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DSS (Decision Supporting System)

The decision supporting system (DSS) software was developed from a conceptual model, processed for the Ministry of the Environment by a group of experts in 2003, and subsequently translated by experts from the University Parthenope in a mathematical model based on fuzzy logic.

The original conceptual model  (as described in the C1 Action) is based on the assumption that the occurrence of a risk, associated with the release of a GMO into the environment, is closely linked to the presence of four components and their interrelationships:
SOURCE    DISSEMINATION FACTORS    MIGRATION PATHS    RECEPTORS
Where:
Source is the site where the GMO is released and/or is capable of expressing its harmful characteristics;
Dissemination factors are related to the biological characteristics of the migration paths and chemical, physical and biological properties of the receiving environment;
Receptors are organisms and ecosystems.

 

According to the methodological proposal , the user, answering to specific groups of questions, can follow a path made as a decision tree, starting from the source, which leads to the organisms and ecosystems potentially affected and to the identification of impacts (if present). The original conceptual model has been integrated to include the ability to select the standard procedures for risk management strategies useful to minimize potential adverse effects on biodiversity and on appropriate indicators selected for monitoring of potential impacts. The DSS is built in order to help the user to choose between the options for mitigation measures available for the specific case. The project team has reviewed the current mitigation measures in existing field-test scenarios in the experimental areas chosen within the project (see Figure 1 and 2). For example, at the local level have been verified safe distances from GM crops, and localized and mapped potentially interfertile species. The final model thus allows to answer the following questions:

- What are the new features of the GMP?

- What are the interactions between GMP and environment?

- What are the receptors present in the release site?

- In the given conditions which are the exposed receptors?

- What is the level of exposure?

 

- What are the potential effects on receptors?

The conceptual model was subsequently translated into a mathematical model, which led to the development of the software. Its requirements are:

- Inferential web-based database;

- Fluency of compilation;

- Ability to access information previously entered into the database;

- Estimate of the potential risks;

- Possibility to weigh the effects of any mitigation measures applied by the operator.

This "translation" was done using fuzzy logic by mathematical and statistical experts from the University Parthenope of Naples. The system looks like a fuzzy inferential model and the main purpose is making the system closer to human reasoning. In fuzzy logic, concepts that are too complex or imprecise to be handled with traditional instruments, can be studied through a linguistic approach, where natural language words or phrases are used instead of numbers. In other words, the linguistic approach sacrifices some accuracy in favor of the meaning. In fuzzy logic, basic tools are the linguistic variables, that are those variables whose values ​​are not represented by numbers, but by words or phrases expressed in natural language. The linguistic variables can be combined in a set of rules IF-THEN to achieve the inference, constructed through appropriate logical operators (rules). To follow a kind of approximate reasoning and provide quantitative measures associated with the risks it was necessary to parameterize the concepts associated with each question derived from the conceptual model through fuzzy sets. Every single answer to a question can be described by a "degree" of belonging to a certain concept. For example, the "contamination of the supply chain" can belong with a certain degree to the high set rather than medium or than low. Depending on the degree of membership of the individual answers we can use inference to identify new questions and then choose different paths.

The system structured in this way allows to identify and quantify the potential impacts resulting from the deliberate release of a specific GMP, which can reach one or more receptors through a pre-established set of migration routes. The paths are defined by a set of questions formulated with special tabs and organized in a questionnaire. The user can observe the risks, corresponding to the potential impacts, expressed by linguistic variables such as Low, Medium, High, and organized report.

The testing phase is over and the software, in its user friendly graphical interface, is available on the server owned by ISPRA at the link http://www.ogm-dss.isprambiente.it/. By connecting to the link above, you will see the home page (see Figure 3). The user can choose to access the system or to register in case of first access. The menu options presented are: display of the user guide (also reported in the links at the bottom of this page); edit of the user profile; computing of the questionnaire; archive of the fulfilled questionnaires. In particular, in "Create questionnaire", if it is the first time you log in to this function, or if in the previous session the questionnaire has been fulfilled, you start with a new questionnaire, but if in the last session the questionnaire was not completed, either intentionally or unintentionally (eg . due to network failures), the software resumes the compilation from the point where it had been interrupted. The software is structured in such a way that the computing is very simple. The types of answers related to the different questions proposed are: text, date, numeric, linguistic, multiple, tabular, geographical and links. After answering the questions in the questionnaire you can display a summary final report (see Figures in the links below) consisting of three parts: questions, tables, risks.

 

Further information:

Figure 1. Flowchart of maize

Figure 2. Flowchart of oilseed rape

Figure 3. DSS Home page

Figure 4. Insert data

Figure 5. Insert numerical answer

Figure 6. Insert linguistic answer

Figure 7. Insert multiple answer

Figure 8. Insert tabular answer

Figure 9. Insert geographical answer

Figure 10. Insert link

Figure 11. Final report: questions

Figure 12. Final report: risks

User guide

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