2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 13 , Issue 1 , PP: 01-23, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Doaa Sami Khafaga 1 *
Doi: https://doi.org/10.54216/AJBOR.130101
Achieving the United Nations Sustainable Development Goals (SDGs) requires robust forecasting tools capable of capturing complex temporal and multi-dimensional patterns in global sustainability data. Traditional statistical models often struggle with the high dimensionality and nonlinear dynamics of such datasets, motivating the adoption of advanced Deep Learning (DL) methods combined with metaheuristic optimization techniques. This paper proposes a novel forecasting framework leveraging Gated Recurrent Units (GRUs), Long Short-Term Memory networks (LSTMs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), optimized using the Human-Inspired Metaheuristic Optimization Algorithm (iHOW) and its binary variant (biHOW) for feature selection. The key contribution lies in integrating metaheuristic-driven feature selection and hyperparameter tuning to significantly enhance predictive performance and computational efficiency in SDG forecasting. Results highlight substantial improvements over baseline models: the GRU baseline achieved an R2 of 0.8037 with a Mean Squared Error (MSE) of 0.0772; application of biHOW for feature selection improved the GRU’s performance to an R2 of 0.9251 and MSE of 0.0011; and further hyperparameter tuning with iHOW elevated performance to an R2 of 0.9671 with MSE maintained at 0.0011. These results demonstrate the effectiveness of iHOW in balancing exploration and exploitation, providing high-accuracy forecasts with reduced error, thereby supporting more informed decision-making. The implications extend beyond sustainability analytics, presenting transferable forecasting frameworks for data-driven, real-time decision support in business sectors such as finance, energy, healthcare, and climate risk management. This alignment of predictive analytics with strategic financial and operational planning underscores the commercial value of integrating AI-driven forecasting into sustainability-focused investment and policy frameworks.
Sustainable Development Goals (SDGs) , Deep Learning Forecasting , Human-Inspired Meta-heuristic Optimization (iHOW) , Feature Selection and Hyperparameter Tuning , AI-Driven Decision Support Systems
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