Machine Learning-Based Approaches to Forecasting Social, Economic, and Environmental Trends

Darshan Mehta

Machine Learning-Based Approaches to Forecasting Social, Economic, and Environmental Trends

Author(s)	Darshan Mehta
Country	United States
Abstract	The increasing availability of large-scale digital datasets has created new opportunities for forecasting complex social, economic, and environmental trends. Machine Learning (ML) provides computational methods capable of identifying nonlinear relationships, temporal patterns, interactions, and emerging signals that may be difficult to capture through conventional statistical approaches. This study examines the application of machine-learning-based approaches to forecasting social, economic, and environmental trends, focusing on time-series models, supervised learning, ensemble methods, deep learning, and hybrid forecasting frameworks. A qualitative and comparative research methodology based on secondary literature is employed to examine the applications, advantages, limitations, and governance challenges associated with machine-learning forecasting. The analysis demonstrates that ML can support economic forecasting through prediction of indicators such as inflation, employment, demand, and market conditions; social forecasting through analysis of demographic changes, migration, public behaviour, and social-service demand; and environmental forecasting through prediction of air quality, climate variables, natural hazards, and ecological conditions. However, forecasting accuracy depends heavily on data quality, temporal stability, model selection, feature engineering, and appropriate validation procedures. Issues including algorithmic bias, interpretability, data availability, concept drift, privacy, and uncertainty also present significant challenges. The study proposes an integrated forecasting framework that combines machine learning with domain expertise, statistical validation, explainable AI, and scenario analysis. Such an approach can improve evidence-based decision-making while recognising the uncertainty inherent in long-term social, economic, and environmental forecasting.
Keywords	: Machine Learning, Forecasting, Social Trends, Economic Trends, Environmental Trends, Predictive Analytics, Time-Series Forecasting, Artificial Intelligence, Data Analytics, Decision Support.
Field	Engineering
Published In	Volume 4, Issue 4, July-August 2022
Published On	2022-07-17

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American Journal of Advanced Multidisciplinary Research and Innovation

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Machine Learning-Based Approaches to Forecasting Social, Economic, and Environmental Trends

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