Introduction to Artificial Intelligence with Project Ideas and Practice Problems
This open-access short-term course introduces the theory and application of Artificial Intelligence in Water Resource Engineering and Hydroinformatics — from neural network fundamentals to nature-inspired algorithms and real-world project case studies.
Structured into 4 sections and 28 modules, the course covers:
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🧠 Artificial Neural Networks (ANN) — Architecture, training algorithms (Conjugate Gradient Descent, Newton's Method, Quasi-Newton, Levenberg–Marquardt), PNN, GMDH, performance metrics, and no-code software tools
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🌿 Nature-Inspired Optimization — Genetic Algorithm, Glowworm, Mine Burst, Water Cycle, Cuckoo Search, Dolphin Echolocation, and Fish Swarm algorithms
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🧬 Genetic Algorithm & MCDM Applications — MCDM fundamentals combined with GA-based decision-making
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📂 AI Case Studies — Real project applications in water allocation, penstock monitoring, rain-water harvesting site selection, and wetland risk minimization