Prediction Of Solid Waste Generation Research Articles

Time series forecasting of solid waste generation in selected states in Malaysia

This study aims to forecast Malaysian solid waste generation by identifying the state's landfill capacity to facilitate solid waste generated in the next two years. The solid waste management system depends extremely on landfill capacity. Due to the increased amount of solid waste generation, the authority is required to manage landfill utilization appropriately in selected regions, where landfill capacity was fully utilized. An accurate prediction of solid waste generation is required for the authority plan for landfill management. This paper provides the forecasting values for the seven states in Malaysia. The ARMA and ARIMA models are used to determine the best model for forecasting solid waste generation values. The results show that the ARIMA (2, 1, 1) model works best in Johor, Negeri Sembilan, and Wilayah Persekutuan Kuala Lumpur, while the ARIMA (1, 1, 2) model works best in Kedah and Perlis. Furthermore, the ARMA (1, 1) model is best for Pahang, and the ARMA (2, 1) model is best for Melaka. The ARIMA (3, 1, 1) model is the best for forecasting solid waste generation across all states. The findings are consistent with previous literature, which stated that solid waste generation would increase in one of Malaysia's districts over the next two years. They did not, however, consider the landfill's capacity to handle solid waste generation. These findings shed light on the potential volume of solid waste generated in the coming years, allowing authorized agencies to plan landfill capacity in Malaysia for environmental sustainability.

Solid Waste Generation and Disposal Using Machine Learning Approaches: A Survey of Solutions and Challenges

We present a survey of machine learning works that attempt to organize the process flow of waste management in smart cities. Unlike past reviews, we focused on the waste generation and disposal phases in which citizens, households, and municipalities try to eliminate their solid waste by applying intelligent computational models. To this end, we synthesized and reviewed 42 articles published between 2010 and 2021. We retrieved the selected studies from six major academic research databases. Next, we deployed a comprehensive data extraction strategy focusing on the objectives of studies, trends of ML adoption, waste datasets, dependent and independent variables, and AI-ML-DL predictive models of waste generation. Our analysis revealed that most studies estimated waste material classification, amount of generated waste per area, and waste filling levels per location. Demographic data and images of waste type and fill levels are used as features to train the predictive models. Although various studies have widely deployed artificial neural networks (ANN) and convolutional neural networks (CNN) to classify waste, other techniques, such as gradient boosting regression tree (GBRT), have also been utilized. Critical challenges hindering the prediction of solid waste generation and disposal include the scarcity of real-time time series waste datasets, the lack of performance benchmarking tests of the proposed models, the reliability of the analytics models, and the long-term forecasting of waste generation. Our survey concludes with the implications and limitations of the selected models to inspire further research efforts.

Prediction of solid waste generation and finding the sustainable pathways in the city of Dhaka

PurposeSolid waste (SW) is the result of rapid urbanization and industrialization, and is increasing day by day by the increasing number of population. This thesis paper emphasizes on the prediction of SW generation in the city of Dhaka and finding sustainable pathways for minimizing the gaps in the existing system.Design/methodology/approachIn this paper, the survey of different questionnaires of the Dhaka South City Corporation (DSCC) was conducted. The data of SW generation, for few years of each month, in the city of Dhaka were collected to develop a model named Artificial Neural Network (ANN). The ANN model was used for the accurate prediction of SW generation.FindingsAt first, by using the ANN model with the one hidden layer and changing the number of neurons of the layer different models were created and tested. Finally, according to R values (training, test, all) the structure with six neurons in the hidden layer was selected as the suitable model. Finally, six gaps were found in the existing system of solid waste management (SWM) in the DSCC. These gaps are the main barrier for the better SWM.Originality/valueThe authors propose that the best model for prediction is 12-6-3, and its training and testing results are given as 0.9972 and 0.80380, respectively. So the resulting prediction is so much close in comparison with actual data. In this paper, the opportunities of those gaps are provided for working properly and the DSCC will find the better result in the aspect of SW problem.

Forecasting solid waste generation: a Fourier series approach

Successful planning of a solid waste management system depends on the accuracy of prediction of solid waste generation. With a continual economic development and increase in the living standards, the demand for goods and services is increasing at an unprecedented rate, resulting in a commensurate increase in per capita waste generation. In order to facilitate informed decision making for an effective solid waste management, we propose a Fourier Series Model to forecast solid waste generation in Kumasi, Ghana. A monthly waste data from 2007 to 2014 was obtained from the solid waste department of the Kumasi Metropolitan Assembly, Ghana. This was used to formulate the Fourier series model for forecasting solid waste. This novel application incorporates the characteristics of the data making them it appropriate for forecasting solid waste. MAPE and RMSE comparison of our proposed model with existing method for forecasting solid waste shows that our method competes favourably well.

Longterm forecasting of solid waste generation by the artificial neural networks

AbstractThis study presents a new approach—preprocessing for reaching the stationary chain in time series—to unravel the interpolating problem of artificial neural networks (ANN) for long‐term prediction of solid waste generation (SWG). To evaluate the accuracy of the prediction by ANN, comparison between the results of the multivariate regression model and ANN is performed. Monthly time series datasets, by the yrs 2000–2010, for the city of Mashhad, are used to simulate the generated solid waste. Different socioeconomic and environmental factors are assessed, and the most effective ones are used as input variables. The projections of these explanatory variables are used in the estimated model to predict the generated solid waste values through the yr 2032. Ultimately, various structures of ANN models are examined to select the best result based on the performance indices criteria. Research findings clearly indicate that such a new approach can be a practical method for long‐term prediction by ANNs. Furthermore, it can reduce uncertainties and lead to noticeable increase in the accuracy of the long‐term forecasting. Results indicate that multilayer perception approach has more advantages in comparison with traditional methods in predicting the municipal SWG. © 2011 American Institute of Chemical Engineers Environ Prog, 2011

Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling

Both planning and design of municipal solid waste management systems require accurate prediction of solid waste generation. Yet achieving the anticipated prediction accuracy with regard to the generation trends facing many fast-growing regions is quite challenging. The lack of complete historical records of solid waste quantity and quality due to insufficient budget and unavailable management capacity has resulted in a situation that makes the long-term system planning and/or short-term expansion programs intangible. To effectively handle these problems based on limited data samples, a new analytical approach capable of addressing socioeconomic and environmental situations must be developed and applied for fulfilling the prediction analysis of solid waste generation with reasonable accuracy. This study presents a new approach – system dynamics modeling – for the prediction of solid waste generation in a fast-growing urban area based on a set of limited samples. To address the impact on sustainable development city wide, the practical implementation was assessed by a case study in the city of San Antonio, Texas (USA). This area is becoming one of the fastest-growing regions in North America due to the economic impact of the North American Free Trade Agreement (NAFTA). The analysis presents various trends of solid waste generation associated with five different solid waste generation models using a system dynamics simulation tool – Stella ®. Research findings clearly indicate that such a new forecasting approach may cover a variety of possible causative models and track inevitable uncertainties down when traditional statistical least-squares regression methods are unable to handle such issues.

Prediction analysis of solid waste generation based on grey fuzzy dynamic modeling

Abstract Successful planning of a solid waste management system depends critically on the prediction accuracy of solid waste generation. But the prediction condition of generation trend in many developing countries is quite different from those in developed countries. The lack of sampling and analysis in many developing countries due to insufficient budget and unavailable management task force has resulted in a situation where the historical record of solid waste generation and composition can never be completed in the long term. To effectively handle these problems with only limited samples and fulfil the prediction analysis of solid waste generation with reasonable accuracy, a special analytical technique must be developed and applied before the subsequent system planning for urban solid waste management is carried out. This study presents a new theory — grey fuzzy dynamic modeling — for the prediction of solid waste generation in the urban area based on a set of limited samples. The practical implementation has been accessed by a case study in the city of Tainan in Taiwan. It shows that such a new forecasting technique may achieve better prediction accuracy than those of the conventional grey dynamic model, least-squares regression method, and the fuzzy goal regression technique.

An analysis of recycling impacts on solid waste generation by time series intervention modeling

Abstract Successful operation of solid waste management systems frequently depends on accurate predictions of solid waste generation. Conventional prediction models are usually estimated based on demographic and socioeconomic factors in a per-capita basis. The per-capita coefficients may be taken as fixed over time or they may be projected to change with time. However, time series data of solid waste generation consist of observations made over a number of years at the same location. By analyzing time series data, forecasters can identify trends embedded in solid waste generation over time and can develop hypotheses regarding the policy change or the continuation of these trends into the future. At present, the impacts of recycling activities has received wide attention at different levels of government agency and the effect of successful recycling programs inevitably adds more uncertainties in the prediction of solid waste generation. This analysis applies time series intervention modeling to evaluate recycling impacts on solid waste generation. A demonstration of how this forecasting information can be used for the capacity evaluation of incinerators in Taipei City of Taiwan is also included in this paper.