An integrated multiple layer perceptron-genetic algorithm decision support system for photovoltaic power plant site selection

There is a need for non-renewable energy sources in generation of power for almost every domestic and commercial purposes. This source of energy helps in the development of a country. Because of the increasing usage of the fossil fuels and depletion of these resources, our focus has been shifted towards the renewable sources of energy like solar, water and wind. Therefore, in the present scenario, the usage of renewable sources has been increasing rapidly. Selection of a solar power plant (SPP) requires environmental factor, local terrain, and local weather issues. Thus, a large amount of investment is required for installation. Multi-criteria decision making (MCDM) is a method that identifies one in choosing the best sites among the other proposed options. This paper gives a detailed study of optimal ranking of SPP site using analytical hierarchy process (AHP), multiple layer perceptron (MLP) neural network trained with back propagation (BP) algorithm and genetic algorithm (GA). Three SPP sites of India were considered and various important criteria like local weather, geographical location, and environmental factors are included in our study as SPP site selection is a multi-criteria problem. A precise comparison of these three methods is listed in this paper.

GIS-Based Decision Support System for Safe and Sustainable Building Construction Site in a Mountainous Region

The site selection process for a building entails evaluating a variety of factors with varying degrees of importance or percentage influence. In order to ensure that critical site selection factors are not overlooked, a methodology for calculating a building’s safe site selection must be developed. The study identified three broad aspects widely considered in site selection, namely environmental, physical, and socioeconomic criteria. To assess the safest site selection of residential building construction for sustainable urban growth, we used GIS-based multi-criteria decision-making approach that combined Fuzzy-AHP and weighted linear combination (WLC) aggregation method used to calculate the SSPZ. The final safe site suitability map was generated by aggregating all aspects such as geophysical, socio-economic and Geo-environmental thematic layers and their associated Fuzzy-AHP weights using the weighted linear combination method. The sites potential index’s mean value of 0.513 with standard deviation of 0.340, minimum and maximum GeoPhySSSI are 0.0 and 0.91, respectively, SSS index is classified into zones by histogram profile using natural breaks (jenks)” Subsequently, safe sites identified and divided into six classes namely no construction, very low suitable site low suitable site, moderate suitable site, high suitable site, and very high suitable site.“ According to the statistical analysis, 3.64% and 32.12% of the total area were under very high and high SSSZ, while 26.40% and 6.22% accounted to the moderate and low suitable potential, respectively” Our findings suggest that integrating the fuzzy collection with AHP is highly desirable in terms of alternative and decision-making effectiveness. The study reveals that the areas of high and moderate suitability are located near existing habitant area, major roads, and educational and health services; they are not located in restricted/protected areas or are vulnerable to natural hazards. The findings indicate that unsuitable and less-suitable land uses such as vegetation, protected areas, and agriculture lands cover nearly one-third area of Abha-Khamis Mushyet regions, implying that using Fuzzy-AHP and GIS techniques will significantly aid in the conservation of the environment. This would significantly mitigate adverse effects on the ecosystem and climate.