Abstract
Accurate prediction of the burned area from forest fires is essential for effective wildfire risk management and mitigation; however, this task remains challenging due to the highly nonlinear relationships and extreme skewness inherent in fire-weather data. This study proposes an optimized hybrid deep learning framework that integrates a Convolutional Neural Network and a Multilayer Perceptron (CNN–MLP) with metaheuristic optimization to enhance burned-area regression accuracy. A wrapper-based Binary Firefly Algorithm (BFA) is employed to identify the most informative subset of meteorological, fire-weather, and spatial features, reducing redundancy and improving generalization. To further optimize model performance, a hybrid Particle Swarm Optimization–Whale Optimization Algorithm (PSO–WOA) is used to tune the hyperparameters of the CNN–MLP architecture automatically. Experiments conducted on the UCI Forest Fires dataset demonstrate that the proposed PSO–WOA–CNN–MLP model significantly outperforms optimized baseline deep learning models, including CNN, MLP, LSTM, and GRU. The proposed model achieves very low prediction errors (Mean Squared Error (MSE) = 0.0093, Mean Absolute Error (MAE = 0.0767, Median Absolute Error (MedAE = 0.0616, Mean Absolute Percentage Error (MAPE = 0.0066) and an exceptional coefficient of determination (R² = 99.89%), indicating near-perfect agreement between predicted and observed burned areas.
Data availability
The data that support the findings of this study are available at https://archive.ics.uci.edu/dataset/162/forest+fires.
References
Andrianarivony, H. S. & Akhloufi, M. A. Machine learning and deep learning for wildfire spread prediction: a review. Fire 2024. 7, 482 (2024).
Xu, Z. et al. Deep learning for wildfire risk prediction: integrating remote sensing and environmental data. ISPRS J. Photogrammetry Remote Sens. 227, 632–677 (2025).
Castelli, M., Vanneschi, L. & Popovič, A. Predicting burned areas of forest fires: an artificial intelligence approach. Fire Ecol. 11, 106–118 (2015).
Wood, D. A. Prediction and data mining of burned areas of forest fires: optimized data matching and mining algorithm provides valuable insight. Artif. Intell. Agric. 5, 24–42 (2021).
Bayat, G. & Yildiz, K. Comparison of the machine learning methods to predict wildfire areas. Turkish J. Sci. Technol. Res. Paper. 17, 241–250 (2022).
Cortez, P. & Morais, A. A Data mining approach to predict forest fires using meteorological data. http://www.dsi.uminho.pt/˜pcortez
Agustyaningrum, C. et al. Forest fire data analysis using conventional machine learning algorithms. 32–37. https://doi.org/10.5220/0012441300003848 (2024).
Mambile, C., Kaijage, S. & Leo, J. Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, tanzania: integrating satellite images, weather data and human activities data. Nat. Hazards Res. 5, 335–347 (2025).
Alizadeh, N., Mahdianpari, M., Hemmati, E., Marjani, M. & FusionFireNet: A CNN-LSTM model for short-term wildfire hotspot prediction utilizing spatio-temporal datasets. Remote Sens. Appl. 37, 101436 (2025).
Li, Z., Huang, Y., Li, X. & Xu, L. Wildland fire burned areas prediction using long short-term memory neural network with attention mechanism. Fire Technol. 57, 1–23 (2021).
Ragab, M. Hybrid firefly particle swarm optimisation algorithm for feature selection problems. Expert Syst. 41, e13363 (2024).
Gad, A. G. Particle swarm optimization algorithm and its applications: a systematic review. Archives of Computational Methods in Engineering 2022 29:5 29, 2531–2561 (2022).
Mirjalili, S. & Lewis, A. The Whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016).
Sharifi, T., Mirsalim, M., Soleimanian Gharehchopogh, F. & Mirjalili, S. Cultural history optimization algorithm: a new human-inspired metaheuristic algorithm for engineering optimization problems. Neural Comput. Appl. 37, 21009–21068 (2025).
Anka, F., Gharehchopogh, F. S., Tejani, G. G. & Mousavirad, S. J. Advances in mountain gazelle optimizer: a comprehensive study on its classification and applications. International Journal of Computational Intelligence Systems 2025 18:1 18, 247- (2025).
Hosseinzadeh, M. et al. Improving phishing email detection performance through deep learning with adaptive optimization. Scientific Reports 2025 15:1 15, 36724- (2025).
Eaturu, A. & Vadrevu, K. Evaluation of machine learning and deep learning algorithms for fire prediction in Southeast Asia. Sci. Rep. 15 (1), 15–18807 (2025). (2025).
Jeon, G., Cho, C., Kim, K. & Choi, W. Deep-learning-based wildfire occurrence prediction with human factors in Gangwon Province, South Korea. Ecol. Inf. 92, 103519 (2025).
Pang, Y. et al. Forest fire occurrence prediction in China based on machine learning methods. Remote Sens. 2022. 14, 5546 (2022).
Saha, S., Bera, B., Shit, P. K., Bhattacharjee, S. & Sengupta, N. Prediction of forest fire susceptibility applying machine and deep learning algorithms for conservation priorities of forest resources. Remote Sens. Appl. 29, 100917 (2023).
Lin, X., Li, Z., Chen, W., Sun, X. & Gao, D. Forest fire prediction based on long- and short-term time-series network. Forests 2023. 14, 778 (2023).
Naderpour, M., Rizeei, H. M. & Ramezani, F. Forest fire risk prediction: A Spatial deep neural network-based framework. Remote Sens. 2021. 13, 2513 (2021).
Oncel Cekim, H., Güney, C. O., Şentürk, Ö., Özel, G. & Özkan, K. A novel approach for predicting burned forest area. Nat. Hazards. 105, 2187–2201 (2021).
Zhang, L., Shi, C. & Zhang, F. Predicting forest fire area growth rate using an ensemble algorithm. Forests 2024. 15, 1493 (2024).
Sun, X. et al. A forest fire prediction model based on cellular automata and machine learning. IEEE Access. 12, 55389–55403 (2024).
Karurung, W. S., Lee, K. & Lee, W. Assessment of forest fire vulnerability prediction in indonesia: seasonal variability analysis using machine learning techniques. Int. J. Appl. Earth Obs. Geoinf. 138, 104435 (2025).
Gao, B. et al. Prediction and driving factors of forest fire occurrence in Jilin Province, China. J. Res. (Harbin). 35, 21 (2024).
Bhatt, S. & Chouhan, U. An enhanced method for predicting and analysing forest fires using an attention-based CNN model. J. Res. (Harbin). 35, 67 (2024).
Shadrin, D. et al. Wildfire spreading prediction using multimodal data and deep neural network approach. Sci. Rep. 14 (1), 14–2606 (2024). (2024).
Jayasingh, S. K., Swain, S., Patra, K. J. & Gountia, D. An experimental approach to detect forest fire using machine learning mathematical models and IoT. SN Comput. Sci. 5, 148 (2024).
Su, Y. et al. FC-StackGNB: A novel machine learning modeling framework for forest fire risk prediction combining feature crosses and model fusion algorithm. Ecol. Indic. 166, 112577 (2024).
Yun, B., Zheng, Y., Lin, Z., Li, T. & FFYOLO A lightweight forest fire detection model based on YOLOv8. Fire 2024. 7, 93 (2024).
Roh, M., Lee, S., Jo, H. W. & Lee, W. K. Development of a forest fire diagnostic model based on machine learning techniques. Forests 2024. 15, 1103 (2024).
UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/162/forest+fires
Malakouti, S. M. From accurate to actionable: interpretable PM2.5 forecasting with feature engineering and SHAP for the Liverpool–Wirral region. Environ. Challenges. 21, 101290 (2025).
Malakouti, S. M. Leveraging SHapley Additive exPlanations (SHAP) and fuzzy logic for efficient rainfall forecasts. Scientific Reports 2025 15:1 15, 36499- (2025).
Elshewey, A. M., El-Bakry, H. M. & Osman, A. M. Enhancing water potability classification using a hybrid GRU with LSTM deep learning models based on relief algorithm and bayesian optimization. Evol. Syst. 17, 10 (2026).
Fahim, A., Osman, A. M., Tarek, Z. & Elshewey, A. M. Enhancing air quality index classification based on ensemble machine learning techniques. Eng. Technol. Appl. Sci. Res. 15, 29325–29333 (2025).
Qiao, J. et al. A hybrid particle swarm optimization algorithm for solving engineering problem. Sci. Rep. 14 (1), 14–8357 (2024). (2024).
Elshewey, A. M., Osman, A. M., Youssef, R. Y., El-Bakry, H. M. & Hassan, S. A. Z. Enhancing forest fires classification using a hybrid convolutional and BiLSTM deep learning model. Model. Earth Syst. Environ. 11, 379 (2025).
Mambile, C., Kaijage, S. & Leo, J. Application of deep learning in forest fire prediction: a systematic review. IEEE Access. 12, 190554–190581 (2024).
Alkhatib, R., Sahwan, W., Alkhatieb, A. & Schütt, B. A brief review of machine learning algorithms in forest fires science. Appl. Sci. 2023. 13, 8275 (2023).
Al Janabi, S., Al Shourbaji, I. & Salman, M. A. Assessing the suitability of soft computing approaches for forest fires prediction. Appl. Comput. Inf. 14, 214–224 (2018).
Kerdprasop, N., Poomka, P., Chuaybamroong, P. & Kerdprasop, K. Forest fire area Estimation using support vector machine as an approximator. Int. Joint Conf. Comput. Intell. 1, 269–273 (2018).
Nguyen Da, T., Nguyen Thanh, P. & Cho, M. Y. A cloud 15 kV-HDPE insulator leakage current classification based improved particle swarm optimization and LSTM-CNN deep learning approach. Swarm Evol. Comput. 91, 101755 (2024).
Nguyen Thanh, P. & Cho, M. Y. Advanced AIoT for failure classification of industrial diesel generators based hybrid deep learning CNN-BiLSTM algorithm. Adv. Eng. Inform. 62, 102644 (2024).
Nguyen-Da, T., Nguyen-Thanh, P. & Cho, M. Y. Real-time AIoT anomaly detection for industrial diesel generator based an efficient deep learning CNN-LSTM in industry 4.0. Internet Things. 27, 101280 (2024).
Nguyen, T. P., Lin, C. C. & Cho, M. Y. Fault detection of industrial air separation stations based on metaheuristic optimization and bidirectional long short-term memory technique. Internet Things. 36, 101850 (2026).
Acknowledgements
This work was funded by the University of Jeddah, Jeddah, Saudi Arabia, under grant No. (UJ-25-DR-20568). Therefore, the authors thank the University of Jeddah for its technical and financial support.
Funding
This work was funded by the University of Jeddah, Jeddah, Saudi Arabia, under grant No. (UJ-25-DR-20568). Therefore, the authors thank the University of Jeddah for its technical and financial support.
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Mohamed H. Mousa: Writing, Project administration, and Conceptualization. Abdullah M. Algamdi: Writing, Investigation, and Project Administration. Ahmed M. Elshewey: prepared figures and recorded the table results. Yasser Fouad: Wrote the main manuscript, edited, and performed Formal analysis. All authors reviewed the manuscript.
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Mousa, M.H., Algamdi, A.M., Fouad, Y. et al. CNN-MLP framework for forest burned areas prediction using PSO-WOA algorithm. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35836-4
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DOI: https://doi.org/10.1038/s41598-026-35836-4
