comparative study of predicting hospital solid waste generation using multiple linear regression and artificial intelligence

A successful hospital solid waste (hsw) management needs an accurate estimation of waste generation rates. the conventional regression methods upon increasing the number of input variables hardly can predict the hsw generation rate and require more complex modeling. in return, application of machine learning methods seems to be able to increase the power of predicting the produced wastes. to predict the hsw, multiple linear regression(mlr) and several neuron- and kernel-based machine learning methods were employed to analyze data from hospitals of karaj metropolis. the number of wards, active and occupied beds, staffs and inpatients, and ownership type and activity years of hospital were defined as the model inputs. in addition, proposed models performance was evaluated based on coefficient of determination (r2) and mean-square error (mse). the performance of neuron- and kernel-based machine learning methods indicated that both models were satisfactory in predicting hsw. however, the better results of 0.82–0.86 for average r2 value and 0.003–0.008 for average mse value, indicated relative superiority of kernel-based models compared to neuron based (average r2 = 0.68–0.74, average mse = 0.009–0.023) and mlr models. number of staffs and hospital ownership type were the most influential model variables in predicting the hsw generation rate. the machine learning methods could interpret the relationship between waste generation rate and model inputs, appropriately. thus, they play an effective role in developing cost-effective methods for suitable hsw management.