An Enhanced GRU model with Optimized Update Gate: A Novel Approach in Municipal Solid Waste Prediction

The management of Municipal Solid Waste (MSW) establishes a decisive facet in the development of sustainable and vigorous societies that requires an adequate framework in order to prevent a risk of environmental pollution.Therefore, it's imperative to establish a robust framework to handle the generated MSW.In this regard, early predictions of waste generation with high accuracy emerges as a pivotal factor in serving the municipal authorities to formulate an effective MSW management system.Various researchers have significantly contributed to the field of early waste predictions by developing different deep learning models to attain the highly accurate predicted results.However, it is important to note that each model has its strengths and limitations.This study has predominantly focused on the enhancement of the Gated Recurrent Unit (GRU) model.The primary limitation in the standard GRU model lies in both of its gates (reset and update) processing the identical data.This redundancy has contributed to a prolonged training time and a diminished convergence rate.Therefore, this study has proposed an enhanced GRU model with optimized update gate (EGRU-OU) to address the redundancy issue that lies between the two gates.The EGRU-OU model will provide the filtered data specifically to the update gate which is instrumental in significantly reducing the redundant information between the two gates.Moreover, this study has employed two different datasets to conduct a comprehensive analysis of the results.One dataset has been collected for 16 different developed countries, whereas, the other dataset has been collected for Multan City, Pakistan.These datasets have been segmented into three subdivisions: training data, constituting 70% of the dataset for model training; testing data, comprising 15% for model evaluation; and validation data, representing the remaining 15% for additional model validation.In addition, The EGRU-OU model has been compared with other benchmark models including standard GRU model, Long Short Term Memory (LSTM) and Artificial Neural Network (ANN) model based on three distinct error metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R).The outcomes have clearly demonstrated a superior performance of the EGRU-OU approach as compared to other models with the least error matrices values of MAE being 0.036 and RMSE being 0.0684.