Abstract

Landslides pose threat to people and property, so early warning systems need to use advanced prediction models. This study looks into how deep neural networks (DNNs) can be used to predict landslides by taking advantage of their ability to find complex relationships in big datasets. One new method is to use hyperparameter tuning to improve the DNN's ability to make predictions. The model is taught using a variety of geographic and meteorological data, such as the type of soil, the amount of rainfall, the land's shape, and past landslides. To get the best model performance, hyperparameter tuning is done using advanced optimization algorithms to improve the neural network design. The suggested DNN framework is better at predicting the future than traditional methods, as it can pick out small patterns that show how likely a landslide is to happen. The process of hyperparameter tuning makes the model even better, making sure that it can be used reliably in a wide range of places and weather situations. The study adds to the growing field of landslide prediction by showing a complete and useful way to combine hyperparameter optimization with deep learning. The results could help make early warning systems more reliable, which would lessen the damage that floods do to homes and buildings.