*}{0.13 km\({}^{2}\)/20 m \(\times\) 20 m} & \multirow{2}{*}{0.891} & [PERSON] et al. [103] \\ Naive Bayes (NBI) & & & & & \\ \hline Rotation Forest based & \multirow{2}{*}{930} & \multirow{2}{*}{15} & \multirow{2}{*}{0.13 km\({}^{2}\)/20 m \(\times\) 20 m} & \multirow{2}{*}{0.891} [PERSON] et al. [103] \\ Radial Basis Function (RFRF) neural network & & & & & \\ \hline Aggregating & \multirow{2}{*}{1295} & \multirow{2}{*}{16} & \multirow{2}{*}{561 km\({}^{2}\)/30 m \(\times\) 30 m} & \multirow{2}{*}{0.968} [PERSON] et al. [104] \\ One-Dependence & \multirow{2}{*}{1295} & \multirow{2}{*}{16} & \multirow{2}{*}{561 km\({}^{2}\)/30 m \(\times\) 30 m} & \multirow{2}{*}{0.968} & [PERSON] et al. [104] \\ Estimators, SVM, & & & & & \\ ANN-RBF, LR, NB & & & & & \\ \hline Ensemble decision tree & 103 & 10 & 242 km\({}^{2}\)/20 m \(\times\) 20 m & 0.883 & [PERSON] et al. [13] \\ \hline Rotation Forest & \multirow{2}{*}{103} & \multirow{2}{*}{10} & \multirow{2}{*}{242 km\({}^{2}\)/30 m \(\times\) 30 m} & \multirow{2}{*}{0.741} & [PERSON] et al. [95] \\ Ensemble Model & & & & & \\ \hline Hybrid Reduced Error & \multirow{2}{*}{103} & \multirow{2}{*}{10} & \multirow{2}{*}{242 km\({}^{2}\)/30 m \(\times\) 30 m} & \multirow{2}{*}{0.989} [PERSON] et al. [100] \\ Pruning Trees & & & & & \\ \hline Hybrid model of Multi & \multirow{2}{*}{391} & \multirow{2}{*}{16} & \multirow{2}{*}{270 km\({}^{2}\)/10 m \(\times\) 10 m} & \multirow{2}{*}{0.972} [PERSON] et al. [105] \\ Boost ensemble and & & & & & \\ SVM & & & & & \\ \hline Neuro-fuzzy inference & \multirow{2}{*}{391} & \multirow{2}{*}{16} & \multirow{2}{*}{270 km\({}^{2}\)/30 m \(\times\) 30 m} & \multirow{2}{*}{0.95} & [PERSON] et al. [106] \\ system & & & & & \\ \hline Fuzzy Expert System, \multirow{2}{*}{49} & \multirow{2}{*}{8} & \multirow{2}{*}{43 km\({}^{2}\)/15 m \(\times\) 15 m} & \multirow{2}{*}{0.844} & [PERSON] et al. [96] \\ Extreme Learning & \multirow{2}{*}{49} & \multirow{2}{*}{8} & \multirow{2}{*}{43 km\({}^{2}\)/15 m \(\times\) 15 m} & \multirow{2}{*}{0.844} & [PERSON] et al. [96] \\ Machine & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Various computational models used for landslide susceptibility in Uttarakhand Himalayas. evaluation model for Kullu, Himachal Pradesh and found it to produce better results when compared to FR and AHP.