Regularized Noise based GRU Model to Forecast Solid Waste Generation in the Urban Region

Abstract

Today’s Solid Waste Management System needs better strategies to perform effective operations. The speed at which waste is being generated in urban regions is directly affecting the surrounding environment impacting the health of living beings. Advancements in deep learning has made time series forecasting very efficient making better predictions. Recurrent Neural Network (RNN) is a deep learning approach which helps in analyzing and forecasting time series data by holding the complication of sequence dependencies. In this study, a RNN model, Regularized Noise based Gated Recurrent Unit (RNGRU) has been proposed to analyze and predict solid waste generation in Australia. The dataset is collected from www. oecd.org which has waste data 20780 records from 1990 to 2018. Various input features such as Region name, Year, Waste generated in tonnes and technical indicators such as Moving Average (MA), Exponential Moving Average (EMA), Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), On Balance Volume (OBV), Momentum (MTM), Daily waste variation are used to train the model. The proposed RNGRU model is compared with LSTM model and the best model is chosen based on the performance metrics, Mean Absolute Error(MAE), Mean Squared Error (MSE), and Root Mean Squared Error(RMSE). The outcome of the experiments shows that the RNGRU model is best for prediction with low error rates, MAE value 0.0147, MSE value 0.0010 and RMSE value 0.0900 respectively.