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Pakistan is highly prone to devastating floods, as seen in the June 2010 and September 2022 disasters. The 2010 floods affected 20 million people, causing 1,985 fatalities. In 2022, approximately 33 million individuals were impacted, with multiple districts declared as ‘calamity struck’ by the National Disaster Management Authority (NDMA). Since June 14th, these floods have caused the loss of approximately 1,400 lives. Hence, the urgent necessity to develop an accurate and efficient flood risk prediction system for early warning purposes in Pakistan. This research aims to address this need by developing a predictive model using machine learning (ML) techniques such as k-nearest neighbors (KNN), support vector machine (SVM), Naive Bayes (NB), artificial neural network (ANN), and random forest (RF) for flood risk prediction in the Indus Basin of Pakistan. The performance of each model was evaluated based on accuracy, precision, recall, and F-measure. The findings revealed that SVM outperformed the other models, achieving an accuracy of 82.40%. Consequently, the results of this study can provide valuable insights for organizations to proactively mitigate frequent flood occurrences in Pakistan, aiding preventive actions. HIGHLIGHTS ML models KNN, SVM, NB, ANN, and RF were used to predict floods in the Indus River Basin, Pakistan.; The dataset consisted of five features: date, precipitation, temperature, monthly discharge, and flood occurrence, covering the years 1985 to 2013.; Performance evaluation of the models included metrics such as accuracy, precision, recall, and F-measure.; SVM demonstrated the highest accuracy among all the models tested.; |
