Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping

Abstract

Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced convolutional neural network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN-based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected Zhangzha Town in Sichuan Province, China, and Lantau Island in Hong Kong, China, as the study areas. Each landslide inventory and corresponding predisposing factors were stacked to form spatial datasets for LSM. The receiver operating characteristic analysis, area under the curve (AUC), and several statistical metrics, such as accuracy, root mean square error, Kappa coefficient, sensitivity, and specificity, were used to evaluate the performance of the models. Finally, the trained models were calculated, and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine learning-based models have a satisfactory performance. The CNN-based model exhibits an excellent prediction capability and achieves the highest performance but also significantly reduces the salt-of-pepper effect, which indicates its great potential for application to LSM.

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

“…The activation function follows the convolutional layer and Rectified linear unit function is the most popular and effective activation function. The down-sampling layer or pooling layer is used to reduce the size of features, and the over-fitting tendency of the model [60]. DNN is a popular method employed in various natural hazard-related studies.…”

“…Liu et al [60] did a comparative study of CNN with conventional ML models. The LSM generated by the CNN-based model is sensitive to the high-risk landslide zone and significantly reduces the salt-and-pepper effect, which guarantees the consistency of susceptibility assessment.…”

“…For example, the study [51] used terrain factors generated from a DEM scale at 30 m resolution, the geological factors were vectored from a 1:200,000 geological map, and all factors were resampled at 30 m resolution for convenience. Liu et al [60] employed resampling of the digital terrain model (DTM) with a 5 m × 5 m grid to 30 m × 30 m to make the scale of all factors consistent. Liu et al [61] suggest exploring the effect of quantity and quality of LCFs on the prediction accuracy of ML models.…”

Recent Advances of Deep Learning in Geological Hazard Forecasting

“…Due to the powerful image feature extraction performance of CNNs, they can theoretically be directly applied to landslide susceptibility assessment. For example, Liu et al [160] compared CNNs and traditional machine learning methods for landslide susceptibility mapping and demonstrated that both CNNs and traditional machine learning-based models have satisfactory performance, and CNNs achieved the highest performance. Youssef et al [161] compared SVM, one-dimensional and two-dimensional CNNs and demonstrated that two-dimensional CNNs have the best performance compared to one-dimensional CNNs and SVMs.…”

Clasificación de uso del suelo y vegetación con redes neuronales convolucionales

“…La exactitud de la prueba aumenta a medida que la curva se desplaza desde la diagonal hacia el vértice superior izquierdo. Un valor mayor indica que el modelo puede lograr un mejor rendimiento (Liu et al, 2022).…”