Climate classification prediction based on compound drought and hot events and AI classification algorithms: Iraq as a case study
Keywords:
STI, SPI, compound hot/dry, climate change, artificial inelegance algorithm.Abstract
Climate change is one of the most important challenges facing the world in this century due to its significant role in affecting the way of life on Earth in addition to its significant impact on the world's economies. The increase in extreme hydrological phenomena such as drought and floods has required more studies to analyse these phenomena and develop plans to reduce their impact on society and the economy. In the present research, the climate of Iraq was studied and analysed using the Standardized Precipitation Index (SPI), the Standard Temperature Index (STI), and the analysis of compound hot and dry events. Temperature and precipitation data from four meteorological stations representative of the climate of Iraq were used. Two classification algorithms were used to predict the annual climate classification of Iraq. The results showed that Iraq was affected by climate change, as the percentage of extreme hot and dry events reached 90% during the period 2012-2022, with the annual rainfall rate decreasing by more than 40%, and temperatures were observed to rise at a rate of 0.68 degrees Celsius per decade. The novelty of this research is the application of classification algorithms in machine learning using hot and dry categorical classification as input variables and the study demonstrated the efficiency of the Bayesian network algorithm in predicting the annual and monthly climate classification.
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