Matches in SemOpenAlex for { <https://semopenalex.org/work/W3201733408> ?p ?o ?g. }
- W3201733408 endingPage "304" @default.
- W3201733408 startingPage "289" @default.
- W3201733408 abstract "Abstract The generation of municipal solid waste (MSW) is a multivariable-based process, and therefore its quantification is relatively difficult. Proper management of MSW is one of the main concerns of all the metropolitan cities and hence its successful management plan is inevitable. A very first step toward this is the precise and error-free estimation of garbage generation. Accurate and consistent anticipation of the measure of waste will allow proper designing and functioning of an effective waste collection system. The chapter compares various artificial intelligence models to observe and assess their ability to predict the quantity of waste being produced. Artificial neural network (ANN), adaptive neuro-fuzzy inference system, genetic algorithm–ANN, and adaptive neuro-fuzzy inference show good performance toward prediction. These machine learning algorithms constantly forecast monthly generation of waste by training with time series. The models dealt in this chapter validate the possibility of creating tools that help in early planning of managing the future waste estimates by means of source separation, processing, and integration of publically available data from various sources toward smart waste management." @default.
- W3201733408 created "2021-10-11" @default.
- W3201733408 creator A5004406731 @default.
- W3201733408 creator A5051295588 @default.
- W3201733408 creator A5055474883 @default.
- W3201733408 creator A5058394213 @default.
- W3201733408 date "2021-01-01" @default.
- W3201733408 modified "2023-10-16" @default.
- W3201733408 title "Artificial Intelligence Models for Forecasting of Municipal Solid Waste Generation" @default.
- W3201733408 cites W1567158111 @default.
- W3201733408 cites W1965982810 @default.
- W3201733408 cites W1969002952 @default.
- W3201733408 cites W1980907673 @default.
- W3201733408 cites W1981816266 @default.
- W3201733408 cites W1982969742 @default.
- W3201733408 cites W1986140246 @default.
- W3201733408 cites W1992371917 @default.
- W3201733408 cites W1997016663 @default.
- W3201733408 cites W2005423665 @default.
- W3201733408 cites W2006298291 @default.
- W3201733408 cites W2010222480 @default.
- W3201733408 cites W2015063326 @default.
- W3201733408 cites W2019207321 @default.
- W3201733408 cites W2022106816 @default.
- W3201733408 cites W2026755748 @default.
- W3201733408 cites W2027789615 @default.
- W3201733408 cites W2029513327 @default.
- W3201733408 cites W2032135645 @default.
- W3201733408 cites W2035123394 @default.
- W3201733408 cites W2037180893 @default.
- W3201733408 cites W2038473732 @default.
- W3201733408 cites W2038858133 @default.
- W3201733408 cites W2039385967 @default.
- W3201733408 cites W2040219465 @default.
- W3201733408 cites W2042625367 @default.
- W3201733408 cites W2049118732 @default.
- W3201733408 cites W2059717829 @default.
- W3201733408 cites W2062863528 @default.
- W3201733408 cites W2069604986 @default.
- W3201733408 cites W2069942641 @default.
- W3201733408 cites W2073524950 @default.
- W3201733408 cites W2085030517 @default.
- W3201733408 cites W2094793254 @default.
- W3201733408 cites W2110983336 @default.
- W3201733408 cites W2114074267 @default.
- W3201733408 cites W2133649387 @default.
- W3201733408 cites W2140476011 @default.
- W3201733408 cites W2142827986 @default.
- W3201733408 cites W2144091775 @default.
- W3201733408 cites W2145884063 @default.
- W3201733408 cites W2166186246 @default.
- W3201733408 cites W2167205116 @default.
- W3201733408 cites W2182833538 @default.
- W3201733408 cites W2269011705 @default.
- W3201733408 cites W2288377653 @default.
- W3201733408 cites W2318428678 @default.
- W3201733408 cites W2320098985 @default.
- W3201733408 cites W2335794719 @default.
- W3201733408 cites W2412393926 @default.
- W3201733408 cites W2521799132 @default.
- W3201733408 cites W2538533766 @default.
- W3201733408 cites W2551291808 @default.
- W3201733408 cites W2561317421 @default.
- W3201733408 cites W2587480881 @default.
- W3201733408 cites W2613713344 @default.
- W3201733408 cites W2736678235 @default.
- W3201733408 cites W2767049575 @default.
- W3201733408 cites W2771606018 @default.
- W3201733408 cites W2790274496 @default.
- W3201733408 cites W2793604280 @default.
- W3201733408 cites W2796730372 @default.
- W3201733408 cites W2800142469 @default.
- W3201733408 cites W2810743723 @default.
- W3201733408 cites W2886001132 @default.
- W3201733408 cites W2899218398 @default.
- W3201733408 cites W2900122422 @default.
- W3201733408 cites W2900417912 @default.
- W3201733408 cites W2902098804 @default.
- W3201733408 cites W2909358758 @default.
- W3201733408 cites W2911315776 @default.
- W3201733408 cites W2917171524 @default.
- W3201733408 cites W2922528255 @default.
- W3201733408 cites W2923798910 @default.
- W3201733408 cites W2977783280 @default.
- W3201733408 cites W2994942052 @default.
- W3201733408 cites W3001585636 @default.
- W3201733408 cites W3024048126 @default.
- W3201733408 cites W3042119478 @default.
- W3201733408 cites W3093157906 @default.
- W3201733408 cites W3123511113 @default.
- W3201733408 cites W4236993164 @default.
- W3201733408 cites W4242972754 @default.
- W3201733408 cites W4250664506 @default.
- W3201733408 cites W4251235113 @default.
- W3201733408 doi "https://doi.org/10.1016/b978-0-12-824463-0.00019-7" @default.
- W3201733408 hasPublicationYear "2021" @default.
- W3201733408 type Work @default.
- W3201733408 sameAs 3201733408 @default.