Waste-to-Resource Transformation: Gradient Boosting Modelling for Organic Fraction Municipal Solid Waste Projection

Eniola Adeogbaǂ, Peter Bartyǂ, Edward O’Dwyer, Miao Guo*

Department of Chemical Engineering, Imperial College London, London, SW7 2AZ U.K.ǂ Equivalent contribution

*corresponding author: miao.guo@imperial.ac.uk

Abstract

Food and garden waste are important components of organic fraction municipal solid waste (OFMSW), representing carbon

and nutrient rich resources composed of carbohydrates, lipid, protein, cellulose, hemicellulose and lignin. Despite progressive

diversion from landfill, over 50% of landfilled MSW is biodegradable, causing greenhouse gas emissions. In conventional

waste management value chains, OFMSW components have been regarded as by-products as opposed to promising resources

with energy and nutrient values. Full exploitation of waste resources calls for a value chain transformation towards proactive

resource recovery and waste commoditization. This requires robust projection of OFMSW composition and supply variability.

Gradient boosting models are developed here using historical socio-demographic, weather and waste data from UK local

authorities. These models are used to forecast garden and food OFMSW generation for each of the 327 UK local authorities.

The developed methods perform particularly well in forecasting garden waste due to a greater link to measurable

environmental variables. The research highlights the key influences in waste volume prediction and demonstrates the difficulty

in transferring models to local authorities without training data. The predictive performance and spatial granularity of model

projections offer a promising approach to inform decision-making on future waste recovery facilities and OFMSW

commoditization.

Key words: machine learning, organic municipal solid waste, Gradient Boosting model, waste recovery, value chain

Introduction

The projected 50% increase in global population in the 21st century1 combined with non-OECD economic growth is

expected to increase resource (e.g. food and energy) demands as well as lead to rising waste generation. Municipal solid waste

(MSW) defined as the municipal waste collected and treated by or for municipalities, including organic fraction MSW

(OFMSW, food waste, garden and park waste, paper and cardboard, wood) and inorganic MSW (textiles, rubber and leather,

plastics, metal, glass and others)2. Global MSW growth is projected to exceed 11 million tons per day (59%-68% organic

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fraction) by 2100 under ‘business as usual’3. Increasing waste trends are particularly intense in less developed countries2. Such

waste trends not only increase the resource stress but also contribute to greenhouse gases (GHGs). Annual global food waste

(equivalent to one-third of food produced globally) is equivalent to the waste of 8.5% annual water withdrawn and 28% of

agricultural lands4; MSW merged as a major concern as post-consumer waste account for 5% of global GHGs. A

transformation to resource-circular systems and sustainable MSW management is necessary.

Despite the ongoing shift away from a landfill-dominated system, MSW chemical composition variability and conventional

waste value chains hinder the transformation of waste sector towards a resource-circular system. Growing environmental

pressure has resulted in regional/national targets to divert waste from landfills and increase recycling and recovery rate. As

part of a circular economy strategy, the EU has set the targets to recover MSW (recycling 65% MSW by 2035) 5 and restrict

OFMSW sent to landfill (35% of the 1995 baseline by 2020)6. UK OFMSW to landfill represents 22% of the 1995 baseline

value with over 7.7 million tonnes of biodegradable municipal waste ending up in landfill in 2016.6 This implies that over 50%

of landfilled MSW is biodegradable.6,7 The decomposition of organic MSW in landfill is the predominant contributor (92%) to

the GHGs associated with the MSW sector (4% of total GHG) in the UK.8 In conventional waste management value chain and

market, OFMSW along with other waste streams have been regarded as by-products (carrying zero or low-value) rather than

marketable commodities with well-defined grades (in contrast to oil products as energy carriers). In fact, OFMSW streams are

not only carbon-rich resources as energy carrier but also contain high nutrient value (e.g. protein, lipid and minerals). The

waste sector presents promising opportunities for resources to be converted to value-added products via thermochemical and

biochemical pathways such as anaerobic digestion. To exploit waste resource value requires a transformative waste value

chain and commoditization of waste resources, which requires quantitative projection of waste composition and supply.

However, the waste composition is highly complex and variable. Take food waste as an example. The UK nationwide analyses

showed significantly varying carbohydrates (30-250 g/kg), lipid (10-128 g/kg), protein (5-140g/kg), soluble sodium and

potassium (1.2-55 g/kg) contents.9 These are dependent on spatially-explicit factors (e.g. local diet and behavior) and

seasonally environmental variables (e.g. winter and summer). The analytical experiments to quantify such varying waste

composition can be labor intensive and cost ineffective; but it is essential to inform the technology design to maximize waste

recovery. Moreover, planning (e.g. sizing and logistics) and operation of waste recovery facilities requires continuous and

consistent waste feedstock supply; whereas it is difficult to precisely quantify the waste availability in particular OFMSW

volumes due to its low traceability and mixed waste collection system origin. Notably, household waste (27.3 million tons in

2016) dominates the local authority collected MSW in the UK (~90%)7 and shares over 70% of the food waste stream.10 These

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lead to inhomogeneous OFMSW streams driven by household behaviors and consumption trends which are not only affected

by environmental factors but also socio-economic variables at local level (e.g. income). Such challenges have been highlighted

in a previous study11, where the authors pointed out the energy recovery barrier is the inability to quantify the garden waste.

Thereby, technology implementation and waste value chain transformation call for robust projection of waste feedstock

quantity and quality (composition) at spatial and temporal scales.

This study aims to project variability in organic solid waste generated in a given UK local authority, accounting for socio-

economic and other environmental factors. Our research focuses on food and garden waste streams as important components

of OFMSW. The chemical compositions and potential conversion pathways of food and garden waste are presented in Figure

SI- S1 in Supporting Information (SI).

Methods for forecasting OFMSW

There is an increasing research interest in forecasting MSW generation in the context of informing local governments to

plan efficient waste management systems.12. A variety of advanced techniques have been used to forecast MSW generation,

which can be broadly classified as follows: descriptive statistical methods13 ; material flow models14 ; regression analysis15 ;

time series analysis16,17; and artificial intelligence models18,19. A list of advanced MSW forecasting techniques and their features

is presented in Table SI-S1.

Descriptive statistical methods typically use demographic information such as population growth and average waste

generation per capita as the main predictor, however this method is prone to inaccuracies due to the dynamics of the MSW

generation process.13

Material flow models have been adapted to predict waste generation under various social and economic scenarios. 14 This

approach fully characterises the dynamic nature of MSW generation, however it is typically applied to total waste rather than

collected waste.20. Hekkert et al.21 highlighted that comparisons of the results of material flow analysis with real observed

waste data on the highest aggregation levels were questionable due to the presence of different aggregations or low consistency

within the studies.

In regression analysis, MSW generation is correlated with economic and demographic variables. 19 The suitability of this

method for a complex real world problem such as MSW generation is limited due to strict requirements placed on the input

variables. These requirements include independency of explanatory variables, constant variance and normality of errors in

order to conform with fundamental regression assumptions.22

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In contrast with the aforementioned methods, time series analysis is independent of demographic and socio-economic

factors and relies only on historical waste data. A non-linear dynamics based prediction technique was used to forecast waste

generation and compared to a traditional time-series approach known as seasonal AutoRegressive and Integrated Moving

Average (sARIMA).16

More recently, machine learning and artificial intelligence techniques such as support vector machines (SVM) and artificial

neural networks (ANN) have been used to predict MSW generation on long, medium and short-term scales. 23,24 Zade and

Noori 25 used ANNs to forecast weekly waste generation and Abbasi et al. 26 used partial least squares (PLS) for feature

selection with SVM for waste forecasting.

Our research differs from previously published literature which modelled total MSW volumes. Our research focuses on

waste composition projection by accounting for two categories of OFMSW – food and garden waste. In the specific context of

waste-to-resource transformation, the organic waste fraction of MSW is unique in the complexity of its environmental impact

and its potential for value-added product recovery.

This research uses a gradient boosting model, which is based on decision tree regression models. Gradient boosting has

demonstrated the ability to model complex non-linear relationships between variables and has proven higher prediction

accuracy than traditional time series approaches such as AutoRegressive and Integrated Moving Average (ARIMA). 27 The

major drawback of the ARIMA model is its assumption of a linear relationship between independent and dependent variables

which does not mirror the complex nature of real world relationships between variables.28

When compared to widely adopted ANN models, the gradient boosting method has many distinct advantages. There are

typically fewer hyper-parameters to be tuned and methods of interpretation are better developed than for ANNs. More

importantly for our application, the gradient boosting implementation used in this paper has an in-built method for handling

missing values in the data, whereas ANNs are less capable in this regard.

Finally, different from approaches adopted in previous research which focused on MSW forecasting for a given region, this

study investigates the transferability of the OFMSW prediction models to regions not included in the training set. Such

generality would provide a promising means to quantify the future availability of food and garden waste and inform local

authorities currently without collection schemes in place. Our proposed approach has the potential to benefit ODA countries

where the increasing waste trends are particularly intense.2

4

Materials and Methods

Data Collection

An extensive literature review of past MSW forecasting research highlighted population, socio-demographic and climate-

related variables as the most important contributors to MSW generation.23,27,29 The selected features (Table 1), were therefore

chosen to reflect each of these factors.

Table 1 External features used in models for food and garden waste

Features Period Source

Population of Authority Yearly WDFArea Population DensityIndex of DeprivationTemperature Monthly CEDARainfallSolar radiationFraction of population living in rural areas Yearly ONS

Unemployment rate Yearly ONSHousehold income Yearly ONS

Local authority waste data was obtained from the UK municipal waste database Waste-Data-Flow (WDF).30 The data was

provided for each quarter spanning the 7-year period of 2009 - 2016. Each entry in the dataset provided information on the

name of the local authority, period of interest, type of material collected, collection method, number of households served by a

waste collection service and collected waste tonnage. Each entry is provided for a combination of a given authority and a given

quarter, e.g. City of London, January-March 2016. This combination of authority and quarter defines an ‘example’. The actual

number of examples available for training the model was limited by the fact that only a subset of local authorities have

separate collection schemes in place for food and garden waste. The availability of garden and food waste data across different

UK local authorities is illustrated in Fig. 1.

Additional datasets were incorporated into the feature set to provide contextual information about the authorities. This

included meteorological data (e.g. mean temperature, rainfall and solar radiation) obtained from the Centre for Environmental

Data Analysis (CEDA), as well as socio-demographic data (e.g. household disposable income and rural-urban classification)

from the Office of National Statistics (ONS).

5

Figure 1 Availability of garden waste (A) and food waste (B) data across the UK (number of data points per authority)

Modelling Approach

A Gradient Boosted Regression Tree (GBRT) model was used to predict long-term waste generation. GBRT is used here to

estimate waste volumes using only contextual features of a given local authority. This process is performed in three stages-

training, cross-validation and testing (see Figure SI-S2 for a graphical outline of the process). First the model is trained to

identify patterns between features and the target variable, waste volume. In the cross-validation step, the model parameters are

fine-tuned to improve model performance and ensure that the model can generalise to examples beyond those it was trained

on. Finally, the model is tested on a number of unseen examples and its prediction accuracy is determined. Other model types

were examined and tested, including support-vector-machines and random forests, but the GBRT model was found to be most

effective for the two specific use cases in this paper (see Tables SI-S2,3 for further details).

The GBRT method combines the strengths of two algorithms: regression trees and boosting. The resulting model is an

additive regression model comprised of decision trees fitted in a stage-wise manner. Decision trees partition the feature space

into regions and use a series of rules to identify regions with homogenous responses to predictors.31 A different linear model is

then fitted to each region. This binary partitioning is performed recursively and at each stage, the splitting variable and split

point are determined using a greedy recursive-partitioning algorithm.

Fig. 2 illustrates the binary partitioning process in the context of our research. Consider two predictor variables X 1 and X2,

representing population density and mean temperature of a specified local authority at a given point in time, and a response Y,

the total waste generated in the area. Splitting occurs at the first node using population density as the splitting variable. The

next step involves splitting at the subsequent node, using temperature as the splitting variable. The recursive splitting process

A BV

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is stopped once a defined minimum node size is attained and the resulting large tree is pruned using a process called ‘cost-

complexity pruning’. This process involves removing weak links identified through cross-validation. The result is a single

decision tree which best describes the underlying relationship between a set of variables.32

Figure 2: (A) visualization of a decision tree using two demonstrative predictor variables (population density and temperature) to generate a single response (waste volume); (B) regression tree tuning parameters.

GBRT is an extension of traditional regression trees which incorporates a statistical technique known as “boosting”. This

procedure improves prediction accuracy by combining the outputs of “weak” classifiers to form a single consensus model.

Hastie et al.31 defined a weak classifier as ‘one whose error rate is only slightly better than random guessing’. In the context of

regression trees, boosting is a form of functional gradient descent that optimises performance by adding a new tree at each step

that minimises the gradient of the loss function.32 The first regression tree is the one which minimises the loss rate to the

maximum extent, subsequent decision trees are then fitted to the residuals of the preceding tree. It is also important to note that

although the model is updated each time a new tree is calculated, the existing trees remain unchanged. Instead, the linear

model fitted to each observation is re-calculated to account for the effect of the new tree.31

In order to optimise predictive performance, several parameters including number of estimators, maximum tree depth and

learning rate were tuned. Tuning was performed using graphical methods of comparing training and cross validation

accuracies, with the aim being to maximise the R2 score for the cross-validation set. The number of estimators determines the

maximum number of trees in the model, as illustrated in Fig. 2, while tree depth determines the degree of interaction between

features. Friedman et al.33 states that ‘since each new tree builds on the residuals of the previous tree, shallow trees with a

depth of 4–6 are often preferred’. Learning rate is another key parameter that determines the weighting of each tree in the final

model. A low learning rate will increase the number of trees used and allow regularization of results. Ultimately, this results in

Maximum tree depth

BA

7

Temp. >= 17 C

Waste = 250

Temp. < 17 C

Population Density >= 37 ha-1Population Density < 37 ha-1

Number of estimators

... +++

better model performance and a reduced risk of over-fitting to the training dataset despite a subsequent trade-off in

computation time.34

The final model is a linear combination of all decision trees whose contribution to the overall model is weighted by the

learning rate to minimise the root mean squared error loss function.34 XGBRegressor was implemented using Python’s

XGBoost package version 0.81.

Feature Engineering

Features were mapped to each UK local authority on a quarterly basis from 2009 to 2016. UK weather data was obtained

from CEDA at a grid resolution of 5x5 km2. These were mapped to the relevant regions by locating the nearest grid point to

each local authority with grid point locations determined by minimizing the Euclidean distance between the grid points and the

longitude and latitude given for the local authority. A small number of these authorities were excluded due to discrepancies in

nomenclature between waste authorities and local authorities.

Once the feature mapping stage was completed, features were checked for co-linearity and excluded if their linear

correlation R-squared score exceeded 0.8. Features excluded by this method include local authority dwelling stock and a

number of metrics related to the rural population in local authorities. Feature choices were kept consistent for all model types

to allow comparison of feature importance and more importantly, model accuracy.

Models

Two model types were developed - a forecasting model and a “peer” model. For each of these model types, individual sub-

models were created for food and garden waste.

The forecasting model was developed to predict food and garden waste volumes for future time periods. The food and

garden waste sub-models were trained using data from 2009-2013 for all applicable local authorities and then tested for the

period from 2014-2016. The mean waste volume prediction was compared to actual waste data for this test period. The

purpose of the forecast model is to predict waste volumes in the future for local authorities which already have collection

schemes in place to aid in planning.

The peer model was developed in response to an observed gap in the literature regarding the generalisability of these waste

prediction models to regions which the model had not previously been trained on. The purpose of this model was therefore to

predict OFMSW volumes based only on intrinsic information about the local authority for a given period such as population

8

and weather patterns. In our specific case, the purpose of creating a peer model is to be able to extrapolate forecasts for organic

waste to those local authorities that do not yet have organic waste collection schemes in place, to highlight the ‘lost potential’

for organic waste collection. As such, it could be used to support the decision to implement such a scheme in a local authority.

The peer model was trained on 60% of the 327 local authorities over the entire time period from 2009-2016 and tested on

the other 40% of local authorities which had not been seen by the model. The predicted waste volumes for the unseen local

authorities were compared to actual waste volumes to determine the accuracy of the peer model. Table 2 shows the tuning

parameters used for each model type on the garden and food waste streams.

Dataset splitting

In order to obtain training, cross validation and test sets for the peer model, the original datasets were split such that similar

waste volume distributions were observed across all three sets. Authorities were ranked by their mean waste volume and split

into quintiles based on this metric. Finally, stratified random sampling was used to split the authorities into training, cross

validation and test sets, stratifying on these quintiles.

Model evaluation

The model performance is measured by a coefficient of determination R2 (Eq.(1)) measured against a baseline model (i.e.

mean of the waste volume in the training set).

R2=1−∑

i( y¿¿i− y train)

2

∑i

( y¿¿ i− y i , pred)2(1)¿

¿

where y iand y i , pred denote the true and predicted waste volumes, respectively; y train represents the mean waste volume in the

training dataset.

This coefficient of determination can be calculated for each of the training, cross-validation and testing datasets for each

model. The models’ parameters are tuned with the aim of maximising the cross-validation R2.

Model Interpretation

A number of model interpretation techniques could be applied in waste forecasting models to provide useful insights into

individual feature contribution to the model’s decision-making process. Lundberg et al. 35 showed that a widely adopted

9

approaches such as split count and gain are inconsistent metrics of feature importance and proposed a new method, known as

Shapley Additive Explanations (SHAP), where SHAP advantages and limitations are thoroughly discussed. SHAP combines a

number of Additive Contribution Explanation algorithms to provide a measure of feature contributions that meets the

requirements of local accuracy, missingness and consistency35.

SHAP values measure the contribution of each feature to the model’s output for a certain example. The mean absolute

SHAP values across all examples for each feature are calculated to evaluate the importance of the features for the model.

Higher mean absolute SHAP values indicate that those features generally have a more significant overall additive contribution

to the model’s output. In this study, SHAP values were calculated using the TreeSHAP algorithm 36, implemented in Python’s

SHAP library, version 0.25.2.

Results and discussion

Model Performance

Table 2 shows the prediction accuracy for both the forecasting and peer models for each waste type, as measured by the R 2

value across the entire training/cross-validation/test set. Overall, the forecasting model shows good prediction accuracy on

both food and garden waste. For both model types, test accuracy is consistently higher for garden than food waste, with a 29%

and 11% increase in R2 value for garden waste compared to food waste from the forecasting and peer model respectively. The

peer model shows significantly lower test accuracies for both waste types with an R2 of just 0.29 for garden waste due to the

inherently difficult nature of predicting the waste output of previously unseen local authorities.

Table 2 Models for food and garden wasteForecasting and peer model parametersModel type Waste Type Tree Depth Number of Estimators Learning RateForecasting Garden 4 8000 0.025

Food 4 1000 0.025Peer Garden 3 900 0.004

Food 2 1800 0.006Model prediction resultsModel Type Waste Type Accuracy (R2 score)

Training CV TestForecasting Garden 0.994 0.770 0.651

Food 0.951 0.691 0.506Peer Garden 0.719 0.549 0.291

Food 0.787 0.373 0.262

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Figure 3. Model predictive performance on mean quarterly collected garden (A) and food waste (B) per authority for training and test set from 2009 – 2016.

Fig. 3 compares forecasting model prediction results for mean quarterly garden and food waste, to actual collection

volumes. The quarterly prediction results were averaged across all local authorities to demonstrate mean waste volumes for the

UK as a whole.

On aggregate, model accuracy is higher at the national level compared to the local authority level with test R 2 scores of

0.766 and 0.899 for garden and food waste models, respectively. The forecasting model for garden waste follows the training

set very closely but is less accurate when applied to the test set. However, it captures the seasonality of garden waste

generation indicated by peaks and troughs. The forecasting model for food waste captures the increasing trend in waste

collection overtime as well as short-term changes in the overall trend.

4.2 Feature Importance

Fig. 4 shows the relative feature importance for the garden and food waste prediction models. The results indicate that

population is the most important feature. However, the importance of socio-economic and environmental factors varies

significantly between the models. Monthly solar radiation and mean temperature are significant features for the garden waste

prediction models, however they are relatively unimportant for the food waste models. Socio-demographic variables, such as

population dynamics, gross disposable household income per head and index of deprivation appear to be more important

features for food waste prediction models.

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BA

Figure 4. Relative feature importance for garden waste (A) and food waste (B) prediction models.

Figure 5. Correlation between magnitude of feature values and impacts on prediction model outputs for food waste (A) and garden waste (B)

Fig. 5 illustrates the degree to which model output is positively or negatively impacted by the magnitude of the value of each

feature. Feature impact on model output was measured using SHAP. Both models demonstrate that more populated regions

have a high positive impact on model output. Lower population values show a receding influence on the output, but the

magnitude of this influence is noticeably smaller than that for high population values. This suggests that for higher

populations, the population size itself becomes a more dominant feature, for waste prediction, while for lower populations,

other features are emphasised to a greater degree.

Discussion

A B

A B

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The forecasting models provide robust projection for two types of OFMSW i.e. food and garden waste. They represent

carbon and nutrient rich resources for recovery including carbohydrates, lipid, protein, cellulose, hemicellulose and lignin.

However, more robust results were obtained when predicting garden waste volumes compared to food waste. This is largely

due to the availability of the features characterizing each waste type. We hypothesized garden waste collection to be

influenced primarily by weather-related factors and these are represented well by the feature set used for model training.

Conversely, food waste collection could be influenced by individual household behaviours in addition to socio-economic

variables. These are more difficult to quantify on a local authority basis therefore the feature set is less representative for food

waste.

Feature importance results provide insight into the relative weighting of each feature in the model’s decision-making

process. Population is the most important feature for both model types as expected (more inhabitants generate higher levels of

OFMSW), while monthly sunshine is a more dominant feature for garden waste than mean temperature. More generally,

weather reflects the seasonally varying features, acting as the second most important feature for garden waste prediction

models, indicated by high mean SHAP values for monthly solar radiation and temperature. The effect of using weather as a

feature is reflected by the model’s capability to accurately capture the seasonality of garden waste generation (see Fig 3B).

Additionally, monthly mean temperature and solar radiation are strongly correlated to their impacts on predicted waste

volumes.

Model predictive performance is subject to a number of data and methodology limitations. The presence of incomplete and

noisy data and the inconsistencies in reporting standards across the UK resulted in discrepancies and missing data. Moreover,

variables such as community acceptance of waste-recovery and the differences in waste collection methods between local

authorities are unknown and hence not considered in the models.

Reported accuracies of the peer models are highly dependent on the local authorities included in the separate training, cross

validation and test datasets. As a consequence, variations in prediction accuracy were observed when randomised stratified

sampling was repeated. The generalizability of the models to ‘unseen’ local authorities therefore relies on sufficient

similarities between local authorities in the training and test sets. As only a limited number of local authorities provide

consistent waste data, ensuring a representative split across the training and test sets without introducing external bias is a non-

trivial problem. The results imply that transferring models to local authorities without data with which to retrain is not

straightforward with a data-driven approach such as this. The variability in R2 across different authorities can be seen in

Figures SI-S3,4,5,6.

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In addition, forecasting model accuracy is impacted by variations in the number of food and garden waste collections

across local authorities. Authorities with a higher number of waste collections are represented better by the forecasting model

than those with only a few. This is likely to result in increased model accuracy for such authorities compared to those which

are less well represented in the data.

Overall, the strong performance of the forecasting models at the local authority and national level proves their suitability

for projecting OFMSW variability in waste quantity and quality (composition). Such projection could inform the future

OFMSW commoditization where the commodity grade definition are dependent on waste composition and recovery values as

energy carriers or nutrient substitutes. Furthermore, the spatial granularity of model predictions offers a promising approach to

inform the decision-making on technology choice, sizing, location, and logistics of OFMSW recovery facilities. These

underpin the potential transformation of waste value chains from retrospective waste management to proactive resource

recovery in a coordinated OFMSW value chain, where waste resource and facilities are interconnected via the internet,

supporting real-time decision-making (following an Industry 4.0 framework).

In future research, model performance may be strengthened by improving data quality and incorporating more features

which reflect the dynamics of waste collection, e.g. collection schedules, population demographics and the number of

households registered for OFMSW collection. Further benefits can be realised from the models investigated in this study by

extending their prediction capabilities to account for variability in OFMSW chemical composition. Determination of model

prediction uncertainty could be another important step towards industrial application of this research e.g. prediction

uncertainty quantification with lower/upper bounds for waste forecasts to optimise responsiveness of waste processing

operations.

Acknowledgement

MG would like to acknowledge the UK Engineering and Physical Sciences Research Council (EPSRC) for providing

financial support for EPSRC Fellowship ‘Resilient and Sustainable Biorenewable Systems Engineering Model’

[EP/N034740/1].

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Supporting Information

The following material has been presented in Supplementary Information for Publication in order to enhance the reader’s

understanding of this research and the proposed methodology:

1. Tabulated outline of advanced techniques for MSW forecasting including types of application areas and waste streams

2. Superstructure showing compositions and conversion pathways for food and garden waste constituents of organic waste

3. Graphic showing methodology for development of the gradient boosting model

4. Comparison of performance of tested models on forecasting and peer prediction tasks for green garden waste

5. Figures showing variations in R-2 scores at the local authority level for food and garden waste across the forecasting and

peer models

For Table of Contents Use Only

Predictive analytical techniques are applied to organic-fraction municipal solid waste, enabling effective waste-to-resource

transformation systems to close the loop on the circular economy.

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