Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components

Solid Waste (SW) is one of the critical challenges of urban life. These SWs are considered environmental pollutants that are a threat to ecology and human health. Predicting SW generation is an essential topic for scholars to better manage SWs. This study investigates the application of optimised ANN models for predicting monthly SW generation in Iran using datasets about seven Iranian megacities. The Archimedes Optimisation Algorithm (AOA), Sine Cosine Algorithm (SCA), Particle Swarm Optimisation (PSO) technique, and Genetic Algorithms (GA) were used for training the ANN model. The enhanced gamma test was used to determine the best input combination. AOA and the gamma test were used concurrently to reduce the time needed for choosing the best input combination. Gross domestic product (GDP), population, household size, and numbers of months were the best input combination set. This best input combination was then inputted into the hybrid and standalone ANN models for predicting monthly SW generation. During the final phase, the outputs of ANN-AOA, ANN-SCA, ANNPSO, ANN-GA, and ANN models were used as inputs for an inclusive multiple model (IMM) in order to enhance model accuracy. The IMM model reduced the training phase root mean square error (RMSE) of ANN-AOA, ANNSCA, ANN-PSO, ANN-GA, and ANN models by 55%, 59%, 68%, 72%, and 73%, respectively. Although ANN-AOA provided higher R2 and lower RMSE values than ANN-PSO, ANN-SCA, ANN-GA and ANN models, the IMM model outperformed ANN-AOA, considering that it integrates the advantages of all models used in the current study. The current study also used the fuzzy reasoning concept for modifying ANN model structures. The results indicated that such ANN models' time requirement was lower than those without fuzzy reasoning concept. The general results of the current study indicate that the ANN-AOA and the fuzzy-reasoning based Inclusive Multiple Model have a high ability for predicting different target variables.

Automated summary

This study explores the use of optimized ANN models to predict monthly solid waste generation in Iranian megacities, with GDP, population, household size, and number of months identified as the best input combination. The Inclusive Multiple Model (IMM) is used to enhance accuracy, reducing the root mean square error of various ANN models significantly. The study also incorporates fuzzy reasoning concepts to improve model structures and overall prediction ability.