Site selection of municipal solid waste incineration plant using GIS and multicriteria decision analysis.

Fast population growth and urbanization has increased the generation of municipal solid waste in Oman. Currently, Oman relies on landfills to handle growing amounts of waste, which has burdened the existing solid waste management infrastructure. This situation calls for establishing incineration plants that can considerably reduce the volume of waste, and can simultaneously produce electricity.

Predicting sanitary landfill leachate generation in humid regions using ANFIS modeling.

Landfill leachate is one of the sources of surface water pollution in Selangor State (SS), Malaysia. Leachate volume prediction is essential for sustainable waste management and leachate treatment processes. The accurate estimation of leachate generation rates is often considered a challenge, especially in developing countries, due to the lack of reliable data and high measurement costs.

Prediction of municipal solid waste generation using nonlinear autoregressive network.

Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society.

Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model.

Solid waste prediction is crucial for sustainable solid waste management. The collection of accurate waste data records is challenging in developing countries. Solid waste generation is usually correlated with economic, demographic and social factors.

Solid waste forecasting using modified ANFIS modeling.

Unlabelled: Solid waste prediction is crucial for sustainable solid waste management. Usually, accurate waste generation record is challenge in developing countries which complicates the modelling process. Solid waste generation is related to demographic, economic, and social factors.

Modeling of methane oxidation in landfill cover soil using an artificial neural network.

Knowing the fraction of methane (CH4) oxidized in landfill cover soils is an important step in estimating the total CH4 emissions from any landfill. Predicting CH4 oxidation in landfill cover soils is a difficult task because it is controlled by a number of biological and environmental factors. This study proposes an artificial neural network (ANN) approach using feedforward backpropagation to predict CH4 oxidation in landfill cover soil in relation to air temperature, soil moisture content, oxygen (O2) concentration at a depth of 10 cm in cover soil, and CH4 concentration at the bottom of cover soil.