Sadegh Yaghoobzadeh

Feature engineering over complex architectures: A comparative analysis of machine learning models for short-term water demand forecasting

This study investigates the performance of six forecasting architectures to assess whether complex deep learning methods yield superior results compared with advanced ensemble approaches driven by rigorous, domain-informed feature engineering, and introduces a novel residual-engineered hybrid architecture.