A DATA-DRIVEN APPROACH TO MONTHLY TEMPERATURE FORECASTING FOR CLIMATE ADAPTATION AND URBAN PLANNING IN KARACHI, PAKISTAN

Abstract

Temperature prediction is useful in combating the constantly changing climate conditions in the urban regions with reference to aspects such as agriculture, urban development and safety. The present study aims at providing accurate predictions for the monthly average temperature of Karachi city in Pakistan using machine learning algorithms with the goal of producing robust prediction resources for climate change planning. Karachi faces challenges such as rising temperatures, the urban heat island effect, and forecasting limitations. The city needs accurate temperature data to save its assets and people from climate change. The model was checked by comparing the estimated temperature for the year 2024 with the observed values. According to the results, the 2024 predictions achieved a low Mean Squared Error of 0.49, demonstrating the high accuracy of the predictive model. For instance, the mean predicted temperature for the Karachi for May 2024 was 35.7 °C while the actual temperature was 35.8 °C, the difference of only 0.1 °C. Furthermore, the study makes two predictions and controls up to the first three months of the year 2025. The model successfully forecasted the temperatures for January, February, and March 2025, with observed average temperatures of 26.8°C for January and February, and 27.1°C for March which corresponds to the usual working season temperature patterns and validates the proposed model for long term forecasting. This investigation is helpful for reflecting Karachi’s temperature trends and will be useful for creating more efficient structures as well as preventing measures for climate change. This research helps in understanding the temperatures in Karachi effectively and has a potential for using machine learning methods to resolve environmental problems. This research highlights the potential of data-driven approaches for enhancing climate resilience and offers a practical framework for temperature forecasting in regions to support sustainable city planning.