Investigation of the Socioeconomic Factors Influencing Municipal Solid Waste Generation and Development of Waste Generation forecast Model Using Machine Learning for Dubai

1. American University of Sharjah
College of Engineering
Engineering Systems Management Program
ESM 685: Capstone Course
Spring 2021
Instructor: Prof. Vian Ahmed
Investigation of the Socioeconomic Factors Influencing
Municipal Solid Waste Generation and Development of Waste
Generation forecast Model Using Machine Learning for Dubai
Report 2 – Research Methodology
Group B
Christina Varghese 81439
Jihane Ahmed 81393

2. Table of Contents
1. Main Findings from Literature 4
1.1 Factors that Influence Waste Generation 4
1.2 Application of different machine learning models in Solid Waste treatment 6
2. Research Methodology 8
2.1 Purpose of Analysis 9
2.2 Research Philosophy and Approach 12
2.3 Research Strategy 14
2.4 Data Collection Strategy 16
2.4.1 Literature Review Approach 16
2.4.2 Quantitative Approach 16
2.5 Stages of Machine Learning in Research 17
2.5.1 Data Availability and Collection 19
2.5.2 Data Pre-Processing 20
2.5.3 Removing Missing Values 20
2.5.4 Machine learning Model 21
2.6 Strategies for Data Analysis 22
2.6.1 Socio Economic Factor Analysis 22
2.6.2 Waste Forecast Analysis 24
2.6.2.1 Linear Regression Model 24
2.6.2.2 Support Vector Machine Model 25
2.6.2.3 Artificial Neural Network Model 25
2.7 Limitations 26
2.8 Validity and Reliability 27
2.9 Ethics 30
3. Conclusion 31
4. References 34

3. List of Figures
Figure 1 Appearance of various machine learning models employed in Solid waste 5
Figure 2 Stages of Machine Learning approach in research 10
Figure 3 Example of Structure of an ANN model 15
List of Tables
Table 1 Research Questions Mapping 16

4. 1. Main Findings from Literature
1.1 Factors that Influence Waste Generation
While the previous sections describe the different stages of waste management and the
challenges faced in UAE to achieve their sustainable goals in the waste management category,
data on waste generation is essential to understand the very need for waste management. Hence,
the waste generation forecasts and the factors that influence waste generation would provide
better insights towards effective waste management plans and strategies. Therefore, this section
aims to highlight the various factors that influence waste generation in a region and thus choose
it for the research study. [56]
Identifying these factors is challenging and the most important considerations in MSW [2]
forecasting. On reviewing the literature, many of these factors are mentioned in [1-6] Several
studies develop models or algorithms that have used these factors for waste forecasting.
However, many of them could not address the waste generation by different activities and from
different social groups. Based on the existing studies, the factors studied can be overall
categorized as economic status [7-11], demographic [12], household-related variables [13],
waste management measures[14,15], and local policy and regulations [7,15]. These factors are
considered as their composition is different in each sector.
A study by (Zhu et al., 2008) described that waste management measures involve facilities such
as dumpsites, collector bins, transport facilities, recycling facilities, and policy regulations were
determined to understand if there was any impact due to certain fees (for disposal, incineration)
or reward system for recycling that are enacted in the region. The study of (Milea 2009;
O'Connell, 2011) described that general socioeconomic factors which are considered as

5. influencing factors of MSW generation are GDP, urban Population, urban paved roads, per
capita consumption expenditure, energy consumption, geographical location of area are
effective waste management techniques [2,15]. Previous studies have shown that the combined
factors of GDP and urban population growth are the most important socioeconomic drivers of
MSW generation. [17-19].
These studies included socioeconomic factors such as GDP, urban population, paved roads, per
capita consumption expenditure, and energy consumption are strong waste management
approaches. Studies that have found that GDP and urban population expansion are the main
causes behind MSW generation have been done previously.
The investigation in this research also describes that facilities such as landfills, waste disposal
facilities, waste collection facilities, transport facilities, recycling facilities, and municipal
waste disposal and recycling regulations all contribute to understanding whether or not there is
any influence of certain fees (for disposal, incineration) or a recycling reward system that is
put in place in the area. The previous studies and concepts will be kept in the view while
discussing the research.
In other words, in several regions, MSW has increased with the rapid growth in population and
rapid urbanization. Similar to the researchers in [6,7] who developed models, based on the
interrelationships of economic, demographic, housing structure, and waste management policy
variables influencing the rate of solid waste generation, there are very few studies that we're
able to consider all these factors due to the inaccessibility of data, time consumption, etc.
Hence, it's very evident that most scholars have considered socioeconomic factors, such as
population size or economic status, such as GDP. The data of the latter were more commonly
accessible in the respective regions[20,21]. As there are no general research studies conducted
in the field of waste indicator influence in UAE, these papers have enabled shortlisting of the
possible waste indicators to be evaluated with respect to the emirate of Dubai. At the First

6. Annual Waste Management Conference in Dubai, the UAE (Al-Sayigh 1993) presented a paper
on recycling and composting, organizing, and retrieving data. Storage and transportation and
waste disposal. the overall effectiveness of the system was, in his opinion, the following: much
of the financial Improvement was made in the waste management system unadulterated beauty.
1.2 Application of different machine learning models in Solid Waste
treatment
On reviewing the various machine learning approaches in the solid waste treatment and
management papers, fig.1 reflects the adopted machine learning models in solid waste
treatment and their percentage. Organic solid waste treatments, which often include recycling
and composting, often have limitations, such as low effectiveness, low reliability, high expense,
and the possibility for environmental pollution. The amount of interest in machine learning to
solve organic solid waste treatment's challenging difficulties has grown in the previous decade.
This topic of study is severely lacking in a comprehensive review of the findings. In his study
[22] states about classifying machine learning studies from 2003 to 2020 and summarising the
machine learning model suitability for various application domains, as well as the machine
learning model's applicability's limitations and future possibilities. Based on his study, the
research conducted with regards to municipal solid waste management was the most prevalent,
with additional research done on anaerobic digestion, thermal treatment, composting, and
landfill. An artificial neural network (ANN) is one of the most extensively employed models
for tackling a wide range of non-linear organic solid waste challenges. Studies adopting
Artificial Neural Networks (ANNs) account for 53.89%, followed by Support Vector Machine
with a percentage of (15%), Genetic Algorithm with (9%), and Random Forest along with
Decision tree analysis (7%). Other models such as Multiple Linear Regression (MLR), K-
nearest neighbor (KNN), adaptive network-based fuzzy inference system (ANFIS), gradient
boosting machine(GBM), gradient boosting regression tree (GBRT), GEP, and KMC account

7. for 15% of the total. Although conventional treatment and recycling methods for solid solid
waste have inherent shortcomings, such as low performance, low accuracy, high cost, and
environmental risks, they are often preferable to alternative approaches with no
advantages.[66] As the application of machine learning to the management of organic solid
waste has grown in prominence over the past decade, so has its use in tackling the growing
number of difficult problems. However, extensive research has been done, but the literature
lacks a comprehensive analysis of findings. This report compiles and summarises all research
papers published between 2003 and 2020, outlining their respective implementation areas,
features, and applicability of various machine learning models. [65]
Additionally, it assesses the strengths and weaknesses of the proposed solutions, as well as
predicting potential prospects. Municipal solid waste management studies accounted for the
vast majority of research done in this region, with anaerobic digestion, thermal treatment,
composting, and landfill making up the rest. An artificial neural network (ANN) is the most
commonly used model in the field of nonlinear organic solid waste (NOLSW) since it has
proven helpful in solving many difficult problems [60].

8. Figure 1 Appearance of various machine learning models employed in Solid waste [55]
The study of [22] is limited to these operational and organizational issues. A major constraint
is the absence of an efficient policy implementation mechanism and technological standards,
in the public and policymakers' general lack of knowledge and education of the climate. Thus,
it merely compounds the issue of waste management, making it even more unsustainable.
Systems for management empowering and empowering culture contextually integrated,
complex, yet adaptive semantic web-based modeling systems if any genuine progress is to be
made emerging-world SWM activities. Among the several discussed machine learning
approaches, this research would opt for the two widely used approaches. Artificial neural
networks (ANN) and support vector machines (SVM) and a simpler model, multiple linear
regression model, predict the waste forecasts for the yeast 2021-2030 in Dubai.
2. Research Methodology
Based on the literature review findings and the research questions:
· How can machine learning be applied in solid waste management?
· What are the possible variables that cause waste production?

9. · Is there a significant relationship between socioeconomic factors such as the
quantity of water consumed, total buildings under construction, and the number of
visitors and waste generation in Dubai?
· Why does Dubai/UAE need to expedite the implementation of effective and
sustainable waste management techniques?
The factors such as the quantity of water consumed, total building under construction, and the
number of visitors and waste generation in Dubai are also significant factors of waste
production. The methodology of the research will be discussed in the following sections. It will
focus on different methodology aspects, which are the research approach, strategies, and
research methods used to achieve the aim and objectives of the research. The research aims to
identify the factors that influence waste management and how effective and sustainable waste
management techniques can reduce management. It will also discuss the data collection
techniques and data analysis methods. In addition to these, ethical considerations practices and
the validation of data to confirm consistency are covered.
2.1 Purpose of Analysis
Based on the discussed main findings, it is understood that there is a lack of detailed studies in
UAE regarding waste management; however, with the boosting economy, the solid waste
generated continues to increase. Therefore, this section aims to briefly highlight the reasons for
pursuing this research and the direction.
Consisting of seven emirates in total, UAE's Population is varied across each of them, with
Abu Dhabi, Dubai, and Sharjah having the most [23]. Over the recent years, the Emirate of
Dubai has noticed substantial economic growth. Similarly, changes in the field of waste

10. management have also been observed. However, the primary challenges currently faced in
UAE include the lack of research conducted in this field and the lack of effective and
sustainable waste management methods to improve waste management in the country.
This study intends to examine the relationship between a few major indicators, such as GDP
and Population, with the waste generated for each year in the region. “The value of all products
and services generated inside the borders of a country throughout a year is known as GDP.
The growth rate of the gross domestic product (GDP) is a vital measure of a country's economic
health.” GDP increased by 1%, leading to a rise in municipal service of 1.76% on average as
domestic waste. This estimation was relevant to 5% of the population. When population growth
rises by 1%, it would likely result in a 0.11% rise in municipal waste. This estimation was
necessary at the 95% confidence level. Over the past 10, population and GDP in emerging
destinations has grown at a rate of 4.4% a year whereas established economies' growth will be
just 2.2% a year. [67] Health is dependent on socioeconomic factors such as jobs, education,
and income. Socioeconomic issues include economics applied to culture as well as society
itself. This combination is mutually interdependent. Therefore, GDP and population play an
essential role in the production of waste. After which, a forecast model for solid waste
generation in the area for the next ten years, 2021-2030, would be studied to understand the
need to enforce sustainable methods sooner, using a few machine learning approaches. To
conduct this research, consistent data is critical for a fruitful study. Factors such as quantity of
water consumed, total buildings under construction, and the number of visitors and waste
generation in Dubai are considered in research as a variable that will be discussed and focused.
Solid waste and its types
Municipal Solid Wast

11. Waste created from residences, offices, hotels, stores, schools, and other organisations is
referred to as municipal solid waste (MSW). Food waste is the biggest component. This
includes paper, plastic, rags, metal, and glass. Debris from demolition and construction, like
wood and metal, is frequently included of waste that has been put in collection, as well as minor
quantities of toxic and hazardous trash, such as light bulbs, batteries leftover medication and
trashed automotive parts non-renewable.
Other forms of solid waste is industrial waste and agricultural waste production.
Therefore, the analysis on the various waste indicators and forecasts will be conducted for the
emirate of Dubai only because the database and sampling collection is only limited to Dubai.
In other words, this research intends to explore the answers to the following questions:
· How can machine learning be applied in solid waste management?
· What are the possible variables that cause waste production?
· Is there a significant relationship between socioeconomic factors such as the
quantity of water consumed, total buildings under construction, and the number of
visitors and waste generation in Dubai?
· Why does Dubai/UAE need to expedite the implementation of effective and
sustainable waste management techniques?
Based on these four research questions, the research approach, in general, is a mixed approach.
The first two research questions are approached through the literature review and hence
considered as an exploratory approach. In contrast, the remainder questions are explained using
quantitative data, which would be discussed in the data collection section. It is examined using
multiple linear regression models and then forecasted using three suitable machine learning
approaches.

12. 2.2 Research Philosophy and Approach
Research philosophy is how data should be collected, analyzed, and used concerning a
phenomenon. [22] It is essential to indicate the type of the research philosophy to identify the
research design suitable for the research to find answers for the research questions. Based on
the research questions, the best research philosophy suited is a positivist philosophical stance.
In a definition, positivism states that only "verifiable" information, which can be obtained
through the senses (such as by measuring), is accurate. The researcher's position in positivism
studies is limited to gathering data and interpreting it by employing an analytical approach.
Observable and quantifiable scientific results are also the product of research. The core of
positivism is derived from facts and figures that contribute to statistical analysis. It is the
empiricist view; positivism is in agreement with positivism. Awareness comes from our
experiences as human beings. It believes the universe is made up of small, basic particles with
an atomistic, ontological viewpoint. We are composed of separate, perceivable components
and occurrences that communicate logically, which can be observed and regular, or ordinary,
method. The positivist approach will capture the quantitative methods to visualize patterns,
work on quantifiable observations, and obtain some statistical analysis. [26]
The research aims to identify the factors that contribute to waste generation and develop a
forecasting model to predict the waste generated in the UAE based on the identified factors.
According to the nature of the research problem, the most suited research approach is the mixed
approach, in which both explanatory and exploratory methods are adopted. The exploratory
approach allows for the exploration and identification of the factors contributing to waste
generation. According to [24], an exploratory kind of approach typically leads to a superior
comprehension of the current issue; however, it doesn't prompt a real outcome for the most
part. Therefore, analysts utilize an exploratory approach to acquire knowledge of a current
phenomenon and gain a new understanding to frame a more exact issue [24].

13. It starts dependent on an overall thought, and the approach's results are utilized to discover
related issues with the subject of the exploration. For example, in an exploratory approach, the
interaction of the exploration fluctuates as indicated by the finding of new information or
knowledge. Likewise, interpretative exploration or grounded hypothesis approach, the results
of this approach give answers to questions like what, how, and why [25]. Then, this approach
aims to answer the following research questions:
● What are the possible factors that influence a waste generation?
● How can machine learning techniques be applied in different stages of solid waste
management?
● Why does Dubai/UAE need to expedite the implementation of effective and sustainable
waste management techniques?
On the other hand, the explanatory approach helps this research by establishing causal
relationships between variables; in other words, this approach supports our concern to assess
how one variable is responsible for changes in another variable. [27[ An example could be
finding the causal relationship or correlation between the amount of waste and the population
size. This explanatory approach intends to address the following question:
● Is there a significant relationship between population growth, GDP, and waste
generation in Dubai?
To answer this question, the below hypotheses are constructed to determine if the independent
variables, i.e., socioeconomic factors, can be considered as waste indicator(s) for Dubai. The
following hypotheses are created and must be tested in this research;
H01: Gross Domestic Product significantly affects the waste generation in Dubai.
H02: Population growth rate significantly affects waste generation in Dubai.
H03: The quantity of water consumed significantly affects waste generation in Dubai.

14. H04: Total buildings under construction significantly affects the waste generation in Dubai
H05: Total number of visitors significantly affects the waste generation in Dubai.
Overall, this research approach adopts a mixed approach, adopting both deductive and
inductive methods. Also known as inductive reasoning, induction is an approach in which
hypotheses are developed based on the data. Then, to draw conclusions, patterns, resemblances,
and regularities in experience (premises) are observed (or generating theory). In deductive
reasoning, a general premise leads to a particular inference. Moving from the general to the
specific is known as top-down thought. We can identify and deduce from the literature the
factors that influence the waste generation and how machine learning is valid in solid waste
management through the deductive approach. Whereas the inductive approach supports
devising new findings, such as the waste indicators for Dubai and the waste generation forecast,
based on the results generated from machine learning which would generate new data that
allows for decision making in reality. [63]
2.3 Research Strategy
According to the nature of the research study, the research is based on the statistical data for
the waste amount already available at online resources. This research comprises two parts; in
the first part, the researchers need to explore "How" and "What" impact or factors influence
the waste amount. The research will need to predict the waste amount for the next 10 years
(2021-2030) in the second part. This implies that this research requires evaluating the effect of
more than independent variables on a dependent variable. Therefore the most suited strategy
of this research is by conducting non-Experiment design. Ex post facto study or after-the-
fact research is a type of research design in which the investigation begins after the fact has
occurred without interference from the researcher. It is a type of research design known as

15. after-the-fact research involves research in which the study commences after an event has
occurred with no researcher influence. Social research is almost entirely focused on
retrospective studies in which there is no way to change the characteristics of human
participants. It is commonly utilised as a substitute for legitimate, real-world research to
examine causal linkages or if it is simply not feasible or ethical to follow the complete research
process of a bona fide experiment. Despite analysing historical facts, post facto research shares
some of the underlying logic of inquiry used in experiments. [62]
Experiments enable us to pursue this research. Each factor is tested if it has any correlation to
the generated waste, thus choosing the waste indicators for Dubai accurately. In the world of
quantitative research, correlation coefficients are used to calculate the strength of a relationship
between two variables. Other common correlation coefficients include Pearson's. This is the
correlation coefficient that you can see in linear regression equations. Pearson's R is the first
statistics concept you can understand while you're getting started. Pearson's correlation
coefficient is almost always being used when someone refers to the correlation coefficient. If
Pearson's correlation coefficient is positive, the hypothesis would be considered true as it
signifies some impact on waste generation[36]. An experimental design means to create a set
of procedures to test a hypothesis [26].

16. 2.4 Data Collection Strategy
The process of collecting the data and its analysis for the qualitative and quantitative study for
this research will be discussed in this section. Based on the Research nature, research strategy,
and research question, the Quantitative data collection method is used for collecting the data.
This method is suitable for this research because the available sources are literature, theory,
and existing past data at online resources.
2.4.1 Literature Review Approach
As stated in the first research question, the first phase is collecting data and gaining knowledge
from the literature review on whether and how machine learning is valid in solid waste
management and its applications in the same field. This qualitative study would benefit the
researchers to navigate further and shortlist their approach to analyze data for the third question
and the fourth research question.
The second research question calls for finding the various waste indicators; hence, deducing
the various possible factors known to influence solid waste generation comprises the second
phase. The researchers dive and explore studies conducted in other cities and/or countries, as
there are no previous similar studies conducted in UAE or its emirates.
2.4.2 Quantitative Approach
In the third phase of the research, to the theory deduced from the literature review, which
highlights the various waste indicators, a quantitative data collection method is applied to
induce or collect information to justify the same for UAE and allow further analysis. The
statistical data for the derived factors would be sought after with respect to Dubai to identify if

17. there's any correlation to the waste generation for the same period. This calls for all the chosen
variables to have sufficient data for the periods same as the waste generation data for Dubai.
The period for which quantitative data would be collected is from 2000-2020. The waste
indicators and the waste generated would help the researchers evaluate and interpret the
correlation between each indicator and the waste generation for each year. A mere qualitative
data would not be sufficient to interpret if these factors influence solid waste generation in
Dubai.
After evaluating the various factors, the last phase, which is the core functionality of this
research, includes using the most related waste indicators to forecast the trend in solid waste
generation for the next ten years, i.e., 2021 to 2030.
2.5 Stages of Machine Learning in Research
Before implementing the machine learning models, some preprocessing techniques to clean the
dataset would be conducted. Then, according to the study, there may be some data
transformation. Finally, the Linear Regression, Support Vector Machine, and Artificial Neural.
Data
Gathering
Data
Arrangement
Data
Analsis
Evaluation

18. The machine learning algorithm is called linear regression and is used in supervised learning.
This does a regression calculation. The regression model projects a predicted value on the basis
of many factors. While forecasting is the most common use, it is mostly used to investigate
how variables are connected and to forecast. SVM is a supervised machine learning technique
that can be used for classification or regression tasks. It is usually utilized in classification
difficulties, though there are exceptions. An artificial neural network is a computational system
that loosely resembles a biological neural network, and is commonly referred to as a neural
network. ANNs are made up of nodes that function like artificial neurons, which approximate
the connections and functions of neurons in a biological brain. Neural networks function well
with linear and nonlinear data, however because of the vast diversity of training required for
real-world functioning, the systems that employ neural networks are commonly met with a fair
amount of criticism. A machine learning algorithm will only be able to understand the
underlying structure that permits it to generalize to new circumstances if it has abundant
instances of varying scenarios. Network models will be implemented while using forecasting
techniques and evaluating the machine learning performance using evaluation measures [59]
shown in the below figure 2.
Figure 2 Stages of Machine Learning approach in research
Note: The rest of the information will be discussed in the next section 2.6.2.3

19. 2.5.1 Data Availability and Collection
The UAE has one federal statistical data center that manages two open data portals, [27] and
[28], for research and other applications regarding the UAE in general. However, only two of
the seven emirates, namely Abu Dhabi and Dubai, have their statistical centers [29] and [30],
respectively. The major drawback of the federal statistical centre is that since they were
founded in 2015, as per the UAE Federal Law 2/2020, their data with regards to waste is not
available for much earlier than 2010, and the desired data for few indicators such as current
GDP, is not available for years prior to 2009. However, UAE data banks on the World Bank
portal [31] have data for most of the indicators for the desired year range and lack the waste
datasets. Therefore, the unavailability of sufficient waste datasets was the major reason for
eliminating this research on UAE as a whole and hence dotting either the emirate of Abu Dhabi
or the emirate of Dubai.
The statistical centres for Abu Dhabi and Dubai have been functioning over several years.
Although Dubai is older and hence has wider data sets for the desired waste indicators and the
waste generated, it led to choosing the emirate of Dubai to pursue this research.
Data collected from the Dubai Statistics Center & the official open data portal with the name
of the waste dataset. This dataset is freely available on this platform and can be used in machine
learning data analysis. Dataset would consist of 7 worksheets related to the total waste amount,
gross domestic product, Water consumption, building under construction, tourism -visitors,
population size, and main dataset worksheet.
Therefore, in order to evaluate the factors and the solid waste forecasting for Dubai, historical
data is primarily used from the yearbook, quarterly statistical reports, and other publications
issued by the Dubai Statistics Center and their official open portal. [90] Suppose there are any

20. gaps in data, such as for "Total collected wastes" for a particular year, in order to fix the
problem. In that case, there will be some approximations for those values based on values
preceding and following the required value. For example, if an estimation is missing for a year,
say 2004, it will be computed as an average of 2003 and 2005. [58]
2.5.2 Data Preprocessing
Data preprocessing and transformation is the second step after collecting a dataset [32]. In this
process, the goal is to try to understand the dataset according to the research study.
The first step is to handle the missing values, irrelevant values, and NA's values. Machine
learning models need data in the numerical form, so it is necessary to transform text data in
numerical form with the help of available natural Language Processing (NLP) approaches if
required [33].
2.5.3 Removing Missing Values
Missing values is the main problem in any dataset while implementing a model or training a
model with a dataset [34]. Therefore it is required to apply some data cleaning techniques for
handling missing values. First, all missing values will be replaced with the average value for
numerical data, and the missing values will be removed from categorical data. After this, the
model will be implemented in RapidMiner so; the RapidMiner tool is used to remove NA
values or missing values. RapidMiner supplies data mining and machine learning processes,
including data loading and transformation (ETLs), data preprocessing and visualization,
predictive analytics and statistical modelling, assessment, and implementation.

21. 2.5.4 Machine learning Model
According to this research study, after data cleaning and transformation data, a machine
learning model will be implemented to analyze waste amount data for predicting the next 10
years' trend. Three machine learning models would be used in the implementation process,
such as Linear regression, Support Vector Machine, and Artificial Neural Network. These
machine learning methods were used to train the forecasting model and evaluate the model
performance based on the literature review and other sources [37,38]. More details of these
models will be explained in the upcoming sections.
Based on the dataset, the researchers will analyze and visualize the forecasting trend. In other
words, identifying the future trend of the waste amount in UAE, from all past combinations of
the waste amount and the waste amount data.
For analyzing the association between features (attributes) and identifying relationships
between them, a correlation matrix could be used , and other features relation techniques. With
the help of association or correlation finding techniques, one can determine which variables or
features are most important to implement the models for best accuracy and accurate results
[35,36].
Implemented machine learning models using Multiple Linear Regression, SVM, and
Artificial Neural Network(ANN) for the collected data will use 70% for training data and
30% testing data for evaluating the model performance [38]. However, different
combinations of the percentage of training and testing the data could lead to better results.
Multiple linear regression is a statistical technique that uses various explanatory variables to
predict the outcome of a response variable. Multiple regression is a linear (OLS) regression
extension that uses only one explanatory variable. An SVM is a monitored learning machine
that uses classification algorithms for two-group classification problems. They can categorize

22. new text after giving an SVM model collection of labeled training data for each group. An
artificial neural network (ANN) is a computer device designed to replicate the analysis and
processing of information by the human brain. It is the cornerstone of artificial intelligence and
resolves issues that would be impossible or difficult by human or mathematical standards. [61]
2.6 Strategies for Data Analysis
This section intends to discuss the process of analyzing the quantitative data in order to address
the third and fourth research questions of examining if the shortlisted socioeconomic factors
have any correlation and compare their results to identify the waste indicators. Moreover, the
applicable waste indicators will predict the waste generation from 2021-2030, using three
different machine learning models. Those models would be created and analyzed using
RapidMiner software.
2.6.1 Socio-Economic Factor Analysis
To evaluate the relationship between the shortlisted factors, GDP, Population, water
consumption, annual tourists visited, number of buildings under construction. And how these
selected factors affect the quantity of waste generated, the researchers will test the stated
hypothesis using a scatter plot and Pearson's correlation analysis. Correlation factor analysis
would provide information on the strength and direction of the linear relationship between each
factor and the waste generated. A correlation coefficient or analysis is used to find relationships
between variables or features of the dataset. This research is needed to find out the impact of
independent variables on the dependent variable [35, 36,39].
To begin with the analysis, the plotting of the xy scatter plot for "Total waste amount" with
other variables must be done in order to visualize the trends and gain a general overview of the
dependence between them. Following this, the calculation of "Pearson correlation" between

23. each pair of variables will proceed in order to find which factor has the major influence on
waste generation. For example, using these methods (scatter plot and correlation) for shifted
variables, the researchers would check the correlation between the population size in 2018 and
the waste amount in 2018.
Finding dependence is getting some metrics in order to know that one's dependence "is better"
than another. To perform that, it is needed to compute the below defined Pearson correlation
coefficient for each socio-economic factor:
where,
- correlation coefficient
- values of the x-variable in a sample, i.e., annual waste generated
- mean of the values of the x-variable, i.e., mean weight of waste generated
- values of the y-variable in a sample, i.e., the annual amount of each factor
- mean of the values of the y-variable, i.e., mean value of the factor
All the above steps will be repeated for each factor with a shift over time. For this analysis,
based on the hypothesis H01-H05, the following pairs of variables will be computed for
correlation :
○ Waste generated vs. GDP
○ Waste generated vs. Tourist visitors
○ Waste generated vs. population
○ Waste generated vs. Consumed water
○ Waste generated vs. Buildings under construction
On computing the correlation factor for each pair, based on the basic principle [40], the tested
hypothesis will be rejected if the coefficient factors are below 0. However, the factors that have
the highest correlation value will be chosen as the waste indicators that influence waste
generation in Dubai.

24. 2.6.2 Waste Forecast Analysis
In the machine learning forecasting technique used for finding future trends based on past data.
According to the collected data requirements, we need to use these techniques for predicting
future waste amount trends.RapidMiner provides the default forecasting model such as 'Auto-
Regressive Integrated Moving Average' (ARIMA), Holt-Winters and windowing models [41].
But we can change the specific model for applying forecasting on collected data. Linear
Regression, SVM and Artificial Neural Network model the three models with the applied
forecast on the waste amount data. By default, the RapidMiner ARIMA model generates the
forecasts for the next ten values [42]. Therefore, the default model can be used. However, a
need to tune the forecast models should be applied while using different machine learning
models.
Note
2.6.2.1 Linear Regression Model
The linear Regression model is mostly used for modeling the relationship between dependent
and independent variables or features [43]. This research study predicts the waste amount and
finds the main factors or indicators that influence the quantity of waste. That's why this model
will help to predict the relationship between variables or factors. In the collected data, there are
many independent variables such as the Gross Domestic Product (GDP), the quantity of water
consumed, population, total buildings under construction, and the number of visitors, in
addition to one dependent variable, which is the variable of our interest which is the total
collected waste. The variable (factor) predicted for which the equation solves is called the
dependent variable. While the independent variables are the factors used to predict the value
of the dependent variable [44]. The following equation could present the simple linear
regression model:
y = α + βX [45]

25. The equation has two important factors, α, which is the y-intercept of the regression line, while
β is the slope and y is the dependent variable. The regression line could be positive, negative,
or no relationship. If the graphed line has no slope (just a flat line), there is no relationship
between variables. A positive relationship exists when the regression line slopes upward. In
contrast, a negative linear relationship exists if the regression line slopes downward [46]. From
knowing the general trend line between the multiple independent variables and the dependent
variable, we can identify the relationship's strength. Also, it helps us to understand the effect
of the dependent variable on other independent variables. Lastly, the regression analysis helps
in predicting trends and future values. All of these will be trained using RapidMiner.
2.6.2.2 Support Vector Machine Model
Support Vector Machine (SVM) is one of the popular machine learning algorithms and is
considered one of the most robust and accurate methods among the well-known data mining
algorithms [47].
The capacity of SVM to tackle nonlinear regression assessment issues makes SVM valuable
in time series forecasting [48]. It has become an intriguing issue of escalated concentrate
because of its valuable application in classification and regression models. While using
RapidMiner, we can use this machine learning model before applying the forecast. Support
Vector Machine provides us with a model tune mechanism if the model's output is not
according to the requirements. We can tune the hyper plan parameters while using RapidMiner
studio and apply them to the predicting forecasting model [49].
2.6.2.3 Artificial Neural Network Model
The Artificial Neural Network (ANN) is the most used network for forecasting applications
[50]. Also, this model can be applied for short datasets because ANN generates output by
acquiring knowledge from the patterns and relationships of data[51].

26. Artificial Neural Network is a deep learning modelling approach, and this model facilitates
classification (supervised learning), clustering (unsupervised learning), and regression
analysis. Artificial Neural Network based on the input, output, and hidden layers. The beauty
of this model is it provides the most relevant results compared to other models [52]. The
structure of a neural network algorithm has three layers, as shown in figure 3; the input layer,
which feeds the data values into the next layer (hidden layer), the hidden layer contains several
complex functions that create predictors, those mathematical functions are hidden from the
user. Their role is to modify the input data and make predictions; these functions are also called
neurons. Finally, the output layer has the role of collecting the hidden layer's predictions and
producing the result, which is the model's prediction. [53].
Figure 3 Example of Structure of an ANN model [54]
2.7 Limitations
The literature review benefits the researchers to understand the possible waste indicators for
Dubai, as there are no previous studies or findings conducted in the emirate. Given that the
research deals primarily with statistical data, data gaps are also a very common case in
statistics. Knowing that the region does not have extensive historical records, having gaps in
smaller amounts of data for model training is a limitation to be mindful of.

27. Population data published after 2005 are primarily a population estimate and were later
validated using the number of people with residential status in Dubai due to employment. Still,
it is understood that they are not necessarily residents of Dubai, which might have a negligible
impact.
The waste generation forecast would be based on the historical data of the waste indicators and
their projections. Hence the predictions of the models would be computed as the period of the
forecast over one year. For example, when predicting waste generation for the year 2025 using
population size from previous years, it is needed to estimate the population size in 2021, 2022,
2023, and 2024. Therefore it would be beneficial to perform comparative multi-model
forecasting than using a single model for better prediction accuracy.
2.8 Validity and Reliability
While the objective of this research inclines towards an improvement, to provide beneficial
research to decision-makers and waste generating and management stakeholders, and to
hopefully provide a source of study for future researchers. With a detailed research design, the
reliability and the validity of the research data and the consequent research is of utmost
importance as it aids in evaluating the quality of the study. Therefore, this section describes
how the researchers aim to measure the reliability and validity of their research design.
In a quantitative analysis, validity is characterized as the degree to which a definition is
measured accurately. In quantitative studies, the second factor of consistency is the instrument's
reliability or the precision of the measuring instrument. The word "reliability" has two
meanings: first, whether or not you can get the same response each time you repeat a

28. measurement, and second, how trustworthy a measurement result is. Most simply, study
reliability refers to the consistency and stability of research findings. [56]
Construct validation enables the researchers to understand how the research data and approach
are fairly representative of the entire research the researchers seek to measure. [57] This
validation focuses on following a methodical approach and developing recommendations by
identifying relevant factors and understanding their contribution to waste generation. After that,
develop a forecasting model to predict the waste generated in Dubai based on these identified
factors. Table 1 provides a comprehensive summary of how the research questions are mapped
and how their approaches and outcomes are valid.
Table 1 Research Questions Mapping
S.No Research Question Literature Review
Findings
Data
Collection
Tool
Intended Outcome
1. How can machine
learning be applied
in solid waste
management?
Various methods used to
evaluate and forecast
waste:
·Generation- ANN,
SVM, partial squares
with SVM
·Storage, Collection,
Transportation-
Multilayer neural
network, KNN, Genetic
algorithm (GA), GA
with GIS.
·Composting- ANN,
CNN.
·Incineration- MLP-
ANN, SVM, Ann
models.
·Landfill- ANN, MLP-
ANN, and SVM.
Literature
Review
·To identify the
common and
appropriate method for
evaluating
socioeconomic factors
and their influence on
waste generation.
·To identify the
appropriate ML
approach to forecast
waste generation and
compare their
performance.

29. 2. What are the
possible factors
that influence a
waste generation?
· GDP
· Urban Population
· Housing units
· Number of Tourists
· Geographical location
· Energy consumption
· quantity of water
consumed.
· Availability or non-
availability of
collector bins
· Policies
Literature
Review
·To understand the
waste indicators in
other cities/countries
and check whether
they are consistent
and accessible for
Dubai.
·To identify possible
factors to study and
shortlist
corresponding to
Dubai, UAE.
3. Is there a
significant
relationship
between
population growth,
GDP, and waste
generation in
Dubai?
GDP and population were
significant influences, and
other factors have
negligible influence in
UAE.
Statistical
Data
To confirm the
correlation for UAE's
statistics, using
regression analysis.
4. Why does
Dubai/UAE need
to expedite the
implementation of
effective and
sustainable waste
management
techniques?
·The most common
disposal method is
dumpsite and landfills,
which account for over
55%, but is the least
preferred method
according to the
sustainable waste
hierarchy.
·The performance of the
12th Sustainable
Development Goal
(SDG), Responsible
Consumption And
Protection, is the third-
lowest.
· Improvement on 12th
SDG could create a
significant improvement
in the overall score and
ranking.
Statistical
Data
To examine the future
waste generation
predictions for the years
2021-2030 to emphasize
improving waste
management in Dubai
based on the current
trends.
To ensure the validity of the above-discussed data, it is essential to test the reliability of the
data as well. To do so, one of the most commonly used assessment tools is to calculate the

30. internal correlation coefficient, Cronbach's Alpha for each variable's dataset, including the
waste generated. The below formula [40] shows how to compute this coefficient, α, for the
datasets:
Here N is equal to the number of items, in this case, the number of years, 𝑐, is the average
inter-item covariance among the items for a variable's dataset and 𝜐 Equals their average
variance. The Cronbach alpha coefficient would be computed for each variable, and to consider
them as an acceptable, reliable dataset, the coefficient value must be 0.70 or higher.
These measurement techniques improve the quality of the research and provide sound
quantitative research for the beneficiaries by giving consistent and accurate research.
2.9 Ethics
The data collection process is one of the key phases of any research. Therefore it is also
suggested to have a good practice of prioritizing ethical principles throughout the research
itself. Being sensitive and having credible research can drive the research fundamentally as
well.
A few major ethical considerations that must not be overlooked are professional integrity and
accountability, the integrity of the data and methods, and the responsibilities of research
colleagues and instructors. Ethical considerations are one of the most critical aspects of the
study. Experiment participants should never be placed in any kind of danger. Respect for
research participants' integrity should be considered a significant value. [28] We must first

31. obtain full consent from the participants before starting the study. Given that this research
comprises a mixed approach, there are situations where the research is approached in
exploratory and explanatory studies. Hence, the researchers need to conduct an impartial and
transparent assessment of the findings and acknowledge all sources of findings and data used
to fulfill this research. Moreover, the researchers are obliged to ensure that their research
practices comply with the intention of the statistical data sources and do not have any conflict.
It is also essential for the researchers to acknowledge the data editing procedures, including
any imputation and missing data mechanisms, thus striving to promote transparency in the
design, execution, reporting, and presenting of all analyses.
Above all, with professionalism during the research phases, the researchers vouch to act in
good faith and manner and strive towards successful research. Therefore, although my study
will not be collating data directly from subjects such as humans or animals, the research data
ethics will mainly focus on data integrity.
3. Conclusion
This report focused on identifying all aspects of the research methodology, including research
philosophy, research design, research approach, and strategy. It also discussed the analysis
methods used in the research depending on the research questions to analyze the available
statistical data of UAE.
The most available and accessible data was the population characteristics, GDP, water
consumption, number of houses being built, and the total number of tourists who visited the
emirate and waste generated statistics of Dubai from Dubai Statistic Center (SDC). Therefore,
this research would primarily focus on analyzing the influence of the shortlisted waste

32. indicators over the years 2000 to 2020 to forecast the waste generation, if found to be
correlated, using various appropriate algorithms in ML. Furthermore, from the literature, it was
found that the commonly adopted machine learning model used in the field of solid waste
treatment is the Artificial Neural Networks (ANNs). Therefore, this research will be
implementing machine learning approaches such as linear regression technique, SVM, and
ANN to approach the expected results. Also, these results and findings will help support and
facilitate future research related to waste management in the UAE.

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