Landfill area estimation based on solid waste collection prediction using ANN model and final waste disposal options

Public health of inhabitants has been affected by the increase of unsound waste management in the cities of developing countries. Solid waste management has received extensive attention from the municipalities. A sustainable plan and design of solid waste management system of a city necessitate accurate forecasting of the solid waste generated and collected by the city authorities in the landfill for final disposal and other renewable energy options. In the study, an Artificial Neural Network (ANN) surrogate model was used to predict solid waste collected from the year 2012-2016 at Matuail landfill site of Dhaka South City Corporation (DSCC). 120 monthly solid waste quantity and vehicle trip number getting from weighbridge were used as inputs data into the model. 70% of the data used for the model training, 15% of the data used for validation and 15% of that used for testing. The remaining 60 monthly waste quantities are used as output to develop the model. Feed-forward back propagation neural network was used with the hyperbolic tangent sigmoid activation function and the Levenberg-Marquardt optimization method. The model with 2-5-1-1 topology is selected as the best topology based on the performance metrics i.e., the minimum value of MSE and high value of regression. The ANN based solid waste forecasting model performed to be promising using the available weighbridge data with the coefficient of determination (R-2) for training and testing 0.85 and 0.86. The developed model can be alternatively used successfully with weight bridge software in the landfill to efficiently forecast solid waste collection, particularly that in the countries with a similar demographic and social environment. Considering other proposed alternative disposal options and waste characterization the required landfill area are estimated and found that the landfill authority can save the valuable urban landfill area requirement up to 28.6%. The result shows that the innovative proposed method of landfill area estimation by ANN and final disposal methods can be used alternatively that helps for better planning and management of the landfill site. (C) 2020 Elsevier Ltd. All rights reserved.