# Environmental Data Analysis and Simulation

## Objectives

The goals of Environmental Data Analysis and Simulation are the following:

- Help structure the definition of environmental problems;

- Contribute for its solution conceptualizing simulation, optimization and decision models;

- Implement solutions using computational tools;

- Present solutions using animation and visualization methods.

## General characterization

10365

6.0

##### Responsible teacher

António da Nóbrega de Sousa da Câmara, Francisco Manuel Freire Cardoso Ferreira

Weekly - 5

Total - 90

Português

None

### Bibliography

- Antonio Camara, Environmental Systems, Oxford University Press, New York, 2002

- Afonso A., Nunes C., 2010. Estatística, Probabilidades: Aplicações e Soluções. Escolar Editora.

- Berthouex, P. M. and L.C. Brown, 1994. Statistics for Environmental Engineers, Lewis Publishers, Boca Raton, 335 pp.

- Gilbert, R.O., 1987. Statistical Methods for Environmental Pollution Monitoring, Van Nostrand Reinhold, New York.

- Montgomery, D.C. and Runger, G. C., 2002. Applied Statistics and Probability for Engineers, 3rd edition, John Wiley & Sons Inc., New York, 720 pp.

- Marôco, J., 2010. Análise Estatística com o PASW (ex-SPSS), Report Number

- Moore, D.S. and G.P. McCabe, 2002. Introduction to the Practice of Statistics, 4th edition, W.H. Freeman and Company, New York, 828 pp.

### Teaching method

Teaching is based on an immersion method where homeworks and projects are essential to complement classroom activity

### Evaluation method

This course does not have any exam and it is based in the effort developed along the semester. There is no obligatory attendance.  Final grade = 50% statistics component + 50% simulation/optimization component. Statistics component = individual test (25%) + group works (2) (25%); Simulation component = group works (2) (50%)

There is no minimum grade in any of the evaluation components. All grades acocunted, the grade should be at least 9,5 values.

Group works should have 2 or 3 members.

## Subject matter

- Introduction
- Data Collection - Sampling
- Data collection - sampling, spatial and temporal correlation
- Data visualization
- Processing of data in Excel and databases
- Linear Regression
- Analysis of variance
- Principal component analysis and time series
- Diagrams and causal modeling
- Basics of differential equations and application of the Runge-Kutta
- Network management
- Individual interaction and discussions with students
- Integrated approach to environmental problems

## Programs

Programs where the course is taught: