Search

Soil erosion resulting from heavy rainfall, depends on a number of factors, such as the native soil properties (the angle of internal friction, cohesion, unit weight, etc.), hydraulic conditions (surface runoff, groundwater seepage, etc.), the removal of vegetation and change in the surrounding environment. This study investigated the preliminary factors that could cause Soil erosion and detailed the literature through an exhaustive review of the previous published materials on the field and laboratory research works dealing with the soil erosion control techniques.

Modeling and predicting geohazards is extremely difficult due to their complex behavior in the real-world. In fact, several aspects of these environmental applications are considered in computer-based modeling to accurately estimating real-world phenomena. Till date, none of the proposed methods have reached to zero uncertainties or errors to recognize the entire disaster’s events. Globally, many people have lost their lives due to various types of natural hazards. Therefore, it is important to detect, monitor and predict them to protect the inhabitants against the potential natural hazards that threaten human lives and properties. Recently, artificial intelligent (AI) methods have received a great deal of attraction due to their precision to model the complex problems such as natural hazards. AI can see different aspects of a complex problem with sufficient iteration and details. In recent years, implementation of AI models coupled with geospatial information systems (GIS) are the most efficient and accurate approach to model natural disasters i.e. flooding, earthquake, landslides, forest fire and drought rather than other existing methods. This gives an insight into the ability of applied AI models in some natural hazards applications.