| Sažetak | Kontinuiranim povećanjem broja stanovnika dolazi i do povećanja proizvodnje otpada. Isto tako,
uz gospodarski rast i, povezano s time, poboljšavanja životnog standarda, također, dolazi do
povećanja proizvodnje otpada. Stoga, ne čudi da sve veće količine otpada koje svake godine
nastaju uzrokuju opravdanu zabrinutost zbog ekonomske održivosti i ekološke prihvatljivosti
trenutnog načina gospodarenja otpadom. Glavni problem s kojim se suočava stručna i
znanstvena javnost je kako predvidjeti količinu otpada koja će nastati u bliskoj budućnosti.
Planiranje optimalne regionalne ili nacionalne strategije gospodarenja otpadom usko je povezano
s količinom otpada koja će nastati. Za rješavanje navedenih problema pokazuje se potreba za
kreiranjem pouzdanog modela za predviđanje količine nastalog otpada. Na temelju dosadašnjih
istraživanja, umjetne neuronske mreže pokazuju bolje rezultate kod predviđanja nastanka otpada
u usporedbi s drugim matematičkim modelima, stoga u ovom istraživanju koristit će se upravo
umjetne neuronske mreže kao alat za razvoj matematičkog modela za predviđanje količina
nastalog biorazgradivoga komunalnog otpada na europskoj i nacionalnoj razini. U ovom
istraživanju poseban naglasak stavljen je na razvoj modela za predviđanje nastanka
biorazgradivoga komunalnog otpada. Proučavanje biorazgradivoga komunalnog otpada od
posebnog je interesa jer se upravo kod ove vrste otpada vidi veliki potencijal za njegovo relativno
jednostavno i jeftino iskorištavanje, i to u vidu sirovine za dobivanje komposta pogodnog za daljnje
korištenje u poljoprivredi ili u vidu ulazne sirovine u bioplinskim postrojenjima.
Za kreiranje umjetne neuronske mreže u ovom doktorskom radu ulazne podatke činio je set
sociodemografskih, ekonomskih i industrijskih podataka 17 država članica Europske unije za
razdoblje od 25 godina. Kreiranim modelom u ovom doktorskom radu željele su se predvidjeti
količine promatranih vrsta otpada koje će nastati na području 17 država Europske unije u
razdoblju od 2020. do 2025. godine. Uz samo kreiranje mreže za predviđanje količina komponenti
biorazgradivog otpada, cilj je istražiti i utjecaj sociodemografskih i ekonomskih pokazatelja na
količine biorazgradivoga komunalnog otpada. Prema razvijenom modelu od 2020. do 2025.
godine očekuje se da će u 17 država Europske unije nastati 411.351.769 tona miješanoga
komunalnog otpada (u sklopu kojeg će nastati i 81.776.732 tona biootpada), 90.280.031 tona
papira i kartona, 35.926.182 tona otpadnog drva i 3.511.589 tona tekstilnog otpada. Rezultati
ovog istraživanja pokazuju kako na sve četiri promatrane vrste komunalnog otpada pozitivno
utječu parametri kao što su broj stanovnika, bruto domaći proizvod po tržišnim cijenama, srednji
ekvivalent neto prihoda, turizam, izvoz nafte i naftnih derivata i neto vanjski dug. S druge strane,
životni vijek, realni BDP po stanovniku, ukupne obveze financijskog sektora i uvoz roba i usluga
negativno utječu na sve četiri vrste otpada. Zaključno se može reći da iako je Europska unija
heterogena zajednica i bez obzira na poteškoće u pronalasku što ažurnijih podataka o otpadu,
kreiran model pokazao je zadovoljavajuća svojstva i mogućnosti u predviđanju količina
miješanoga komunalnog otpada, otpadnog papira, drva i tekstila. Rezultati istraživanja mogu
poslužiti kao pomoć pri uspostavi ekonomičnijeg i ekološki prihvatljivijeg načina gospodarenja
biorazgradivim otpadom. |
| Sažetak (engleski) | The increasing amounts of waste generated each year raise legitimate concerns about the
economic viability and environmental sustainability of the current way of waste management. The
main problem facing professionals and academics is predicting the amounts of waste that will be
generated in the near future. Planning an optimal regional or national waste management strategy
is closely linked to the amount of waste that will be generated. To solve these problems, a reliable
model for predicting the amount of waste needs to be developed. Such a tool should make it
possible to select the most appropriate waste management technique. Based on previous
research, artificial neural networks show better results in predicting waste generation compared
to other mathematical models. Therefore, in this research, artificial neural networks are used as
a tool to develop models for predicting the amount of biodegradable municipal waste at European
and national level.
This doctoral thesis is divided into 5 basic thematic units. It begins with an introduction in which
the reader is briefly introduced to the main topics such as waste and artificial neural networks. It
also defines the research area and the main objectives and hypotheses. For example, the
introduction clearly explains that there are two main objectives of the research. The first objective
is to develop a mathematical model for predicting the amounts quantities of components of
biodegradable waste using artificial neural networks with the aim of applying it at European and
national level. The second objective is to predict the impact of socio-demographic, economic and
industrial indicators on the amount of biodegradable waste using artificial neural networks.
The introduction is followed by a review of previous literature. In this chapter, the concepts of
municipal waste, biodegradable waste and artificial neural networks are explained in more detail.
Research conducted by other authors to date is also presented and explained in detail. They refer
to the parameters that influence waste generation, as well as research that has dealt with the
creation of mathematical models and their success in predicting waste generation. An additional
significance of this research lies in the scale of the research. To date, artificial neural networks
have been used to make predictions on a smaller local or regional scale. This usually covers the
area of a particular city, state or group of closely related states. The aim of this research is to
investigate the accuracy of a model with large-scale data (17 countries of the European Union).
In the Materials and Methods chapter, the methodological approach and the way of creating a
mathematical model are explained. To develop the model, demographic data (population, life
expectancy, educational attainment), economic progress data (gross domestic product at market
prices, gross domestic product per capita, total financial sector liabilities, net external debt,
nominal effective exchange rate, direct investment in the reporting economy, house price index,
data on the number of (non-)employed persons (total number of employed persons,
unemployment rate, youth unemployment rate), tourism data (arrivals in tourist accommodation
facilities, number of nights spent in tourist accommodation facilities), trade data (imports of goods
and services, exports of goods and services, exports of oil and petroleum products) and waste
data (annual municipal waste generation in thousands of tonnes, municipal waste generation per
capita, municipal waste recycling rate, waste disposal) were collected. All data were collected for
a period of 25 years for 17 countries of the European Union: Belgium, the Czech Republic,
Denmark, Estonia, France, Croatia, Ireland, Italy, Latvia, Lithuania, Luxembourg, Hungary, Malta,
the Netherlands, Slovenia, Spain and Sweden.
In this research, the Multi-Layer Perceptron (MLP) model was used, which consists of a total of
three layers: input, hidden layer and output. Before starting to compute the model, the database
of collected data was divided into data for learning (60% of the data), for verification (20%) and
for testing the neural network (20 %). Numerical verification of the obtained artificial neural
network model was tested using the coefficient of determination (r2), reduced chi-squared (χ2),
mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), sum of
squares error (SSE) and average absolute relative deviation (AARD). The constructed neural
network model showed promising generalisation properties for the collected database and could
be used to accurately predict waste generation: 20 networks, the maximum values of r2 (during
the training cycle, r2 for the output variables (mixed municipal waste, municipal waste paper and
cardboard, wood and textiles) were: 0.999, 0.998, 0.997 and 0.998).
The results obtained show that artificial neural networks are indeed a reliable tool to create a
mathematical model to predict the amount of biodegradable municipal waste at European and
national level. The actual accuracy of the results of this research in terms of waste generation in
the 17 countries observed will be verified when the data on waste generation for the year 2020
becomes available.
In addition to creating a network to predict the quantities of biodegradable waste components, the
influence of socio-demographic, economic and industrial indicators on the quantities of municipal
biodegradable waste generated was also observed. Of the 28 input data, 10 input factors have a
positive influence on all 4 observed waste types, while 4 input factors have a negative influence
on all 4 observed waste types. Other observed factors (such as foreign direct investment, annual
unemployment rate data, exports of goods and services and education) did not yield results from
which a single conclusion could be drawn. From the above, it is clear that the accuracy of
predicting the amount of biodegradable waste using artificial neural networks really depends on
the choice of socio-demographic, economic and industrial indicators.
For further studies to be carried out, it is proposed to use parameters such as population, gross
domestic product at market prices, mean net income equivalent, tourism, exports of oil and
petroleum products and net foreign debt, as these parameters positively influence all four types
of municipal waste observed. On the other hand, life expectancy, real GDP per capita, total
financial sector liabilities and imports of goods and services have a negative impact on all four
types of waste. The results of this study are in line with the studies conducted so far, according
to which the generation of municipal waste is mainly influenced by GDP, tourism, population and
wages. Depending on changes in these factors, the amount of waste generated also changes.
The model created can help the waste management system behave like a "living organism". In
this flexible way, the waste management system could change in parallel with social and
economic changes. This would make municipal waste management more efficient and
economical, with less impact on the environment. |
