Fuzzified Risk Assessment Model for Detection and Prediction of Flood Using Machine Learning Techniques

Pluvial flooding is a type of flood that has become a threat to human life and the global economy due to the variability in climate change, rapid urbanization and fast-growing spatial development. Studies have shown that Machine Learning (ML) applications can be used to reduce this risk. However, less attention has been paid to the use of a fuzzy rule-based classification to appraise the performance of ML, based on pluvial flood Conditioning Variables (CVs). Therefore, the study developed a fuzzified risk assessment model for the detection and prediction of pluvial flood using ML techniques. The application was developed by categorizing the pluvial flood CVs using the degree of interdependency index. A total of 144,401 records and eight CVs out of 53 were gathered from the United States Geological Survey and Copernicus Climate Data Store from five Local Government Areas (LGAs) in Oyo State, Nigeria. Fuzzy logic was applied to the eight CVs using the triangular method, after which five ML algorithms were used to train the CVs: K-Nearest Neighbours (KNN), Random Forest (RF), Classification and Regression Trees (CART), Naïve Bayes (NB) and Artificial Neural Network (ANN) algorithms. The fuzzy logic re-classified the selected CVs into five classes: no flood, low, moderate, high and very high pluvial flood susceptibility. The performance of the application was evaluated using the 10-fold cross-validation and hold-out techniques, based on accuracy, sensitivity, specificity, precision and Area Under Receiver Operating Characteristics (AUROC) metrics. The study concluded that the fuzzified ML application can be used in detecting and predicting pluvial floods. Consequently, CART which had the best results, when compared to the rest of the classifier models, is recommended for use by experts. This is because the application showed the capacity to assist experts to alert the public on expected risks. Also, it will further assist local authorities and government agencies for appropriate pluvial flood management plans.