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With the growing availability of extensive historical climate data and the increasing demand for accurate production forecasting, there is a pressing need for reliable methods to determine the stochastic relationship between past and future values. This article introduces a novel deep learning model designed to overcome the limitations of traditional forecasting methods and achieve highly accurate predictions. The proposed approach is a deep long short-term memory (DLSTM) model optimized using genetic algorithms (GAs) and the mountain gazelle optimizer (MGO), which collectively fine-tune the architecture of the DLSTM model. The experiment utilized historical climate data from nine Egyptian cities: <jats:bold>Asswan<\/jats:bold>, <jats:bold>Bane-Suef<\/jats:bold>, <jats:bold>Behira<\/jats:bold>, <jats:bold>Dakhalia<\/jats:bold>, <jats:bold>Menoufia<\/jats:bold>, <jats:bold>Minia<\/jats:bold>, <jats:bold>Qalyubia<\/jats:bold>, <jats:bold>Sharkia<\/jats:bold>, and <jats:bold>Sohag<\/jats:bold> to evaluate the model\u2019s performance. To ensure a fair and comprehensive evaluation, the effectiveness of the proposed MGO-GA-DLSTM model was compared with other established forecasting techniques. The evaluation metrics included <jats:bold>mean absolute error (MAE)<\/jats:bold>, <jats:bold>root mean square error (RMSE)<\/jats:bold>, <jats:bold>mean absolute percentage error (MAPE)<\/jats:bold>, and <jats:bold>R-squared <\/jats:bold>(<jats:inline-formula>\n              <jats:tex-math>$$R^2$$<\/jats:tex-math>\n            <\/jats:inline-formula>) to quantify prediction accuracy and model robustness. The findings demonstrate that the MGO-GA-DLSTM model outperforms existing methods in climate prediction, offering improved accuracy and reliability.<\/jats:p>","DOI":"10.1007\/s10462-025-11247-1","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:22:05Z","timestamp":1748996525000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Optimized deep learning architecture for predicting maximum temperatures in key Egyptian regions using hybrid genetic algorithm and mountain Gazelle optimizer"],"prefix":"10.1007","volume":"58","author":[{"given":"Essam H.","family":"Houssein","sequence":"first","affiliation":[]},{"given":"Mahmoud","family":"Dirar","sequence":"additional","affiliation":[]},{"given":"A. 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