Climate change has increased the frequency of natural disasters, and Pakistan, as a developing nation, is facing severe challenges in coping with floods, which have devastatingly impacted people's livelihoods. In 2022, floods affected over 33 million people, resulting in approximately 1730 deaths, according to the World Bank. Flood prediction is a critical research area. This study employs machine learning techniques to provide accurate and reliable flood forecasts for Pakistan.

Specifically, Support Vector Machines (SVM) and logistic regression algorithms are utilized in this research for flood prediction. Historical data encompassing floods, rainfall, temperature, water level, topographic information, and land cover of Pakistan is collected and split into 75% for model training and 25% for testing. Additionally, topographic data and land cover information are employed to create inundation maps.

The findings highlight three topographic factors that play a pivotal role in predicting flood-sensitive areas: slope, distance to the river, and river. The combined SVM and logistic regression model (SVR-LR) exhibited area under the curve values of 0.98 and 0.95 for the training and testing phases, respectively. These results demonstrate the efficacy of the SVM and logistic regression integration for precise flood forecasting in Pakistan, contributing to enhancing flood resilience in the region.