An Improved Predictive Model for Assessing the Impact of Deforestation and CO₂ Emissions on Flood Hazards in Pakistan

Climate change, along with deforestation as well increasing CO2 emissions, increases the intensity of floods in Pakistan. The common flood forecasting systems do not take into consideration essential environmental features, such as land use alterations and meteorological variables. This study applies machine learning models to develop and improve the combination of flood risk models throughout deforestation and CO₂ emissions and temperature and rainfall pattern changes. The analysis has been conducted via supervised learning models such as Gradient Boosting, Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbor on a 5,500-item dataset from open Punjab Pakistan data: Since the LightGBM model was found to be the model with 95% of prediction accuracy for flood forecasting. You will be aware, from research that points out that deforestation makes soils hold less water, contributing to greater runoff and hence flooding. CO₂ on the rise in the atmosphere contributes to unpredictable weather conditions and worsens the monsoon. Of the predictors of flood risk, rainfall had the highest prediction accuracy (0.78), next was CO₂ emissions (0.79), with deforestation to a lesser extent (0.74). Three variables determine the dynamic response of a flood: soil type, land cover, and reservoir water levels. The study utilized multiple environmental inputs to develop a comprehensive, data-driven prediction system, extending conventional hydrological models. The discovered data gives the necessary information that helps politicians and disaster management organizations, along with urban planners, to develop early warning systems and long-term flood control plans. According to this research, forest planting programs and reduced carbon dioxide emissions are necessary to mitigate the impact of flood threats. Future studies will seek to integrate more aspects of real-time data sources while also covering a broader geographical range and utilizing satellite observations to further improve flood prediction accuracy.