Şentürk, Zehra Karapınar2023-07-262023-07-2620221574-95411878-0512https://doi.org/10.1016/j.ecoinf.2022.101640https://hdl.handle.net/20.500.12684/12286Planning cities regardless of natural resources has led to the existence of threats for several species that occupy an important place in local ecosystems. Constructing buildings, bridges, or motorways over water reservoirs is perilous for resident amphibians, which belong to one of the most fragile animal groups. An automated system that reliably identifies whether any amphibian species live in a water reservoir before construction in that region will help building inspectors during city planning and will ensure that citizens can enjoy the opportunities provided by nature. In this paper, an intelligent amphibian species detection system is developed using features extracted from GIS and satellite images. The presence of seven amphibian species is detected using an artificial neural network (ANN). Depending on the spatial features of the region, the proposed cascade-forward backpropagation neural network (CFBNN) model, a special type of ANN, determines which amphibian species live there to enable ecological precautions to be taken before construction starts and to preserve the biodiversity for the future health of the community. The results clearly demonstrate that the proposed approach significantly outperforms recently suggested strategies with more than 15% improvement on average. The performance of the system shows that the proposed detection approach can be a useful tool for smart and ecological city planning.en10.1016/j.ecoinf.2022.101640info:eu-repo/semantics/closedAccessArtificial Neural Networks; Amphibians; Biodiversity; Conservation; City Planning; Machine Learning; Water ReservoirsArticle692-s2.0-85128320805WOS:000802217900001Q1Q1