Soft Computing Applications in Municipal Solid Waste Forecast: A Short Review

In recent times, there have been studies focusing on soft computing applications in computer and information science, etc. There are currently some articles on forecasting municipal solid waste (MSW) generation using machine learning (ML) and other artificial intelligence (AI) techniques; however, there has never been a book or chapter that has focused on soft computing applications in MSW. In view of that, this chapter focused at usage of soft computing in predicting MSW quantity. AI and ML techniques were thoroughly reviewed. Significant analysis of eight existing models [linear regression, time series, artificial neural network (ANN), supported vector machine, adaptive neuro-fuzzy inference system, decision tree (DT), gradient boosted regression tree (GBRT), and k-nearest neighbors (k-NNs)], including their hybrids, was carried out. After thorough investigation, ANN was found out to be the most used method in MSW generation, while k-NN, DT, and GBRT were the least employed methods.