Artificial Intelligence and Machine Learning Methods for Environmental Applications

ABSTRACT

COURSE CONTENT

Part 1: THE "THREE-STEPS LEARNING-BY-EXAMPLES (LBE)" FRAMEWORK

• Overview, general concepts, and taxonomy of Machine Learning (ML) methodologies
• Interpolation techniques
• Dimensionality reduction methodologies
• Space exploration/sampling methodologies
• Classification and regression methodologies

Part 2: APPLICATIONS OF AI AND ML IN ENVIRONMENTAL ENGINEERING

• Overview of AI and ML methods for environmental applications:
  • Water quality detection
  • Hydrology and prediction/optimization of water resource availability
  • Prediction of lake surface temperature
  • Environmental remote sensing
  • Soil science and agriculture

TEACHING ACTIVITIES

• Theoretical Lessons
• e-Xam Self Assessment (each teaching class or periodically)
• MATLAB Hands-On
• e-Xam Final Assessment

FURTHER READINGS

1. M. Z. Naser, Machine Learning for Civil & Environmental Engineers. Wiley, 2023.
2. S. Araghinejad, Data-driven modeling: using MATLAB in water resources and environmental engineering. Springer, 2014.
3. A. I. J. Forrester, A. Sobester, and A. J. Keane, Engineering Design via Surrogate Modelling: A Practical Guide. Hoboken, N.J.: John Wiley & Sons, 2008.
4. T. Hastie, J. Friedman, and R. Tisbshirani, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer, 2017.
5. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 2000.