An evolutionary machine learning approach for municipal solid waste generation estimation utilizing socioeconomic components

Ghanbari, Forough; Kamalan, Hamidreza; Sarraf, Amirpouya

doi:10.1007/s12517-020-06348-w

Cited by 17 publications

(4 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, ML algorithms have been employed successfully to predict waste generation (Xu et al 2021). To predict waste generation using SVM combined with partial least square (Abbasi et al 2013), a gradient boosting model (Johnson et al 2017), two hybrid models based on decision trees and neural network was applied to predict Canada -wide municipal waste generation SVM and RF (Kumar et al 2018), a hybrid model based on SVM and recurrent neural network (Meza et al 2019), Gaussian process regression (GPR) model tuned by Bayesian optimization (Ceylan, 2020), prediction model using four combination intelligent algorithms, namely SVM, an integrated artificial neural network (ANN), RF, and multivariate adaptive regression splines (MARS) models (Ghanbari et al 2021), decision tree and RF approach (Joshi et al, 2021). Among numerous nonlinear methods, ANN is one of the effective non-linear models to predict MSW generation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, significant studies on waste prediction using statistical modelling techniques, including machine learning (ML), have been presented (Abbasi et al 2013;Abbasi & Hanandeh, 2016;Johnson et al 2017;Ghanbari et al 2021). The application of deep learning models to prediction issues has risen to prominence as high-performance data processing and computer power have advanced.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Based a Comprehensive Analysis for Waste Prediction

Utku

Kaya

2022

Oper. Res. Eng. Sci. Theor. Appl.

View full text Add to dashboard Cite

In its simplest definition, waste can be defined as any substance that is used, not needed and causes harm to the environment. Waste management covers control activities such as prevention of the formation of waste, reuse, separation according to its characteristics and type, storage, transportation, recycling and disposal. The main purpose of waste management is to leave a livable world to future generations, to create a sustainable environment, to protect natural resources, to save energy and costs, to reduce the rate of pollution and the amount of hazardous waste. In today's world where urbanization and industrialization rates are increasing, waste management is gaining importance. The aim of this study is to utilize waste data from Istanbul, Turkey's largest and fastest growing city, to estimate waste amount using a constructed Long Short-Term Memory (LSTM) based deep learning model. The developed LSTM-based model has been compared in practice with k-Nearest Neighbors (kNN), random forest (RF), Support Vector Machine (SVM), multi-layered perceptron (MLP) and Gated Recurrent Unit (GRU). As a result of the comparative and comprehensive analyzes, the experimental results showed that the developed LSTMbased deep learning method is more successful in the waste prediction problem than the other compared models.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning Based a Comprehensive Analysis for Waste Prediction

Utku

Kaya

2022

Oper. Res. Eng. Sci. Theor. Appl.

View full text Add to dashboard Cite

show abstract

“…80%), the rate of vulnerability changes in the multi-objective FFA (Moffa) is lower than that of the multi-objective HHO (Mohho) algorithm. Ghanbari et al (2021) obtained the important socioeconomic parameters of the city of Tehran, Iran in the period of 1991-2013. Important and optimum variables were analyzed and selected using the Pearson correlation analysis, and four variables including income, pop, GDP, and month were selected.…”

Section: Introductionmentioning

confidence: 99%

Calibration of SWAT and three data-driven models for monthly stream flow simulation in Maharlu Lake Basin

2021

Self Cite

View full text Add to dashboard Cite

In this study, the performance of SWAT hydrological model and three computational intelligence methods used to simulate river flow are investigated. After collecting the data required for all models used, the calibration and validation stages were performed. Using the SWAT model and three methods of the Extreme Machine Learning (EML), the Support Vector Regression (SVR), and the Least Squares Support Vector Regression (LSSVR), Maharlu Lake Basin stream flow was simulated and the results obtained at Shiraz station were used for this study. A noise reduction filter was employed to improve the results from the computational intelligence methods, and SUFI-2 algorithm was used to analyze the uncertainty of the SWAT model. Finally, in order to evaluate the models developed and the SWAT model, three statistics (RMSE), (R²), and (NS) coefficient were used. The results indicated that the SWAT model and the machine learning models were generally appropriate tools for daily flow modeling, but the LSSVR model showed less errors in both learning and testing, with the coefficients NS = 0.997 and R² = 0.997 in the calibration stage and NS = 0.994 and R² = 0.994 in the validation stage, which prove their better performance compared to the other methods and the SWAT model.

show abstract

“…Ghanbari et al (2021) attempted to estimate the solid waste generated as a function of detailed socioeconomic components (spanning from 1991 to 2013) of residents in the city of Tehran, Iran(Ghanbari et al, 2021). For estimating the solid waste, three commonly used ML methods, including RF, ANN, and multivariate adaptive regression splines (MARS) were employed.…”

mentioning

confidence: 99%