An intelligent algorithm for accurate forecasting of short term solid waste generation

Abstract: Municipal solid waste management has become a global concern during the past decades in many countries such as Canada and waste management technological advancements and regula-tions have been increased. Solid wastes emit greenhouse gases which result in global climate change, pollution of air and water which has tremendous negative impact on human health. Due to the excessive urbanization and fast economic development, municipal solid wastes have been increased in developing countries. In order to manage this emerging issue, polluted countries need a series of legislations and policies toward solid wastes. Accurate prediction of future mu-nicipal solid waste generation plays a critical role for future planning. This paper focuses on mu-nicipal solid waste generation in city of Tehran, the most populated city in Middle East. Three methods are explored in this paper to analyze the past solid waste time-series analysis: regres-sion, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The first method, which is the classical regression approach, is used as a baseline for considered neural networks models. The second method utilizes the past data as training example of neural network to find autocorrelation among target; lastly, the neuro-fuzzy learns the relation of data using fuzzy-rule. Mean Absolute Percentage Error (MAPE) metric is used to evaluate the per-formance. Finally, analysis of variance (ANOVA) and Duncan experiment are performed to ver-ify and validate the outcome of the experiments.

How to cite this paper

 Fathollahi, M., Farsani, S & Azadeh, A. (2017). An intelligent algorithm for accurate forecasting of short term solid waste generation.International Journal of Data and Network Science, 1(2), 59-68.

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