Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation

The use of machine learning techniques in waste management studies is increasingly popular. Recent literature suggests k-fold cross validation may reduce input dataset partition uncertainties and minimize overfitting issues. The objectives are to quantify the benefits of k-fold cross validation for municipal waste disposal prediction and to identify the relationship of testing dataset variance on predictive neural network model performance. It is hypothesized that the dataset characteristics and variances may dictate the necessity of k-fold cross validation on neural network waste model construction. Seven RNN-LSTM predictive models were developed using historical landfill waste records and climatic and socio-economic data. The performance of all trials was acceptable in the training and validation stages, with MAPE all less than 10%. In this study, the 7-fold cross validation reduced the bias in selection of testing sets as it helps to reduce MAPE by up to 44.57%, MSE by up to 54.15%, and increased R value by up to 8.33%. Correlation analysis suggests that fewer outliers and less variance of the testing dataset correlated well with lower modeling error. The length of the continuous high waste season and length of total high waste period appear not important to the model performance. The result suggests that k-fold cross validation should be applied to testing datasets with higher variances. The use of MSE as an evaluation index is recommended.