Development of environmental data analysis and model building skills using common mathematical and statistical tools. Matlab is the preferred development platform.
Bates, D., Watts, D. (1988). Nonlinear regression analysis and its applications. John Wiley & Sons, New York, pp. 365.
Haefner, J.W. (2005). Modeling biological systems, Principles and applications. Springer, New York, pp. 475.
Marsili-Libelli S. (1989). Modelli matematici per l’ecologia. Pitagora Ed. Bologna, pp. 457.
Witten I.H., Frank E. (2005). Data Mining: Practical Machine Learning Tools and Techniques (Second Ed.). Morgan Kaufmann Elsevier, Amsterdam, pp. 525.
Learning Objectives
Acquire the main numerical techniques for the analysis of environmental data and for environmental model building
Prerequisites
Basic knowledge of calculus, linear algebra, statistics.
Teaching Methods
Room lectures using proprietary slides and Matlab scripts.
Further information
The course material is sent to the students via email prior to each lecture. All the teaching material is also published in the teacher’s personal web site at
The final exam consists of an interview on the course subjects. This can be integrated on a voluntary basis by a project on a topic chosen by the student.
Course program
Time-series analysis in time and frequency, detrending, outlier detection, smoothing and denoising by either splines or wavelets. Time-series synthesis for simulation.
Principal component analysis: cross-correlation suppression, denoising, dimension reduction. Application to water quality data.
Bayesian prediction of qualitative and/or numeric data.
Decision trees based on either qualitative and/or numeric data. Application to the prediction of flood routing and lake stratification.
Fuzzy logic: fuzzification, fuzzy inference, defuzzification. The Mamdani and the Sugeno inferential schemes. Fuzzy modelling of dynamic systems. Fuzzy clustering for environmental data analysis: Euclidean metrics methods (Fuzzy C- means) and variable metrics methods (Gustafson-Kessel e Fuzzy Maximum Likelihood Estimation). Application to the detection of malfunctions in an anaerobic digester.
General rules for model building. Distinction between theory-driven (first principles) and data-driven models. Data requirements. Example of fitting a numerical model in the wider DPSIR scheme (European Water Directive 60/2000). Survey of several simple environmental models to be used for parameter calibration.
Parameter calibration: sensitivity analysis (static and dynamic). Gradient-free optimization methods: optimal flexible polyhedron search. Model validation: parametric and non-parametric tests. Confidence regions of estimated parameters.