A Neuro-Fuzzy Model for the Ecological Risk Assessment in Wetlands William Andrés Ocampo Duque (PhD Student) Dr. Marta Schuhmacher (Supervisor) Rovira.

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Presentation transcript:

A Neuro-Fuzzy Model for the Ecological Risk Assessment in Wetlands William Andrés Ocampo Duque (PhD Student) Dr. Marta Schuhmacher (Supervisor) Rovira I Virgili University School of Chemical and Process Engineering

Outline Motivation Problem Definition Objectives Work Plan

Lack of information about wetlands pollution Constrained budgets for Environmental Information Colombia has more than km 2 of Wetlands; only 3 with International Importance. Motivation

Pressures: Agriculture Industry Transport Cities A Watershed

Potential threats...

Pilot Study: Ebro Delta

Characteristics Surface: 320 km² Flow: 430m 3 /s Humid zone of international interest (Category A) Wide diversity of bird species (300 species) Important economic zone: Rice-growing area, vegetable crops and fruits Fishing A growing tourist industry

Main Problems Hydrologic deficit - Low flow Coastal Erosion. Deficit of Sediments. Salinity of Inland Waters. Excess of Nutrients. Pesticides and Heavy Metals in Waters. Poor Water Quality. Politic Stress. Unsustainable Management !!!

Possible Solutions A good definition of indicators. Improve quality of environmental data. Develop good tools for making-decisions. Ecological Awareness.

Objectives Develop a model for the ecological risk assessment in wetlands (Eco-Risk). Validate the model with a pilot study. Produce a tool for Making-decisions and Management.

Work Plan Problem Identification Effects Identification Extent of Exposure Identification Risk Identification Risk Management/ Risk Reduction Monitoring

Direct and riverine inputs of metals and organic substances in the NE Atlantic Ocean Indicators Indicator: Loads of hazardous substances to coastal waters ( ) Policy issue: Are direct and riverine loads to seas decreasing?

Why Neuro-Fuzzy tools? Ability to manipulate no-numerical and uncertain variables. Ability to generalise solutions. Ability to classify and quantify environmental effects of subjective nature. Ability to deal with missing data. Ability to integrate “scientific information” with “common knowledge”

Example of Fuzzy 1. Define water-quality parameters IF "Dissolved Oxygen is higher than 2.0" AND "pH is higher than 6.0" AND “pH is lower than 9.0” AND “BOD 5 is lower than 100” THEN "Water can be used for irrigation” 2. Define Classification Rules (Membership functions) 3. A “robust decision analysis” A  Water Supply B  Fisheries C  Industrial Supply D  Irrigation E  Environmental protection

Acknowledgements Programme Alban, the European Union Programme of High Level Scholarships for Latin America. Pontificia Universidad Javeriana.

Questions?