Skip to main content
. 2021 Mar 26;197:111015. doi: 10.1016/j.envres.2021.111015

Table 3.

Different technologies used for BWM.

Technologies used in SWM Methodology
Table 3 Different technologies used for BWM
Application References
Artificial Intelligence Artificial Neural Network (ANN) Prediction of bin level status,
Waste generation, waste classification, biogas generation, leachate formation, energy recovery, heating value, co-melting temperature of waste, and optimal waste collection routes.
Golbaz et al. (2019); Milojkovic et al. (2008); Noori et al. (2009); Shamshiry et al. (2011); Song et al. (2017).
Adaptive Neuro-Fuzzy Inference System (ANFIS) Used to forecast the waste generation in such developing countries where accurate reliable data is not always available Younes et al. (2015)
Genetic Algorithm (GA) Used for the identification of optimal routes, management costs in the case of MSW collection Yang et al. (2012); Meyer-Baese et al. (2014); Król et al. (2016)
Support Vector Machine (SVM) Prediction of bin level status, waste generation, classification, waste heating value and energy recovery. Chen et al. (2016); Harrington (2012); Dixon et al. (2008); Costa et al. (2018).
Solid waste Identification Barcode Waste disposal, reduce landfill space, intelligent recycling and risk management Greengard (2010); Saar et al. (2004).
Radio frequency identification (RFID) Bin tracking, sorting and recycling, driver tracking Ali et al. (2012); Hannan et al. (2015); Arebey et al. (2011).
SWM Data Acquisition Sensors Optimization, waste sorting, odor, moisture and energy measurement Lewis et al. (1992); Fuchs et al. (2008).
Imaging Waste sorting, routing and collection, monitoring and optimization Hannan et al. (2015); Arebey et al. (2011).
Data Communication Technologies GSM/GPRS, VHFR Long range communication Ali et al. (2012); Boustani et al. (2011).
Bluetooth, Wi-Fi Short range communication Chowdhury et al. (2007); Friedlos (2005).
Spatial Technologies Global Positioning System (GPS) Vehicle tracking, Route and collection optimization, planning, scheduling Minghua et al. (2009)
Geographic
Information Systems (GIS)
Site selection, planning, estimation, optimization and management Karadimas et al. (2008); Katpatal et al. (2011).
Remote Sensing (RS) Site selection, environmental impact assessment, features monitoring Zhao et al. (2005); Irvine et al. (1997).