Abstract
Landslide susceptibility maps (LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression (LR) and an artificial neural network (ANN) to produce a LSM. The LSM is produced with 11 causative factors and then optimized using forward-stepwise LR (FSLR), ANN, and their combination (FSLR-ANN) until eight causative factors were found for each method. The ANN method produced superior validation results compared with LR. The ROC values for the training data set ranges between 0.8 and 0.9. On the other hand, validation with the percentage of landslide fall into LSM class high and very high, ANN method was higher (92.59%) than LR (82.12%). FSLR-ANN with nine causative factors gave the best validation results with respect to area under curve (AUC) values, and validation with the percentage of landslide fall into LSM class high and very high. In conclusion, ANN was found to be better than LR when producing LSMs. The best Optimization was combination of FSLR -ANN with nine causative factors and AUC success rate 0.847, predictive rate 0.844 and validation with landslide fall into high and very high class with 91.30%. It is an encouraging preliminary model towards a systematic introduction of FSLR-ANN model for optimization causative factors in landslide susceptibility assessment in the mountainous area of Ujung Loe Watershed.
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Abbreviations
- P :
-
probability of landslide occurrence
- Z :
-
value of landslide causative factor
- C 0 :
-
intercept
- C 1, C 2, C n :
-
coefficient, which measures the contribution of independent factors
- CF 1, CF 2,..., CF n :
-
variation of landslide causative factor
- x i':
-
normalized input
- x i, x min, x max :
-
the actual input data, minimum and maximum input data
- x i :
-
input
- w i :
-
weight of input
- y i :
-
result of artificial neuron network
- G :
-
activation functions
- b 1 :
-
bias vector 1
- W 1 :
-
weight matrices 1
- b 2 :
-
bias vector 2
- W 2 :
-
weight matrices 2
- Z seventh :
-
value of landslide causative factor on test seventh
- LS ANN:
-
the final landslide susceptibility map calculated for each pixel
- f wi :
-
weight of each causative factor
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The authors thank Esri Indonesia for supporting the ARC GIS 10.3 in collaboration with Hasanuddin University, Indonesia.
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Soma, A.S., Kubota, T. & Mizuno, H. Optimization of causative factors using logistic regression and artificial neural network models for landslide susceptibility assessment in Ujung Loe Watershed, South Sulawesi Indonesia. J. Mt. Sci. 16, 383–401 (2019). https://doi.org/10.1007/s11629-018-4884-7
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DOI: https://doi.org/10.1007/s11629-018-4884-7