Floods are the natural disaster that occurs most frequently due to the weather and causes
the most widespread destruction. The purpose of the proposed research is to analyze flood …

Urban flood risk management requires an extensive investigation of the vulnerability
characteristics of buildings. Large-scale field surveys usually cost a lot of time and money …

For any machine learning model, finding the optimal hyperparameter setting has a direct
and significant impact on the model's performance. In this paper, we discuss different types …

Twenty-two flood-causative factors were nominated based on morphometric, hydrological,
soil permeability, terrain distribution, and anthropogenic inferences and further analyzed …

Identifying flood-prone regions is critical for effective management of flood hazards as floods
are among the most devastating natural disasters globally. However, accurate modeling and …

Abstract Machine learning models are gradually replacing traditional techniques used for
landslide susceptibility assessment. This study aims to comprehensively compare multiple …

Flood susceptibility mapping plays a crucial role in flood risk assessment and management.
Accurate identification of areas prone to flooding is essential for implementing effective …

Landscape change is intricately linked to natural resource utilization. Landscape dynamics
are closely tied to land use and land cover (LULC), serving as a representation of …

The long short-term memory network (LSTM) model alleviates the gradient vanishing or
exploding problem of the recurrent neural network (RNN) model with gated unit architecture …

Floods have caused significant socio-economic damage and are extremely dangerous for
human lives as well as infrastructures. The aim of this study is to use machine learning …