Scenario-Robust Hydropower Suitability Mapping in Geothermal Regions Using Multi-Paradigm Spatial Modeling
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Hydropower planning in geothermal and environmentally sensitive regions involves substantial uncertainty due to complex terrain, ecological constraints, and competing land-use priorities. This study aims to evaluate whether the suitability patterns of hydropower that are robust to planning assumptions can be identified through cross-method spatial consistency rather than single-model optimization. We propose a scenario-based spatial decision-support framework that integrates knowledge-driven multi-criteria decision analysis (MCDA), supervised machine learning (XGBoost), unsupervised RLKM, and patch-based convolutional neural networks (PCNN) using harmonized satellite-derived spatial datasets. Three alternative planning scenarios, balanced, conservation-oriented, and energy-priority, are implemented through consistent feature-weighting schemes applied across all analytical paradigms. The evaluation focuses on internal robustness indicators, including cross-method agreement, scenario sensitivity, and spatial coherence, rather than external field validation. The results show that supervised learning models exhibit high performance stability across scenarios, whereas PCNN substantially improves spatial coherence by reducing the fragmentation of suitable zones. The MCDA provides a transparent and spatially contiguous baseline, whereas the RLKM reveals scenario-sensitive intrinsic suitability regimes. Areas consistently identified as suitable across methods and scenarios represent high-confidence zones for screening-level planning, whereas scenario-dependent areas indicate elevated uncertainty. This framework advances hydropower suitability assessment toward transparent, risk-aware, and adaptive spatial decision support in complex geothermal environments by shifting emphasis from single-model accuracy to scenario robustness and cross-method synthesis.
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