The comparison of analytical hierarchy process, frequency ratio, and deep learning-based approaches for landslide susceptibility mapping in the Northern Chiang Mai watershed Basins, Thailand

Abstract

Background

Landslides pose a significant natural hazard in Northern Thailand, threatening lives, infrastructure, and sustainable watershed management. Developing reliable landslide susceptibility maps for the Northern Chiang Mai Watershed Basins (NCMBs) was therefore crucial for risk reduction and planning.

Objective

This study employed two traditional methods, Analytical Hierarchy Process (AHP) and Frequency Ratio (FR), alongside two advanced deep learning approaches, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The primary objective was to identify areas prone to landslide susceptibility and determine the underlying causative factors that caused landslides in the NCMBs.

Method

A landslide inventory of 1,222 scars collected from 1989 to 2020 was divided into training and validation datasets. The training data and 10 landslide causative factors (grouped as topography, hydrology, geology, and land modification) were used to construct the susceptibility maps. Feature importance, quantified using Mean Decrease Impurity and Information Gain Ratio, revealed that slope, rainfall, lithology, soil texture, and elevation were the dominant causative factors. Susceptibility maps were produced using each model and validated with the historical landslide inventory. Predictive performance was evaluated through the receiver operating characteristic area under the curve (ROC AUC), along with precision, recall, F1-score, and overall accuracy.

Result

The results indicated that 60–70% of the entire watershed areas were classified as moderately to highly susceptible. The deep learning-based models (CNN and LSTM) achieved superior predictive accuracy (AUC > 0.85) compared to FR and AHP. While less accurate, the traditional models remained valuable due to their interpretability and lower data requirements.

Conclusion

The susceptibility maps reveal that highly landslide-susceptible zones are concentrated in steep, lithologically less resistant, and high rainfall-prone areas. Conversely, low-susceptible zones are located in valleys and on gently sloping terrain. This study provides a valuable reference for future research, contributing to the development of more robust landslide early warning systems and informed land-use planning in Northern Thailand and other regions with similar environmental conditions.