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Forecasting Generation Waste Using Artificial Neural Networks
Elmira Shamshiry1, Behzad Nadi*2, Mazlin Bin Mokhtar1, Ibrahim Komoo2, Halimaton Saadiah Hashim1, Nadzri
YAhya3
(1)Institute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia (UKM)
(2) Southeast Asia Disaster Prevention Research Institute (SEADPRI) University Kebangsaan Malaysia (UKM)
(3) Director General, National Solid Waste Management Department, Ministry Of Housing and Local Government, Malaysia
* [email protected]
Abstract. Municipal solid waste (MSW) is the natural result of human activities. MSW generation
modeling is major significant in municipal solid waste management system planning. Predicting the
amount of generated waste is difficult task because it is affect by various parameters. In this research,
Artificial Neural Network (ANN) was trained and tested to weekly waste generation (WWG) model in
Sari’s city of Iran. Input data is consisting WWG observation and the number of trucks, personnel and
fuel cost were obtained from Sari Recycling and Material Conversion Organization. The gathering
data related to monitoring 2006 to2008.
Key Words: Weekly Waste Generation, Prediction, Artificial Neural Network, Sensitive Analysis.
1
Introduction
One of the after-effects of human activities is the Solid Waste (SW). A suitable management system should be
developed so that the environmental pollution won't endanger people's health. It's an uphill struggle to implement
such a system because of the complicated and wide-ranging nature of the waste. Working out the amount of waste
produced is of vital importance to set up the Solid Waste Management System. (SWMS) Being aware of the
quantity produced can work wonders due to estimating the amount of investigation in the field of machinery, onsite
storage containers, transition stations, disposal capacity and proper organization. There are many different ways to
assess the waste generation (WG) rates; the most instrumental of them are load-count analysis, weight-volume
analysis and materials-balance analysis. Nevertheless, these are the central strategies for estimating the figures of
generated waste, but there are some disadvantages. For instance, the load-count analysis method pinpoints the rate
collection, but not explaining the rate of production. The materials balance analysis method is also in the throes of
many errors if the source of WG be a massive size (such as a city). On the other hand, traditional methods for
calculating the amount of produced solid waste are established, mostly, on the basis of some elements such as
population and social-economic parameters of a society and they are computed according to the generation
coefficient per person.
These methods mostly comprise some models, classic statistical methods and many new techniques like time
series methods and artificial neural networks. In this study, an Artificial Neural Network (ANN) was trained and
tested to weekly waste generation model (WWG) for Sari city which is the capital of Mazandaran province in Iran.
The ANN models are basically based on the perceived work of the human brain. The artificial model of the brain
is known as ANN (Sahin, et al., 2005). For this reason, ANNs have been usefully applied to a wide variety of
problems that are convoluted, define, and gauge; for example, in finance, medicine, engineering, etc. Recently,
ANNs have been used in the management of MSW such as a proposed model based on ANN to foresee the rate of
leachate flow rate in places of solid waste disposal in Istanbul, Turkey (Karaca & Ozkaya, 2006), the prediction of
energy content of a Taiwan MSW using multilayer perception neural networks (Shu et al., 2006), HCl emission
characteristics and back propagation prediction by neural networks in MSW/coal co-fired fluidized beds (Chi et al.,
2005), recycling strategy and a recyclability assessment model based on an ANN (Liu et al., 2002) and the
prediction of heat production from urban solid waste by ANN and multivariable linear regression in the city of
Nanjing, China (Dong, et al., 2003), have been evident in current practice.
Also in the other environmental problems like air pollution (Sahin, et al., 2005 ; Lu, et al., 2004 ; Lu, et al., 2006),
surface water pollution (Sahoo, et al., 2006 ; Shrestha & Kazama, 2007), the ANNs have been used. The result of all
this research has presented the high performance of ANN in prediction of various environmental factors.
2
Data and Materials
Estimating and forecasting the quantity of producing solid waste depends on the different factors such as
geographical situation, seasons, collection frequency, onsite processes, and food habits of the people, economic
conditions, recovery and reuse boundaries, existing laws and the cultural conditions of the people. And these factors
aren’t accurate measurement, and it cannot be used as a standard precise analysis.
Figure 1. Weekly fluctuation of waste generation in Sari (2006-2008)
The most method to achieve the prediction exact amount of production of solid waste is mostly emphasis on the
parameters such as; weekly produced average solid waste, weekly fuel use for collection solid waste, weekly truck
use for collection solid waste and number of personal collect solid waste in the Average weekly,
3
Methodology
In this research, Artificial Neural Network (ANN) was trained and tested to weekly waste generation (WWG) model
in Sari’s city of Iran. Input data; consist of WWG observation and the number of trucks which carry waste, number
of personnel and fuel cost were obtained from Sari Recycling and Material Conversion Organization. The
monitoring data from 2006 to2008 are designed to provide the requirements of training and testing the neural
network.
In this model, the weight of waste in t+1 week (Wt+1), is a function of waste quantity in t (Wt), t-1 (Wt-1)… t-11
(Wt-11) weeks. Another input data, consist of the number of truck, which carry waste in the week of t (Trt), number
of personal (Pr), cost and fuel (Fu). In this research used tangent Hyperbolic function for output layers and to input
layers used function Y=X.
In this study, a neural network is trained and tested through the use of software Neural Network version 2.5 and
Matlab version 7.8. In this method, the data are divided into 3 parts. The first part of the data is related to network
training, the second part is used for stopping calculations when the integrity error starts to increase and the third part
is used for the network integrity.
To examine the performance of the ANN model, four statistical indexes are used: the Mean Absolute Error
(MAE), the Mean Absolute Relative Error (MARE), the Root Mean Square Error (RMSE) and correlation
coefficient (R2) values that are derived in statistical calculations of observation in the model output predictions.
4
Results & Discussion
The statistical analysis of waste materials in Sari during the different seasons between “2006-2008”, is presented in
Table ‎0-1. Because the average and median amount is so close to each other, the waste generation in Sari shows a
normal distribution through ought the different seasons. The large size of Standard deviation reveals a large
generation fluctuation in different seasons of the year during the period of the study.
This great impact could be explained by hobbies and entertainments people have at certain periods and the
economic conditions. Different structures of feed forward ANN with three layers and a different number of neurons
in the hidden layer were investigated to achieve the best ANN structure for estimating generated waste. Estimating
and forecasting the quantity of producing solid waste depends on to the different factors such as cultural, social,
living, etc. And these factors aren’t accurate measurement and it cannot be used as a standard precise analysis.
At last with respect to MAE, MARE, RMSE and R2 suitable models were selected for the study. Network
structure includes the 15-input, 1 hidden layer, and 1-output. In order to estimate the number of neuron in hidden
layer for the best prediction, hidden layers from 1 to 10 Neurons were experimented. Evaluation results of ANN
with different numbers of neurons in hidden layer are as following Table ‎2.
Table 2 . Calculated Errors for ANNs with different neurons in hidden Layers applied in training and testing data sets
Training set
NO
15-10-1
15-9-1
15-8-1
15-7-1
15-6-1
15-5-1
15-4-1
15-3-1
15-2-1
15-1-1
Testing Set
MAE
MARE%
RSME
R2
MAE
MARE%
RSME
R2
90.58
48.69
35.63
40.56
38.56
40.91
26.75
34.24
37.04
33.74
0.049
0.026
0.019
0.021
0.021
0.021
0.014
0.018
0.020
0.018
114.4
69.2
54.9
55.8
56.1
51.8
44.3
46.8
55.2
45.7
0.954
0.982
0.989
0.988
0.988
0.990
0.993
0.992
0.989
0.992
121.0
72.9
55.2
46.9
45.9
57.7
48.9
46.6
38.5
60.3
0.19
0.11
0.09
0.07
0.07
0.09
0.07
0.07
0.06
0.10
159.3
106.5
84.4
66.7
65.6
77.5
68.1
71.5
51.7
81.4
0.905
0.962
0.975
0.984
0.985
0.980
0.983
0.980
0.989
0.975
Summation of errors for each network presented that after increasing number of the neurons in hidden layers, the
error increased. However, all tested ANN structures have presented high accuracy. According to the results ANN
structure with 2 neurons in hidden layers was selected as an optimal one which can minimize the calculation.
However, ANN with 3 to 6 neurons in hidden layers can be employed as the optimal system.
In the first step, a neural network with one neuron in hidden layer was applied. Training and testing data sets are
applied to the ANN. The correlation coefficient for training and testing is 0.992 and 0.975, respectively. Presented
results indicate high accuracy in prediction.
Figure 2. Observed amount of solid waste and predicted output of ANN Model with 1 neuron in hidden layer for testing data
set.
Figure 3. Scatter plot of predicted output of ANN Model with 1 neuron in hidden layer for testing data set versus observed
amount of solid waste.
In the second step, a neural network with two neurons in hidden layer was applied. Training and testing data sets
are applied to the ANN. The correlation coefficient (R2) of testing set increases from 0.992 to 0.989 and the
correlation coefficient (R2) of training set increases from 0.975 to 0.989.
Figure 4. Observed amount of solid waste and predicted output of ANN Model with 2 neurons in hidden layer for testing data
set.
Figure 5. Scatter plot of predicted output of ANN Model with 2 neurons in hidden layer for testing data set versus observed
amount of solid waste.
Figure 6. Observed amount of solid waste and predicted output of ANN Model with 2 neurons in hidden layer for training data
set.
Figure 7. Scatter plot of predicted output of ANN Model with 2 neurons in hidden layer for training data set versus observed
amount of solid waste.
5
Conclusion
Accurate prediction of solid waste generated is of vital importance in the municipal solid waste management. Thus,
the goal of this study was to provide an appropriate model to predict this quantity. The most unique part of this
model is that for the first time prediction of solid waste has been done by the analysis of artificial neural network
and combining the amount of generated waste, fuel consumption, total of labor and quantity and quality of transport
as input data. According to the results ANN structure with 2 neurons in hidden layers was selected as an optimal one
which can minimize the calculation. The methodology or an adapted form of the methodology might also be applied
to other fields, subject to a study of the requirements in each place. Therefore the goal of this research was offering a
suitable model to predict this quantity and link to the geospatial environment.
At last with respect to MAE, MARE, RMSE and R2 suitable models were selected for the study after the
mentioned model performing, correlation coefficient (R2) and mean absolute relative error (MARE) in neural
network for test have been achieved equal to 0.989 and 0.06 respectively.
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