Performance Analysis of Fuzzified Machine Learning Algorithm for Flood Risk Assessment

Abstract

Pluvial flooding is a type of flood that occurs when high-force precipitation surpasses the limit of drainage framework which has become a threat to human life and the global economy, thus this study proposes a fuzzified Machine Learning (ML) applications that 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 applications, based on pluvial flood Conditioning Variables (CVs) for training a classifier. This research proposes a fuzzified classifier models and a performance analysis of the five ML algorithms namely K-Nearest Neighbours (KNN), Random Forest (RF), Classification and Regression Trees (CART), Naïve Bayes (NB) and Artificial Neural Network (ANN) algorithms to detect and predict pluvial flood risk. The performance analysis 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 performance evaluation results for each algorithm, using hold-out techniques in respect of accuracy, sensitivity, specificity, precision, and AUROC for KNN were 95.3%, 95.3%, 92.7%, 93.8% and 92.2% respectively; for RF, 72.8%, 73.0%, 73.2%, 73.0% and 83.6% respectively; for NB, 71.0%, 77.0%, 73.7%, 84.7% and 72.7% respectively; for CART, 98.4%, 98.4%, 98.3%, 98.4% and 98.6% respectively; and for ANN, 83.6%, 84.0%, 96.9%, 74.0% and 87.9% respectively. In addition, results obtained for using 10-fold cross-validation method for KNN were 96.4%, 96.4%, 94.1%, 96.6% and 93.7% respectively; for RF, 95.2%, 95.2%, 93.7%, 94.3% and 94.6% respectively; for NB, 77.3%, 77.3%, 74.7%, 84.3% and 89.5% respectively; for CART, 95.5%, 99.5%, 99.4%, 99.5% and 97.6% respectively; and for ANN, 89.5%, 89.5%, 89.7%, 89.1% and 89.9% respectively. Thus, this study shows 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.