fuzzy inference system

In addition to rich resources of fossil fuels, Iran has a lot of renewable energy potential. On the other hand, considering the climate diversity in the country and the natural conditions and potentials in different regions, instead of national planning, we should move towards regional energy planning and develop a regional renewable energy portfolio. In the present research, firstly, the potential measurement criteria of different types of renewable energy, including solar, wind, geothermal, hydroelectric and biomass, based on geographic information system maps and data received from the Meteorological Organization and SATBA, for 1361 latitudes and longitudes, has been scored. Then, using the Rapidminer software, the geographic points were divided into 5 clusters, each cluster includes areas of equal potential with the greatest similarity. Two of these 5 clusters are considered to be among the dry lands of the country. Then, based on the review of library resources and usage from the opinions of SATBA experts (Renewable Energy Research Group), a fuzzy inference model based on 5 sustainable development criteria including: access to technology, investment costs, capital productivity, employment rate, and environmental consequences along with design potential measurement criteria and based on the fuzzy rules defined on these criteria. The percentage share of each type of energy in the energy portfolio of each cluster was calculated. In the final step, based on demographic criteria including unemployment rate, population growth rate, acceptance culture (literacy rate), investment security, to prioritize clusters for strategic planning of the government and other influential institutions such as governorates, municipalities and chambers of commerce. For example, in cluster 4, includes some cities in the provinces of Isfahan, Khorasan, Yazd, Kermanshah, Fars, and Kohkiloyeh, which are classified as arid and semi-arid regions of the country according to the criteria of potential measurement and development criteria that has an energy portfolio with 25% share of wind energy, 39% share of solar energy, 10% share of hydroelectric energy and 26% share of biomass energy. Population growth and investment security are the first priority.

Several modelling techniques have been developed for habitat suitability modelling. In the meantime, the Fuzzy Inference System (FIS) with ability to model uncertainty of input variables is an effective method to model wildlife species habitat suitability. So, Persian Leopard habitat suitability was predicted in Shimbar Protected Area using FIS. Therefore, the effective environmental variables were determined. We also defined and determined the linguistic variables, linguistic values, and range of them. Then, we designed the membership functions of the fuzzy sets of the input and output variables. Also, the definition of the fuzzy rules in the system was performed. Finally, the defuzzification of output was carried out. The accuracy of the predictive model was tested using AUC. Also, 11 FISs were developed to determine sensitivity of the models and important variables in modelling. The results showed that the predictive model was more efficient than the random model (AUC=0.960). In addition, the ‘distance to capra’ was the most important predictor. According to the success of FIS in Persian Leopard habitat suitability modelling, we suggest this method to improve and complete the existing spatial information of wildlife habitats in Iran, especially about regions and species that have been less studied.