Landfill sites suitability mapping utilizing artificial neural network

It is very crucial to have a precise suitability mapping workflow for new landfill sites in the development planning of municipal solid waste management systems. An appropriate siting of landfill sites will protect both environment and public health. However, the complexity in the process of suitability mapping that arises from the attempt to integrate information or decisions from different disciplines has affected the results and leads to an inefficient landfill siting model. In this study, the enhancement of the Landfill sites suitability mapping model was constructed to serve four purposes; (1) new workflow in creating suitability maps at the regional scale for solid waste planning based on neural network (NN); 2) a hybrid network that combines layer-recurrent network and cascade forward neural network to achieve high performance without requiring prior human knowledge; 3) a methodology for selecting the relevant input criteria for landfill GIS model based on multivariate analysis (MVA) methods for maximal performance; and 4) automating an ArcGIS neural network spatial data mining toolbox for mapping the suitability of landfill sites at a regional scale. A case study on landfill s