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Burak Can

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#explainable ai Open access Aug 2026

AI-Driven Geospatial Hydrology: Machine Learning and Decision Support for Surface Water Dynamics

Official Codebase Release (v1.0.1) Accompanying reproducible code, neural architectures, data pipelines, and decision-support tools for the Springer Nature Monograph: "AI-Driven Geospatial Hydrology: Machine Learning and Decision Support for Surface Water Dynamics" (2026) by Burak Can. Key Capabilities & Modules Earth Observation Pipelines: Cloud-native STAC metadata querying, COG windowed streaming, and 6S radiative transfer normalization (Chapters 1–2). Deep Vision for Water Extraction: Boundary-Aware ResUNet, Hierarchical SegFormer (MiT-B3), and multi-modal SAR-optical FCLS unmixing (Chapters 3–5). Hydrodynamic Sequence Forecasting: Differentiable Kling-Gupta Efficiency (KGE) optimization, Temporal Fusion Transformers, and physics-informed mass conservation loss ($\mathcal{L}_{\text{mass}}$) (Chapter 6). Explainable AI (XAI): Path-integral Integrated Gradients and SHAP feature attribution decoupling climatic stress from anthropogenic water extraction (Chapter 7). Cloud Microservices & Agentic Tools: Dynamic TiTiler tile streaming, PostGIS schemas, and Model Context Protocol (FastMCP) server implementations (Chapters 8–10). Citation @book{can2026geospatial, title = {AI-Driven Geospatial Hydrology: Machine Learning and Decision Support for Surface Water Dynamics}, author = {Can, Burak}, year = {2026}, publisher = {Springer Nature} }

Burak Can · 0 citations
#explainable ai Open access Aug 2026

AI-Driven Geospatial Hydrology: Machine Learning and Decision Support for Surface Water Dynamics

Official Codebase Release (v1.0.1) Accompanying reproducible code, neural architectures, data pipelines, and decision-support tools for the Springer Nature Monograph: "AI-Driven Geospatial Hydrology: Machine Learning and Decision Support for Surface Water Dynamics" (2026) by Burak Can. Key Capabilities & Modules Earth Observation Pipelines: Cloud-native STAC metadata querying, COG windowed streaming, and 6S radiative transfer normalization (Chapters 1–2). Deep Vision for Water Extraction: Boundary-Aware ResUNet, Hierarchical SegFormer (MiT-B3), and multi-modal SAR-optical FCLS unmixing (Chapters 3–5). Hydrodynamic Sequence Forecasting: Differentiable Kling-Gupta Efficiency (KGE) optimization, Temporal Fusion Transformers, and physics-informed mass conservation loss ($\mathcal{L}_{\text{mass}}$) (Chapter 6). Explainable AI (XAI): Path-integral Integrated Gradients and SHAP feature attribution decoupling climatic stress from anthropogenic water extraction (Chapter 7). Cloud Microservices & Agentic Tools: Dynamic TiTiler tile streaming, PostGIS schemas, and Model Context Protocol (FastMCP) server implementations (Chapters 8–10). Citation @book{can2026geospatial, title = {AI-Driven Geospatial Hydrology: Machine Learning and Decision Support for Surface Water Dynamics}, author = {Can, Burak}, year = {2026}, publisher = {Springer Nature} }

Burak Can · 0 citations