Identifying Flood Prediction using Machine Learning Techniques

Abstract

Flood is the most devastating and destructive that can destroy everything on land. These floods will cause further flooding in affected areas. Flood prediction models are being researched to reduce risk, think strategically, reduce human life and reduce property damage from floods. Over the last two years, AI techniques have improved the forecasting process, resulting in better execution and financial planning stability. First of all, these events can take everyone's feelings into account. Artificial intelligence models for flood prediction are crucial for flood warning, flood mitigation or prediction. Machine learning programs have become ubiquitous due to their computational needs for limited information. We believe that collecting only a small amount of data can help representative vector, best scores. The selected tree was successful due to better than expected accuracy and best score. Machine learning algorithms used in this flood prediction are decision trees, logistic regression, etc. For evaluation and comparison. Logistic regression can provide more accurate results than other algorithms and provide high efficiency and improvement. Floods are perhaps the most destructive event in the world, can cause irreversible damage and cause great suffering to humanity. Generally, most farmers are the most disturbed people in the world because their hard work can suddenly fail, causing their hearts to become melancholy. To measure water level and velocity over a large area, it is important to provide an exposure model that includes safety. These models can be aimed to improve the prediction by using different methods. Additionally, these models provide accurate predictions of flood events in a year, but do not provide much understanding and detail of the options needed.