Municipal Solid Waste Management: A Review of Machine Learning Applications

. This study comprises of an analysis of various Machine Learning (ML) algorithms for municipal solid waste management to enhance waste management procedures and reduce the adverse environmental effects. The increasing population has resulted in substantial environmental hazards due to increased waste generation. Therefore, an effective waste management system with much more efficient and innovative waste management techniques is required to reduce the adverse effects that would occur due to the generation of massive waste. This study reviews various ML algorithms to automate and optimize garbage generation, collection, transportation, treatment, and disposal. To deliver and predict effective and precise waste generation, segregation, and collection forecasts, the system integrates multiple ML methods including decision trees (DT), k-nearest neighbours (KNN), support vector machines (SVM), random forests (RF), and clustering algorithms.


Introduction
Due to urbanization and the increasing worldwide population, municipal solid waste management (MSWM) has become more challenging in recent years.Improper waste management can harm the environment, cause adverse health effects, and increases greenhouse gas emissions.Several strategies have been implemented to address these issues, including the 4 R's principle: reduce, reuse, recycle, and recover.However, the successful application of these ideas is still difficult and complicated to accomplish.ML is a promising technology that can revolutionize MSWM [1,2] Furthermore, ML can help in optimizing waste sorting and segregation, resulting in a waste management process that is more effective and economic [3].Applying ML in MSWM may help to advance sustainable development, which can minimize the detrimental effects of garbage on the environment and public health by improving waste management procedures.This method will support social progress and economic growth, maximizing resource efficiency and minimizing waste by adopting the 4 R's philosophy, as in Figure 1.This study explores ML's potential in MSWM and its role in promoting sustainable development.

Machine Learning: An Overview
Machine learning algorithms can be employed in solid waste management to process large volumes of data and make predictions or decisions based on that data [4].ML algorithms have been developed to optimize waste management practices by reducing environmental impacts and promoting resource efficiency [5].
KNN can estimate the waste production rate, analyze the waste output, and identify the regions where excessive waste is produced.Decision Trees predict the efficiency of waste management strategies by identifying the patterns based on factors such as collection, composition, and disposal options [6].Random Forest (RF) algorithm is used to predict waste generation, identify factors that negatively affect waste production, optimize recycling practices, and classify the total solid waste based on its composition to aid in suitable treatment and disposal methods [7].By evaluating data on parameters like environmental composition, population density, and economic indicators, RF can determine patterns and predict solid waste that will be created in a specific area.Artificial neural networks (ANNs) are used to study and forecast many elements of waste management, that includes waste generation rate, recycling rate, landfill capacity, and waste composition [8].To produce accurate projections, ANN algorithms can learn from data trends and help create efficient waste management approaches.Additionally, it can evaluate the effects on the environment of different waste management solutions to support sustainable waste management practices.Support Vector Machine (SVM) can efficiently classify and sort solid waste materials, leading to better waste management practices [2].SVM can classify waste materials into recyclables, organic, and hazardous waste categories by considering various characteristics like composition, moisture content, and density.Forecasts of waste production and patterns in the generation of waste can be provided by long short-term memory neural network (LSTM) [11].Using Gradient Boosting Decision Trees (GBRT), it is possible to identify the most effective routes of collecting waste efficiently to optimize waste management techniques [8].By giving accurate information about the formation, removal, and disposal of waste, these ML algorithms can assist in enhancing waste management methods, reducing costs, and minimizing the environmental effect.

Application of Machine Learning in MSWM
Figure 2 shows the application of the ML algorithms in each stage of municipal solid waste management.The stages include municipal solid waste generation, waste collection, waste transportation, treatment, and disposal.

Municipal Solid Waste Generation Prediction
Municipal solid waste (MSW) may be generated during the extraction or processing of raw materials, consumption of final products, and human activities, which depends on many factors, including population density, economic development, and lifestyle choices.Data on these factors can be analyzed using ML algorithms to predict how much these wastes will be generated in municipalities.

Generation rate prediction
ML algorithms can develop a predictive model that estimates the rate of waste generation in a particular area by studying information on factors such as population density, economic development, and lifestyle choices, allowing for accurate predictions of the quantity of waste generated within a specific period [10].

Quantity prediction
ML algorithms can create a prediction model to estimate the quantity of waste generated in a region by analyzing data from households, businesses, and other sources, including factors such as population density, economic development, lifestyle choices, household size, commercial type, and disposal habits [11].This comprehensive approach enables accurate predictions of the waste produced within a specific period.

Heavy waste generation area prediction
ML methods may also identify areas where a large volume of waste is probably generated [12].The algorithms analyze the data relating to factors such as population density, income level, and waste disposal behaviour to identify areas where there will be an increase in waste production [11].In those areas, such information may be used to target waste reduction and recycle efforts.These algorithms may assist municipalities in planning and implementing waste reduction and recycling programs precisely adapted to the individual needs of their communities, by determining generation volumes, quantities, or areas with significant waste [9].

Modelling methods
Conventional waste generation prediction models including correlation and regression models generally used demographic and socioeconomic factors.Various independent variables were considered in most of the prediction models [13].

Prediction of Waste Generation Using ARIMA Model
The autoregressive integrated moving average (ARIMA) model is a classical statistical method for time series forecasting.By examining the relationship between target values and lagged observations, the ARIMA model aims to convert time series data into a stationary form.However, despite parameter optimization, the ARIMA model has relatively poor performance compared to machine learning models [10].

Prediction of Waste Generation Using NARX Model
The non-linear autoregressive exogenous model (NARX) is a type of neural network specifically designed for modelling non-linear time-series data.It incorporates recurrent feedback connections from other layers within the network, allowing it to capture complex patterns and relationships [9].In this study, we utilized the NARX model as a baseline to evaluate the performance of our proposed method.

Artificial neural network (ANN)
ANN have been effectively applied in the prediction of MSW generation.The learning capability and ability to model nonlinear systems make ANN suitable for the process of MSW generation in both short and long-term scenarios.However, the accuracy of ANN can be affected by irrelevant data and overfitting.Techniques like principal component analysis and wavelet transform are used to address these challenges [14].Enhancing ANN's performance and overcoming its limitations remain ongoing concerns.

Adaptive neuro-fuzzy inference systems
Adaptive neuro-fuzzy inference systems (ANFIS) are data-driven modeling technique combining ANN and fuzzy logic, which has been explored in waste generation prediction.
Limited studies compared ANFIS and ANN models' performance in forecasting MSW generation.ANFIS was a reliable model for considering factors like economic trends, population changes, and recycling.Chen and Chang (2000) demonstrated ANFIS's ability to forecast waste generation with limited input data [15].Both Chen and Chang (2000) and Noori et al. ( 2009) used fuzzy goal regression to improve ANFIS prediction accuracy [15,16].

Support Vector Machine
SVM algorithm is a novel neural network technique that makes use of maximum margin classifiers.The objective of SVM is to find an optimal separating hyperplane with the maximum margin across the data.Unlike traditional neural networks that minimize misclassification error, SVM implements the principle of structural risk minimization [17].In waste generation forecasting, SVM is used to predict weekly MSW generation with reasonable accuracy.Pre-processing input variables using wavelet transform enhanced the accuracy and robustness of the model [18].

K-nearest neighbors
The k-nearest neighbors (KNN) algorithm is widely used for regression and classification tasks due to its simplicity.In time series forecasting, KNN has been applied under nonparametric locally weighted regression conditions.The concept behind applying KNN to univariate time series is that consistent data-generating processes exhibit repeated behavioural patterns [19].By identifying similar past patterns, valuable information can be obtained to predict the immediate future.However, further attempts are yet to be made.The KNN approach efficiently detects new waste samples, streamlining disposal and recycling procedures.

Random Forest (RF)
RF is a widely used machine learning technique explored in predicting solid waste generation.This algorithm belongs to the ensemble learning category and combines multiple decision trees to create a powerful predictive model.By training the RF model on historical data containing waste generation patterns and relevant features, such as demographic and economic factors, it can capture complex relationships and provide accurate forecasts [20].

Waste Collection and Transportation
ML can improve the collection efficiency of MSW by using its predictability and optimization potential.These ML algorithms use clustering, classification, and regression approaches to forecast the rates of waste generation in various regions and determine the most effective routes for waste collection vehicles.This makes it possible for waste management agencies to reduce fuel use and the overall carbon footprint of moving waste [11].

Bin Level Detection
ML can identify the fill level of waste bins, enabling the reduction of unnecessary trips to partially filled bins and can save cost in transportation.Placing the bin and the camera is a significant issue.The camera operator needs specific instructions to place the bin in the image being captured [13].With the help of these algorithms, real-time data gathered from sensors or Internet of Things (IoT) gadgets installed in the bins can be processed, giving precise forecasts and insights for better garbage-collecting procedures.SVM, RF, ANN, KNN, Multi-Layer Perceptron (MLP) etc, are a few ML techniques that can be successfully used in waste bin-level detection.SVM divides data into various groups based on patterns and attributes.SVM can be trained using past data, including sensor readings and related fill levels [17].RF is another extensively used method for detecting the level of a waste bin.This forecast helps waste management organizations to plan effective collection routes and maximize the use of collection vehicles.Bin levels can also be detected using the KNN method, which calculates the fill levels of waste bins based on how close they are to other bins with known fill levels [20].The MLP classifier was able to properly forecast the level of the garbage bin and calculate the volume of waste it contained by examining these features [13].In one study, the KNN method performed better than the MLP approach in terms of classification and grading accuracy [21].

Collection Schedule
MSW collection schedules can be significantly improved using ML algorithms by analyzing historical data, forecasting waste generation patterns, and allocating resources optimally.ML algorithms, including regression models, time series analysis, and optimization algorithms, can be used for collection schedules [1].MSW collection scheduling can be done using DT algorithms by developing a tree-like model that associates decision nodes with parameters such as trash generation trends, weather, population density, and other pertinent aspects.By optimizing the collection schedule at each node, the method iteratively divides the feature space into subsets based on the most informative characteristics.Waste management agencies may calculate the ideal collection schedule for various locations and time intervals by navigating the decision tree while taking the elements that affect garbage generation into account.The decision tree algorithm offers a clear and understandable structure for allocating time for garbage collection based on predetermined criteria and limits.

Crew Performance Analysis
Several methods may be used for analyzing crew performance in solid waste management using machine learning to collect insights and improve operational effectiveness.Random Forest, which integrates many decision trees to produce predictions, is one such algorithm.To evaluate crew performance and pinpoint areas for development, RF may analyze various aspects, including staff structure, equipment utilization, route optimization, and work distribution.Employing historical data, SVM can be used to classify crews into diverse performance categories.Moreover, SVM can provide valuable insights into the distinguishing characteristics of both highly effective teams and those that perform poorly.Additionally, complicated interactions between crew performance measures and different operational parameters may be modelled using neural networks.

Route Optimization
Municipal solid waste (MSW) management depends substantially on route optimization, and machine learning techniques may significantly improve this procedure.The route optimization difficulties, which involve significant expenditures in terms of labour, resources, and unpredictable operating expenses, have a significant impact on waste collection [1].Waste management firms may optimize garbage collection routes and can save expenses.The operational efficiency of routing optimization can be improved by utilizing machine learning techniques, including K-Nearest Neighbours, Random Forest, and Decision Trees.KNN can identify associated areas and calculate the anticipated waste production in a certain place by examining historical data on waste generation trends, geographic data, and other important aspects.To improve routing efficiency and reduce overall costs, RF considers several factors like distance, traffic, time limitations, and waste generation patterns.An alternative machine learning algorithm suitable for enhancing routing optimization is the DT, which shares certain similarities with RF.DT creates a model of decisions and potential outcomes that resembles a tree.To find the most effective collection routes, DT may examine historical data on waste generation, traffic patterns, and geographic information.

Vehicle Allocation
The application of ML algorithms has been widely explored and employed in the domain of waste collection vehicle assignment.The efficiency of utilizing vehicles hinges on the fill levels of waste bins.Within this domain, prevalent machine learning techniques such as Linear Regression(LR) and DT have been utilized.LR simulates the connection between the number of vehicles needed for garbage collection and input parameters (such as waste generation rates, density of population, and location of disposal sites).Waste management firms can utilize resources more effectively and cut down operating expenses using this algorithm.Another effective approach for vehicle allocation in MSW management is DT.DT can evaluate these variables and decide the best distribution of vehicles across various places based on certain standards, including reducing trip times or increasing collection effectiveness.

Vehicle Load Optimization
Vehicle load optimization is essential for effective waste collection and transfer.By examining numerous elements, such as waste generation trends, bin fill levels, and geographic data, machine learning algorithms provide enormous opportunities for optimizing vehicle loads.Conventional methods for machine learning, such as LR, DT, and RF can be employed for this purpose.The most common method for predicting continuous variables based on input data is linear regression.The connection between waste generation patterns and the capacity of waste collection trucks may be modelled using linear regression in vehicle load optimization.The system can calculate the optimum vehicle load based on the anticipated waste volumes by training it on historical data from prior waste collection operations, including parameters like time, location, and waste formation rates.Waste management organizations may organize their resources minimizing collection trips and fuel usage.Another common ML approach that may be used for vehicle load optimization is decision trees.In a decision tree, each leaf node decides or predicts, and the data is divided based on binary decisions.Decision Trees can be trained on past information that contains numerous variables, such as waste generation rates, bin fill levels, and collection schedules, in optimizing vehicle loads.This information enables the algorithm to provide a set of decision rules that establish the ideal vehicle load depending on the input features.DT are easily interpreted and can give insightful information about the variables that affect vehicle load optimization.An ensemble learning system called Random Forests combines numerous Decision Trees to increase the precision of predictions.
Random Forests can overcome the limitations of distinct Decision Trees and offer more reliable forecasts in optimizing truckloads.An ensemble of Decision Trees may be produced through the Random Forest method by training it on historical data that includes trash creation trends, bin fill levels, and other pertinent variables.The ideal vehicle load is predicted individually by each DT in the ensemble, and the result is derived by a voting or averaging procedure.This ensemble technique improves the overall accuracy and reliability of vehicle load optimization while lowering the possibility of overfitting.

Treatment of MSW
Solid waste treatment encompasses diverse processes to minimize the environmental repercussions of waste disposal.These methods encompass incineration, composting, and recycling.Composting leverages biological decomposition for organic waste conversion into nutrient-rich compost, while incineration entails waste combustion.Recycling concentrates on reclaiming valuable materials for reuse.The primary goal of solid waste treatment is to reduce landfill volumes, mitigate pollution, and foster sustainable waste management practices, contributing to environmental preservation.

Biochemical treatment
Incorporating ML methodologies in SWM has led to advancements in bio-chemical treatment processes.Biochemical treatment involves utilizing biological mechanisms to break down organic waste materials, and ML algorithms have proven beneficial in optimizing and enhancing its efficiency.Through the analysis of extensive datasets, ML models can accurately predict degradation rates, identify crucial factors influencing treatment outcomes, and optimize process conditions.This integration enables improved decision-making, efficient resource allocation, and the development of effective waste management strategies.

ML algorithms used in MSW Composting
SWM increasingly uses ML algorithms for various activities, including classification, prediction, optimization, and decision-making.ML algorithms have been used in composting MSW to address several processing issues, including monitoring, quality evaluation, control, and compost property prediction.ML algorithms created a real-time monitoring system for MSW composting [22].Multiple sensors were utilized by the system to gather data on temperature, moisture, pH, and other parameters.A random forest algorithm was then used to classify the composting stages and predict the composting time.The outcomes demonstrated that the system could reliably anticipate the time needed for composting and deliver timely feedback for modifying the composting procedure.In another study, they created a predictive model for compost quality based on the input variables of feedstock composition, temperature, moisture, and aeration [23].The SVM approach utilized by the model effectively categorized compost quality into three tiers: high, medium, and low.It achieved an accuracy rate of 86.5% in this classification task.The model might enhance the final product's quality and optimize the composting procedure.ML algorithms have demonstrated considerable promise for enhancing the effectiveness, sustainability, and efficiency of MSW composting.To create more sophisticated and integrated ML-based systems for SWM, as well as to address the issues of data quality, model interpretability, and scalability in practical applications, more study is required.

ML algorithms used in MSW bio methanation
ML techniques have shown promise in optimizing the bio-methanation process for municipal solid waste treatment.Bio-methanation involves the anaerobic decomposition of organic waste to produce methane gas, which can be utilized as a renewable energy source.ML algorithms can analyse large datasets containing waste composition, process parameters, and environmental factors to develop predictive models.These models can optimize key aspects of the bio-methanation process, like feedstock selection, retention time, temperature, and pH levels.Incorporating machine learning algorithms in bio methanation can improve biogas production efficiency, reduce operational costs, and minimize adverse ecological impacts.

Thermochemical treatment
Thermochemical waste management treatment includes processes like pyrolysis and gasification.These are utilized to convert the waste to be treated into valuable energy products through high-temperature reactions.By utilizing predictive models, machine learning can estimate waste composition and heating value, facilitating improved process control and optimization.

ML algorithms used in MSW Incineration
MSW incineration helps to reduce the volume of waste by converting it to electricity.Incineration holds a negative impact on the environment when not properly monitored and maintained.The application of ML algorithms to improve MSW incineration procedures and lessen their environmental impact has grown in recent years.Another study used a hybrid model that combined SVM and LSTM to forecast the emissions of carbon monoxide, nitrogen oxides, and sulphur dioxide during MSW incineration [24].

Disposal of MSW
MSWs are stored in landfills, which are difficult to model because they comprise components in three phases: solid, liquid, and gas.The biodegradation of solid wastes by a variety of microorganisms results in the production of large volumes of CO2 and CH4.Since the biodegradation process is often exothermic, a large amount of heat is produced and held in landfill.

Landfill surface temperature detection
The process of solid waste landfilling is the temperature of the landfill surface.It is necessary to analyze and monitor landfill temperature to reduce the negative environmental effects and prevent landfill fires.It is expensive and time-consuming to take temperature readings in the field at landfills.For determining the land surface temperature, remote sensing is proving to be a useful and economical method.Wide-ranging temperature information can be obtained using thermal infrared sensors mounted on satellites or aircraft.For monitoring and analysis, these photos give a generalized perspective of the temperature distribution across the surface of the waste.Also, in ground-based temperature sensors, on the surface of the dump, a variety of places can accommodate the installation of ground-based temperature sensors.To detect the temperature at certain locations, these sensors can be buried or placed on the ground.The gathered information is then examined to track the evolution of surface temperature variations through time.

Leachate generation prediction
Managing leachate throughout the processes of percolation, collection, and disposal is significant in landfill design, considering both technological aspects and budgetary considerations [25].Leachate flow rates are influenced by three key weather-related factors: precipitation, air temperature, and relative humidity.Regardless of the landfill construction method chosen, meteorological conditions directly impact the rate at which leachate flows.Among these conditions, rainfall stands out as the primary contributor to leachate generation.The most critical scenario arises following prolonged periods of light rainfall.During such instances, the cover material quickly becomes saturated due to short bursts of intense rain, resulting in minimal net infiltration and triggering runoff of additional rainwater.Based on input characteristics, these models create correlations and forecast leachate generation using statistical techniques.Leachate from landfill cells should be regularly monitored for and sampled because this can yield useful information for calculating leachate generation.To increase the accuracy of estimating leachate generation rates in landfills, a multifaceted strategy integrating empirical data, modelling methods, and constant monitoring is required due to the complexity of leachate generation [26].

Conclusion
Machine learning models have demonstrated successful applications in various domains, including waste categorization, optimization of collection routes, waste volume forecasting, high-volume waste management, and supervision of landfill leachate as well as monitoring emissions of pollutants [27].Additionally, these models have been effectively employed in controlling processes and enhancing efficiency across these areas.The evaluation also draws attention to the booming interest in monitoring MSWM using ML models and the rapid rise in articles in this area over the previous two years.Even though the use of Deep Learning technology in MSWM has grown significantly, it is still in development and requires more useful applications.It is crucial to integrate current data resources, achieve data exchange, and enhance information gathering and oversight in the entire waste disposal process to realize the function of ML algorithms in waste management.
Future applications of ML algorithms in SWM include a wide range of technical capabilities.One area of interest is the use of increasingly sophisticated deep learning methods for classifying and predicting waste, such as GANs and RNNs.For real-time monitoring and analysis of waste management systems, the combination of ML algorithms and IoT technologies is another area of focus.Finally, a significant area of future research will focus on creating decision support systems that can offer wise suggestions for waste management operations and management.