Mohammad K. Younes

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. The optimum ANN model giving the lowest mean square error (MSE) was configured from three layers, with 12 and 9 neurons at the first and the second hidden layers, respectively, log-sigmoid (logsig) transfer function at the hidden and output layers, and the Levenberg-Marquardt training algorithm. This study revealed that the ANN oxidation model can predict CH4 oxidation with a MSE of 0.0082, a coefficient of determination (R 2) between the measured and predicted outputs of up to 0.937, and a model efficiency (E) of 0.8978. To conclude, further developments of the proposed ANN model are required to generalize and apply the model to other landfills with different cover soil properties. Implications: To date, no attempts have been made to predict the percent of CH4 oxidation within landfill cover soils using an ANN. This paper presents modeling of CH4 oxidation in landfill cover soil using ANN based on field measurements data under tropical climate conditions in Malaysia. The proposed ANN oxidation model can be used to predict the percentage of CH4 oxidation from other landfills with similar climate conditions, cover soil texture, and other properties. The predicted value of CH4 oxidation can be used in conjunction with the Intergovernmental Panel on Climate Change (IPCC) First Order Decay (FOD) model by landfill operators to accurately estimate total CH4 emission and how much it contributes to global warming.

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. However, these factors are not constant due to population and economic growth. The objective of this research is to minimize the land requirements for solid waste disposal for implementation of the Malaysian vision of waste disposal options. This goal has been previously achieved by integrating the solid waste forecasting model, waste composition and the Malaysian vision. The modified adaptive neural fuzzy inference system (MANFIS) was employed to develop a solid waste prediction model and search for the optimum input factors. The performance of the model was evaluated using the root mean square error (RMSE) and the coefficient of determination (R(2)). The model validation results are as follows: RMSE for training=0.2678, RMSE for testing=3.9860 and R(2)=0.99. Implementation of the Malaysian vision for waste disposal options can minimize the land requirements for waste disposal by up to 43%.

Solid waste forecasting using modified ANFIS modeling

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. However, these factors are highly varied due to population and economy growths. The objective of this research is to determine the most influencing demographic and economic factors that affect solid waste generation using systematic approach, and then develop a model to forecast solid waste generation using a modified Adaptive Neural Inference System (MANFIS). The model evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R2). The results show that the best input variables are people age groups 0-14, 15-64, and people above 65 years, and the best model structure is 3 triangular fuzzy membership functions and 27 fuzzy rules. The model has been validated using testing data and the resulted training RMSE, MAE and R2 were 0.2678, 0.045 and 0.99, respectively, while for testing phase RMSE =3.986, MAE = 0.673 and R2 = 0.98. Implications: To date, a few attempts have been made to predict the annual solid waste generation in developing countries. This paper presents modeling of annual solid waste generation using Modified ANFIS, it is a systematic approach to search for the most influencing factors and then modify the ANFIS structure to simplify the model. The proposed method can be used to forecast the waste generation in such developing countries where accurate reliable data is not always available. Moreover, annual solid waste prediction is essential for sustainable planning.

Medium term municipal solid waste generation prediction by autoregressive integrated moving average

Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.

Priorities determination using novel analytic hierarchy process and median ranked sample set, case study of landfill siting criteria

Integrating environmental, social, political, and economical attributes enhances the decision making process. Multi criteria decision making (MCDM) involves ambiguity and uncertainty due to various preferences. This study presents a model to minimize the uncertainty and ambiguity of human judgments by means of integrating the counter stakeholders with median ranked sample set (MRSS) and Analytic hierarchy process (AHP). The model uses landfill site selection as a MCDM problem. Sixteen experts belong to four clusters that are government, private, institution, and non-governmental organisations participated and their preferences were ranked in four by four matrix. Then the MRSS and the AHP were used to obtain the priorities of landfill siting criteria. Environmental criteria have the highest priority that equals to 48.1% and the distance from surface water, and the faults zones are the most important factors with priorities equal to 18% and 13.7% respectively. In conclusion, the hybrid approach that integrates counter stakeholders MRSS, and AHP is capable of being applied to complex decision making process and its outputs are justified.

Use of a Combination of MRSS-ANP for Making an Innovative Landfill Siting Decision Model

Landfill siting is a complex, multicriteria decision-making problem that needs an extensive evaluation of environmental, social, land use, and operational criteria. Integration of a median ranked sample set (MRSS) and an analytic network process (ANP) has been implemented to rank the associated criteria and select a suitable landfill site. It minimizes the uncertainty and the subjectivity of human judgments. Four groups of experts with different backgrounds participated in this study, and each group contained four experts. The respondent preferences were ranked in a 4-by-4 matrix to obtain the judgment sets for the MRSS. These sets were subsequently analyzed using ANP to obtain the priorities in the landfill siting criteria. The results show that land topology and distance from surface water are the most influential factors, with priorities of 0.18 and 0.17, respectively. The proposed integrated model may become a promising tool for the environmental planners and decision makers.

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. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R2) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R2 (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell’s conjugate gradient as the training algorithm.