Hybrid Model for Forecasting Temperature in Khartoum Based on CRU data

Abstract

This consider leverages verifiable climatic data from the Climatic Research Unit (CRU), traversing from 1901 to 2022, to create progressed temperature forecasting models for Khartoum, Sudan. By applying state-of-the-art machine learning techniques, including Hybrid model, we aim to progress the precision of temperature forecasts in a semi-arid climate. The integration of long-term CRU data permits for the recognizable proof of climate patterns and patterns, upgrading the unwavering quality of short- and long-term forecasts. Moved forward temperature forecasting can altogether advantage basic segments  empowering way better adjustment to climatic changes and extraordinary climate occasions. Our approach illustrates the potential of combining authentic climate data with machine learning to supply noteworthy experiences for climate flexibility.