Modelling of Flood Prediction by Optimizing Multimodal Data Using Regression Network

Abstract

Natural disasters are an unpredictable one, but the damages caused by the disaster is severe. It causes hazards to both humans and their properties. Among many hazards like earthquake, eruption and flood, the flood prediction is a quite predictable one. But, it requires proper learning for predicting the floods. Because the flood occurs due to the overflow of water from dams and rivers which is caused by heavy rainfall. Due to this, proper learning based on the weather conditions and previous flood data, the possibility of flood range and area can be detected. Such a flood prediction is performed on the real-time data set collected from the Columbia province. It uses data mining approach on the collected information for forecasting the flood level. But, it is not only sufficient for real time. Hence, the image-based flood prediction is proposed to analyse the flood level in the particular region using a satellite image of the area. Yet, both the techniques offer good prediction in the individual unit it suffers from predicting the nearby flood areas. Hence, in this, a combined approach of image processing and data mining is proposed to forecast the flood level. For data mining, the regression learning approach is used to forecast future flood levels. It is combined with the corresponding image processing of the particular area to accurately the flood level based on extracting the depth of water in the area. Finally, the predicted level is broadcasted to the people through social media for immediate action to save their lives. The proposed regression-based data mining and image analysis help to forecast the level of flood in the area accurately along with broadcasting saves precious lives of people.