15-Artificial neural networks and remote sensing for modeling and mapping of hydrological disasters

Artificial neural networks and remote sensing are being used frequently for modeling, forecasting, and mapping hydrological disasters such as floods and landslides. Flood or landslide susceptibility maps are among the main applications. The prediction of areas affected by disasters from observed or future rainfall has also been developed using machine learning methods such as artificial neural networks. In this context, the objective of this short course is to present and demonstrate some approaches for modeling, predicting, and mapping floods and landslides, using the method of artificial neural networks and remote sensing data. The proposal is a theoretical and practical course, with the learning of conceptual aspects and the editing of computer codes and algorithms for reading and processing data.