A hybrid ANN–AHP–GIS framework with dimensionality reduction and uncertainty quantification for solar site selection in Southern India

• develop a hybrid scoring system that integrates ANN and AHP techniques. This will involve unweighted and weighted score fusion, with AHP contributing 40% and ANN contributing 60% for site suitability evaluation. • Incorporate GIS within the hybrid framework enabling spatially explicit assessment, integration of multiple data layers, visualization, and support for data-driven decision-making. • Apply PCA and t-SNE for dimensionality reduction and identification of latent patterns, and validate model concordance with Spearman, Pearson, and Kendall correlation metrics. • Classify sites from A + to D and visualize the tradeoffs using radar charts, 3D scatter plots, and heat maps. • Automate the comprehensive decision support system designed for utilization by policymakers, planners, and investors. This study presents a novel hybrid framework for assessing solar energy feasibility across nineteen sites in Southern India by combining artificial neural networks (ANN) and the analytic hierarchy process (AHP). Using a 40:60 weighting, the model integrates expert-driven AHP and data-driven ANN scores, demonstrating 85 % ranking stability across different settings, indicating a robust and reliable site prioritization that remains consistent despite input variability through Monte Carlo simulations. Nine spatial criteria, including solar irradiation (4–7 kW/m 2 ), land cost variability (±12 %), grid proximity, unused land, land slope, land area, ecological impact, population density, and future energy demand, are incorporated into actionable suitability maps using geographic information systems (GIS). Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) diminish dimensionality, encapsulating 94 % of data variance, thereby facilitating the simplification of intricate criteria for enhanced interpretability without substantial information loss and uncovering latent patterns in site suitability. Robust concordance among scoring systems is validated by Spearman, Pearson, and Kendall correlation analyses (Pearson > 0.99). The framework also includes uncertainty quantification, modeling variance in input data (e.g., ±5% solar irradiation) and ANN prediction uncertainty (±0.03), producing 95 % confidence intervals for site rankings. Among the top-ranked sites are Vizag, Guntur, and Srikakulam. The hybrid technique enhances classification accuracy by 22 % compared to individual models. Three-dimensional scatter plots, heat maps, and radar charts, among other visualization methods, illustrate the tradeoffs between land cost, environmental impact, and infrastructural accessibility. The fully automated MATLAB framework offers policymakers a swift, reproducible, and scalable decision-support tool for efficient, transparent, and risk-informed solar site selection aligned with national energy objectives.