Evaporation plays a significant role in managing water resources and is an important indicator in risk and crisis management, particularly in operating reservoirs and dams. Precise predictions of evaporation rates are crucial to effective water resource management, and various modelling methods, including AI and autoregression, have been employed to create accurate models. This makes it more important to use innovative technology to continuously monitor this phenomenon with accurate scientific results, allowing decision-makers to be aware of and prepare for potential drought risks and crises. In this study, therefore, we propose the establishment of a mechanism that would include analyzing and exploring the data used in this study (Evaporation) and cleaning up the impurities of actual and lost values to obtain accurate data that would serve as actual inputs to ARIMA model that will adopt in this study, This mechanism would contribute to the performance and efficiency of this model using time series data to accurately predict future trends of evaporation plants in the water of the Mosul dam. Our objective is to explain the diversity of climate policies and actions using a data-based approach to analyzing integrated parameters over the years, etc. This is complemented in depth by how different methods of extracting data behaviour are used to study model forecasts. This collaborative study aims to enhance future studies by using more comprehensive datasets with more learning models. The researchers believe in the power of sharing knowledge and are thus committed to sharing the results of other causes outside of global warming that contribute to climate change.
link
https://doi.org/10.54216/FPA.170225