Optimizing Gas Production Forecasting in Iraq Using a Hybrid Artificial Intelligence Model

This paper aims to enhance the accuracy and reliability of gas production predictions for Iraq until 2030 by comparing traditional time series methods (ARIMA) with a hybrid artificial intelligence (AI) model that combines artificial neural networks (ANN) and genetic algorithms (GA). The integration of ANN and GA aims to leverage the strengths of pattern recognition and parameter optimization for more precise forecasting. The study’s objectives revolve around contributing to sustainable development goals within Iraq’s energy sector. The research methodology encompasses data collection, preprocessing, training ANN models, optimizing parameters using GA, and evaluating the hybrid approach’s performance. The results of this research will provide valuable insights for policymakers and industry stakeholders, empowering them to make informed decisions aligned with sustainable development objectives.