Prediction Of Waste Generation Research Articles

Prediction of Waste Generation in Yogyakarta Special Region Province Using ARIMA Model

Prediction of Waste Generation in Yogyakarta Special Region Province Using ARIMA Model

Solid waste generation prediction model framework using socioeconomic and demographic factors with real-time MSW collection data.

This article proposes a framework for developing predictive models of end-of-life product flows, highlighting the importance of conducting thorough analyses before developing waste management and end-of-life product flow strategies. The framework emphasizes the importance of recognizing the nature and quality of the available data and finding a balance between model development time and detail requirements. It is designed to adapt to source material heterogeneity and address varying data availability scenarios, such as the presence or absence of radio frequency identification chips. A case study for the city of Gatineau is presented, showcasing the framework's application through agent-based simulation models in a geographic information systems environment. The study focuses on creating models of municipal solid waste generation based on socioeconomic and demographic factors and collection data to accurately predict the quantity and quality of waste streams, enabling municipalities to assess the environmental impact of their waste management strategies.

Insights into the urban municipal solid waste generation during the COVID-19 pandemic from machine learning analysis

Municipal solid waste (MSW) management has become an important issue for cities around the world. Waste generation prediction plays an important role in MSW management. It can provide strong support for decision-makers to optimize collection and treatment plans, formulate policies, and design long-term strategies for waste reduction. This study develops a district-level predictive approach for MSW generation using machine learning, with a case study for New York City. Commencing in 2020, the year of the pandemic's outbreak with an abnormal fluctuation in MSW generation, the prediction model exhibited a notable accuracy reduction compared to preceding years. In response to this challenge, we have identified and examined four proxy variables pertinent to the socio-economic repercussions of the pandemic and discerned that variables relating to residents' working status can ameliorate the model performance, both before and after the pandemic. We find that the area with the largest abnormality in MSW generation in 2020 is Manhattan and its vicinity. Given the pronounced prevalence of commercial land use in these localities, they warrant heightened scrutiny and strategic focus. It is imperative for policymakers and stakeholders to channel their efforts towards these specific regions and proactively formulate contingency strategies to facilitate expedited and efficacious responses.

Provincial waste generation prediction using evolutionary-based neuro-fuzzy model: A South Africa case study

The accurate prediction of waste generation at the provincial level is essential for effective waste management planning in South Africa. However, the complex and uncertain nature of waste generation patterns poses significant challenges for policymakers and waste management authorities. Existing prediction models often overlook the intricate relationships between various factors influencing waste generation. Therefore, there is a need to develop an advanced predictive model that integrates evolutionary algorithms and neuro-fuzzy systems to provide reliable and comprehensive waste generation predictions. In this study, adaptive neuro-fuzzy inference system (ANFIS) model with two evolutionary algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA) were developed to predict the provincial waste quantities generated using South Africa as a case study. Relevant demographic and economic variables of nine provinces in South Africa namely population, provincial GDP, household number and employment data were set of input variables while the waste quantity generated was set as output variable. The fuzzy c-means (FCM) clustering technique developed the fuzzy inference system based on its speed boost capability. Hyper-parameters setting of the FCM-clustered neuro-fuzzy model in a range of 2–7 clusters was tested. The models’ accuracy was evaluated using known statistical metrics like root mean square error (RMSE), Average Absolute Percentage Relative Error (AAPRE), mean absolute deviation (MAD), and co-efficient of determination (R2). The most accurate model is the FCM-ANFIS-GA with 5-clusters giving RMSE, AAPRE, MAD and R2-values of 0.7335, 0.3656, 7.3359 and 0.9986 at the training phase and 0.8041, 0.3291, 8.1467 and 0.9962 at the testing phase. The developed model would assist in sustainable waste management and would be beneficial to South African Waste Information Center (SAWIC) and other countries.

A formal evaluation of KNN and decision tree algorithms for waste generation prediction in residential projects: a comparative approach

A formal evaluation of KNN and decision tree algorithms for waste generation prediction in residential projects: a comparative approach

Need Analysis of Waste Transporter Fleet in Pati District

Transportation of waste to carry waste from the transfer location or from the source of the waste directly to the Final Disposal Site (TPA). In Pati Regency there is a buildup of waste from shops, trades, and markets. The purpose of this study is to determine the waste transportation system, determine the needs of the waste transportation fleet, and determine the need for the number of waste transportation fleets in 2031. The method used is Stationary Container System (SCS) is used to analyze the waste transportation fleet, Waste Generation Prediction is used to predict volume population waste generation in 2031. There are currently 18 waste transport vehicles in Pati Regency, for dump trucks with a capacity of 8 m for 2 rites/ day, for arm roll trucks with a capacity of 6 m, which is 9 units. Garbage transport vehicles needed by Pati Regency in ideal conditions with the volume of waste generated 662.09 tons/day are 86 units for dump trucks with a capacity of 8 m for 3 rites/day and 26 units of arm roll trucks with a capacity of 6 m for 3 ritations/day. Meanwhile, the need for waste transportation vehicles in 2031 with an estimated waste generation of 714.87 tons/day is 104 units of dump trucks measuring 8m for 3 rites/day and 41 units of arm roll trucks for 3 rites/day measuring 6m³ ??and garbage carts/rickshaws in 2031 based on prediction required 661 units.

Construction and demolition waste generation prediction and spatiotemporal analysis: a case study in Sichuan, China.

The rapidly increased generation of construction and demolition (C&D) waste hinders the sustainable development of cities. Establishing an effective C&D waste management system is of great importance for achieving sustainable development goals. The quantification and prediction of C&D waste, forming the basis of waste management, are worthy of further exploration. C&D waste generation is time series data in which future waste generation is closely correlated with past ones. This study proposes a time-series waste prediction framework to predict C&D waste generation with less data volume by coupling generation rate calculation (GRC) and autoregressive integrated moving average (ARIMA) model. It is demonstrated in Sichuan, China, as a case study. The prediction result reveals that C&D waste generation in Sichuan shows an overall increasing trend, and the waste is mainly generated in the central of Sichuan. Chengdu accounts for over 40% of the total generation in the province, followed by Luzhou, Nanchong, and Mianyang. C&D waste generation shows a significant continual rise in Yibin and Zigong. Overall, most cities in Sichuan have issued related policies and tried to strengthen control over the transportation phase. This study provides an alternative to predict and analyze C&D waste generation from spatiotemporal perspectives. It enriches the C&D waste generation data and provides quantification support for C&D waste management at the regional level.

Greenhouse gas reduction and waste generation prediction from ICT-based smart waste collection system

Greenhouse gas reduction and waste generation prediction from ICT-based smart waste collection system

Global knowledge base for municipal solid waste management: Framework development and application in waste generation prediction

Increasing municipal solid waste (MSW) generation has become not only a major sustainability challenge and a considerable financial burden for municipalities across the globe, but also an opportunity to promote a circular economy, provided adequate information is made available. Data and information on MSW generation, characterization, and management practices are prerequisites to studying and optimizing solid waste management systems (SWMS). However, such data and information are usually dispersed, unsystematized, and suffering from various availability and quality issues. This study aims to assemble and provide access to the current landscape of MSW data by establishing a comprehensive framework for understanding the interconnectedness of various sub-domains of MSW knowledge. Existing databases and governmental reports were reviewed to compile 1720 records of MSW generation, composition, management practices, and socioeconomic contexts for 219 countries and 410 cities. Multivariate linear regression and additive models were built to relate MSW generation, composition, and recovery rates to demographics, economic development, and climate patterns of cities and regions. These models generate new insights into the complex nature of SWMS and provide an evidence-based decision-making tool to future researchers and policy makers. Specifically, economic development (GDP), density factors (population, population density, and household size), sustainability initiatives, education, and regulation are all identified as positive drivers toward the targets of United Nations Sustainable Development Goal 12.

Development of machine learning multi-city model for municipal solid waste generation prediction

Development of machine learning multi-city model for municipal solid waste generation prediction

Deep Learning Applications in Solid Waste Management: A Deep Literature Review

Solid waste management (SWM) has recently received more attention, especially in developing countries, for smart and sustainable development. SWM system encompasses various interconnected processes which contain numerous complex operations. Recently, deep learning (DL) has attained momentum in providing alternative computational techniques to determine the solution of various SWM problems. Researchers have focused on this domain; therefore, significant research has been published, especially in the last decade. The literature shows that no study evaluates the potential of DL to solve the various SWM problems. The study performs a systematic literature review (SLR) which has complied 40 studies published between 2019 and 2021 in reputed journals and conferences. The selected research studies have implemented the various DL models and analyzed the application of DL in different SWM areas, namely waste identification and segregation and prediction of waste generation. The study has defined the systematic review protocol that comprises various criteria and a quality assessment process to select the research studies for review. The review demonstrates the comprehensive analysis of different DL models and techniques implemented in SWM. It also highlights the application domains and compares the reported performance of selected studies. Based on the reviewed work, it can be concluded that DL exhibits the plausible performance to detect and classify the different types of waste. The study also explains the deep convolutional neural network with the computational requirement and determine the research gaps with future recommendations.

Machine learning in recycling business: an investigation of its practicality, benefits and future trends

Machine learning (ML) algorithms, such as neural networks, random forest, and more recent deep learning, are illustrating their utility for waste recycling. The increasing computational power of ML makes waste generation prediction, even at municipal level, possible with satisfying accuracy. ML is so critical and efficient and yet it is severely under-researched in recycling business. Also, the ML application in the recycling business is still a niche area judged by the limitations in its literature sources, the research domains, the ML algorithms’ use and benefits involved or reported in the literature. To unlock the value of ML in recycling business, this paper reviewed 51 related articles systematically and presents the current obstacles and future directions in applying ML to waste recycling industries.

Introducing an evolutionary-decomposition model for prediction of municipal solid waste flow: application of intrinsic time-scale decomposition algorithm

Owing to the importance of municipal waste as a determining factor in waste management, developing data-driven models in waste generation data is essential. In the current study, solid waste generation is taken as the function of several parameters, namely month, rainfall, maximum temperature, average temperature, population, household size, educated man, educated women, and income. Two different stand-alone computational models, namely, gene expression programming and optimally pruned extreme machine learning techniques, are used in this study to establish their reliability in municipal solid waste generation forecasting, followed by Mallow’s coefficient feature selection method. The lowest Mallow’s coefficient defines the optimal parameters in solid waste generation forecasting. The novel hybrid models of intrinsic time-scale decomposition-gene expression programming and intrinsic time-scale decomposition- optimally pruned extreme machine learning methods based on Monte-Carlo resampling are employed, and an empirical equation is presented for solid waste generation prediction. For examining the reliability of these models, five statistical criteria, namely coefficient of determination, root mean square error, percent mean absolute relative error, uncertainty at 95% and Willmott’s index of agreement, are implemented. Considering Willmott’s index, the Monte Carlo-intrinsic time-scale decomposition-gene expression programming model attains the closest value (0.957) to the ideal value in the training stage and 0.877 in the testing stage. The hybrid ensemble model of intrinsic time-Scale decomposition-gene expression programming presented lower values of root mean square error (12.279) and percent mean absolute relative error (4.310) in the training phase and in the testing, phase compared to gene expression programming with (12.194) and (5.195), respectively. Overall, the prediction results of the hybrid model of intrinsic time-scale decomposition-gene expression programming using Monte-Carlo resampling technique agrees well with the observed solid waste generation data.

Prediction of municipal solid waste generation: an investigation of the effect of clustering techniques and parameters on ANFIS model performance

ABSTRACT The present waste-management system in most developing countries are insufficient to combat the challenge of increasing rate of solid waste generation. Accurate prediction of waste generated through modelling approach will help to overcome the challenge of deficient-planning of sustainable waste-management. In modelling the complexity within a system, a paradigm-shift from classical-model to artificial intelligent model has been necessitated. Previous researches which used Adaptive Neuro-Fuzzy Inference System (ANFIS) for waste generation forecast did not investigate the effect of clustering-techniques and parameters on the performance of the model despite its significance in achieving accurate prediction. This study therefore investigates the impact of the parameters of three clustering-technique namely: Fuzzy c-means (FCM), Grid-Partitioning (GP) and Subtractive-Clustering (SC) on the performance of the ANFIS model in predicting waste generation using South Africa as a case study. Socio-economic and demographic provincial-data for the period 2008-2016 were used as input-variables and provincial waste quantities as output-variable. ANFIS model clustered with GP using triangular input membership-function (tri-MF) and a linear type output membership-function (ANFIS-GP1) is the optimal model with Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE) and Correlation Co-efficient (R 2) values of 12.6727, 0.6940, 1.2372 and 0.9392 respectively. Based on the result in this study, ANFIS-GP with a triangular membership-function is recommended for modelling waste generation. The tool presented in this study can be utilized for the national repository of waste generation data by the South Africa Waste Information Centre (SAWIC) in South Africa and in other developing countries.

Fuzzy Logic and Statistical Based Modelling to Predict Rural Solid Waste Generation of West Bengal

Waste generation prediction is a vital component to planning of rural solid waste management. Based on the past statistical data mathematical model can be develop but incorporation of new data cannot be done. In this situation advance model need to be developed, which can predict. Fuzzy logic may be one option for develop such model because dynamic and linguistic date can be used as an input parameter. A mathematical model has been developed to predicting the total. Rural waste generation using fuzzy logic if west Bengal study area. House hold, population, per capita income, district wise domestic product, literacy rate were considered as an independent variable for predicting rural solid waste generation. To described the dependent and independent variable triangular and trapezoidal shaped membership function are used. To described the defuzzification centroid method has been applied. Initially two input variables have been used to identify the correlation with rural solid waste generation Finally all input variable considered for the mathematical model. Per-capita waste generation in rural west Bengal average 150 to 300 gm/day. The statistical analysis rebuilds that age group wise population and income model is the best fitted model.

Prediction of Solid Garbage Waste Generation in Smart Cities using Naive Bayes Algorithm

Smart cities which are becoming overcrowded today are making human beings life miserable and prone to more challenges on daily basis. Overcrowded is leading to vast generation of wastes contributing to air pollution and in turn is affecting health causing various diseases. Even though various measures are taken to recycle wastes, the rate at which it is being produced is becoming higher and higher. This paper deals with prediction of waste generation using Naïve Bayes machine learning algorithm(Classifier) based on the statistics of previous waste datasets. The datasets used for the future prediction are obtained from reliable sources. The implementation of the algorithm is done in Pyspark using Anaconda Jupyter. The performance of the classifier on the datasets is analyzed with confusion matrix and accuracy metric is used to rate the efficiency of the classifier. The accuracy obtained indicates that algorithm can be effectively used for real time prediction and it gives more accurate results for huge input datasets based on independence assumption.

Waste generation prediction under uncertainty in smart cities through deep neuroevolution

The unsustainable development of countries has created a problem due to the unstoppable waste generation. Moreover, waste collection is carried out following a pre-defined route that does not take into account the actual level of the containers collected. Therefore, optimizing the way the waste is collected presents an interesting opportunity. In this study, we tackle the problem of predicting the waste generation ratio in real-world conditions, i.e., under uncertainty. Particularly, we use a deep neuroevolutionary technique to automatically design a recurrent network that captures the filling level of all waste containers in a city at once, and we study the suitability of our proposal when faced to noisy and faulty data. We validate our proposal using a real-world case study, consisting of more than two hundred waste containers located in a city in Spain, and we compare our results to the state-of-the-art. The results show that our approach exceeds all its competitors and that its accuracy in a real-world scenario, i.e., under uncertain data, is good enough for optimizing the waste collection planning.

GIS based sanitary landfill suitability analysis for sustainable solid waste disposal

One of the major challenge in waste management is the issue of selecting an appropriate site for the disposal of municipal solid waste (MSW). This is because landfill siting analysis typically requires evaluating various rules, factors, constraints, and numerous spatial data such as environmental, economic, social etc. This paper introduced an integration of Geographic Information Systems (GIS) and Analytic Hierarchy Process (AHP) in selecting optimal and sustainable site for sanitary landfill for proper solid waste management in Johor Bahru, Malaysia with the consideration of future population and waste generation prediction. The site selection criteria were retrieved based on existing literatures, Malaysian landfill siting guidelines and expert judgements. Several input criteria were used for this study which include water body, road, slope, elevation, rail lines, forest, vacant and agricultural lands, soil, geology, and urban areas. All these criteria maps were prepared in the GIS environment after which the criteria weight was obtained from AHP pairwise comparison matrix and normalization. Furthermore, the weighted criteria were evaluated through GIS software by using the weighted overlay tool in ArcGIS. The results of the study are categorized into ‘most, ‘suitable’ and ‘unsuitable’ landfill sites. The best sites are those that possess the optimal quality and characteristics for sustainable MSW disposal while the suitable sites can be kept as back up for future use.

Prediction of Municipal Solid Waste Generation for Developing Countries in Temporal Scale: A Fuzzy Inference System Approach

Fuzzy Inference System (FIS) based prediction models for the Municipal Solid Waste (MSW) generation has been developed in the present work to study the influences of total population, percapita annual income, literacy rate, age group and monthly consumer expenditure on temporal variability of MSW generation for Kolhapur city, India. Ten models were developed considering two input variables at a time to study the effect of the socioeconomic and demographic parameters on MSW generation. Finally, all five input variables were considered in a single model to predict MSW generation in a temporal scale. Result shows that, the model with input variables consumer expenditure and age group was best fitted with highest coefficient of determination (0.985) value and lowest standard error of the estimate (1.562) value for the modelling period. For the design period, models related to consumer expenditure show higher waste generation. Models related to population and age show prediction similar to ‘Kolhapur Municipal Corporations’ prediction. However model with input literacy and income shows very low waste generation prediction. The proposed modelling technique is very useful in MSW generation prediction for a temporal scale in uncertain and random environment globally.

Prediction of household and commercial BMW generation according to socio-economic and other factors for the Dublin region

Both planning and design of integrated municipal solid waste management systems require accurate prediction of waste generation. This research predicted the quantity and distribution of biodegradable municipal waste (BMW) generation within a diverse 'landscape' of residential areas, as well as from a variety of commercial establishments (restaurants, hotels, hospitals, etc.) in the Dublin (Ireland) region. Socio-economic variables, housing types, and the sizes and main activities of commercial establishments were hypothesized as the key determinants contributing to the spatial variability of BMW generation. A geographical information system (GIS) 'model' of BMW generation was created using ArcMap, a component of ArcGIS 9. Statistical data including socio-economic status and household size were mapped on an electoral district basis. Historical research and data from scientific literature were used to assign BMW generation rates to residential and commercial establishments. These predictions were combined to give overall BMW estimates for the region, which can aid waste planning and policy decisions. This technique will also aid the design of future waste management strategies, leading to policy and practice alterations as a function of demographic changes and development. The household prediction technique gave a more accurate overall estimate of household waste generation than did the social class technique. Both techniques produced estimates that differed from the reported local authority data; however, given that local authority reported figures for the region are below the national average, with some of the waste generated from apartment complexes being reported as commercial waste, predictions arising from this research are believed to be closer to actual waste generation than a comparison to reported data would suggest. By changing the input data, this estimation tool can be adapted for use in other locations. Although focusing on waste in the Dublin region, this method of waste prediction can have significant potential benefits if a universal method can be found to apply it effectively.