دانشگاه شهید بهشتی پژوهشهای دانش زمین 2008-8299 16 Special Issue 2025 12 01 Comparative assessing the performance of ANN, RF and CNN machine learning methods in identifying landslide prone areas Comparative assessing the performance of ANN, RF and CNN machine learning methods in identifying landslide prone areas 73 91 106289 10.48308/esrj.2025.240455.1282 FA Sayyad Asghari Saraskanroud Department of Physical Geography, Faculty of Social Science, University of Mohaghegh Ardabili, Ardabil, Iran Maryam Riahinia Department of RS & GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz , Ahvaz, Iran Batool Zeinali Department of Climatology, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran Raoof Mostafazadeh Department of Natural Resources, Faculty of Agriculture and Natural Resources, Water Management Research Center, University of Mohaghegh Ardabili, Ardabil, Iran Journal Article 2025 06 24 Landslides are one of the natural hazards in mountainous areas that cause a lot of damage every year, thus, determining the landslide prone area is very important. This study assessed the performance of artificial neural network (ANN), Random Forest (RF), and Convolutional Neural Network (CNN) methods in identifying landslide prone areas. The study area is Lorestan Province, Khorramabad Watershed in western Iran, a region highly susceptible to landslides. After pre-processing the satellite images, the training samples were collected using field visits. Then, the neural network with a modifed structure was used for classification based on the simultaneous integration of the algorithm used. The available data were divided into 70% for the training, and 30 % for the validation stages. The performance of the generated classification maps of three employed methods were evaluated using the overall accuracy and confusion matrix. The results of evaluating the performance and accuracy of the CNN algorithm for identifying landslide areas show 93% overall accuracy. While the evaluation of the results obtained from ANN and RF methods shows that the overall accuracy of the neural network method is 90% and its overall accuracy is 88% and in the random forest method the overall accuracy is 84% and the overall accuracy is 82%; This study shows that the proposed method has shown the best performance compared to other methods according to evaluation criteria.These findings highlight the superiority of the CNN-based approach in accurately mapping landslide-prone areas, making it a reliable tool for future hazard assessment and risk management in mountainous regions. Landslides are one of the natural hazards in mountainous areas that cause a lot of damage every year, thus, determining the landslide prone area is very important. This study assessed the performance of artificial neural network (ANN), Random Forest (RF), and Convolutional Neural Network (CNN) methods in identifying landslide prone areas. The study area is Lorestan Province, Khorramabad Watershed in western Iran, a region highly susceptible to landslides. After pre-processing the satellite images, the training samples were collected using field visits. Then, the neural network with a modifed structure was used for classification based on the simultaneous integration of the algorithm used. The available data were divided into 70% for the training, and 30 % for the validation stages. The performance of the generated classification maps of three employed methods were evaluated using the overall accuracy and confusion matrix. The results of evaluating the performance and accuracy of the CNN algorithm for identifying landslide areas show 93% overall accuracy. While the evaluation of the results obtained from ANN and RF methods shows that the overall accuracy of the neural network method is 90% and its overall accuracy is 88% and in the random forest method the overall accuracy is 84% and the overall accuracy is 82%; This study shows that the proposed method has shown the best performance compared to other methods according to evaluation criteria.These findings highlight the superiority of the CNN-based approach in accurately mapping landslide-prone areas, making it a reliable tool for future hazard assessment and risk management in mountainous regions. https://esrj.sbu.ac.ir/article_106289_25ef10cd25b4de565784a2caf721999c.pdf