# Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

^{2}value (0.998) confirmed the accuracy of the model. The analysed waste was categorised into recyclable, general waste and food residue. The authors estimated the total waste generated during the lean season at 2010.5 kg/day, in which large hotels accounted for the largest fraction (66.7%), followed by medium-sized hotels (19.4%) and guesthouses (2.6%). During the peak season, about 49.6% increases in waste generation rates were obtained. Interestingly, 45% of the waste was generated by British tourists, while the least waste was generated by African tourists (7.5%). The ANN predicted that small and large hotels would produce 5.45 and 22.24 tons of waste by the year 2020, respectively. The findings herein are promising and useful in establishing a sustainable waste management system.

## 1. Introduction

## 2. Research Methodology

#### 2.1. Research Area and Dataset

#### 2.2. Model Development and Description of the Input Parameters

#### 2.3. Multiple Linear Regression Analysis

_{1}, …, x

_{n}represent the five independent variables in this study, and β

_{0}, …, β

_{n}denote the impact of each independent variable on the response variable.

#### 2.4. Principle of Central Composite Design

_{r}is the repeated runs.

^{2}), predicted R

^{2}and adjusted R

^{2}. The statistical significance of the model was verified with Fisher variation ratio (F-value), the probability value (Prob > F) with 95% confidence level and adequate precision.

#### 2.5. Principle of Artificial Neural Network Model

_{x}(x = 1, 2, 3, 4, …) is the output variable; w

_{j}and W

_{ji}(j = 1, 2, 3, …, n; i = 0, 1, 2, 3, …, m) are connection weights; m and n represent the number of input and hidden nodes, respectively. The f corresponds to the sigmoidal activation function; b

_{x,i}and B

_{0j}represent the bias terms associated with each input, output and hidden layer nodes, respectively.

#### 2.6. Model Performance Evaluation

^{2}) values were derived using the following equations:

_{o}is the observed values of rate of waste generation for type t, p is the number of independent parameters, w

_{o}’ is the average of HSW generation and w

_{p}is the predicted value of HSW generation for type t. R

^{2}measures the closeness of the observed data to the predicted data, MAE is a statistical quantity that measures how close predictions are to the eventual outcomes, and SEP is a measure of the accuracy of the predictions. The smaller the value of the error indices for a specified model, the higher the prediction performance of the model [20,21].

## 3. Results and Discussion

#### 3.1. Results of MLR

#### 3.2. Analysis of AAN Model Results

_{max}and X

_{min}represent their actual, maximum and minimum values, respectively.

_{i}) on waste generation rate (kg/day) as given in Equation (10):

_{i}is the relative importance of the ith input variable on the response; W, N

_{i}and N

_{h}represent the connection weights, numbers of input and hidden neurons, respectively. The subscripts ‘k’, ‘m’ and ‘n’ is the input, hidden and output neuron, while the superscripts ‘i’, ‘h’ and ‘o’ represent the input, hidden and output layers, respectively.

#### 3.2.1. Selection of Backpropagation (BP) Training Algorithm

^{2}) and the least mean square error (MSE) were used as the yardstick to select the best BP. Of all the BP algorithms examined, the Levenberg–Marquardt (LMA) BP algorithm specifically resulted in the least mean square error (0.0014) and its R

^{2}value (0.989) is closest to unity. Hence, LMA was selected as the training algorithm in this research.

#### 3.1.2. Optimisation of Neuron Number

#### 3.3. Analysis of CCD

^{2}value to unity, the better the model is at forecasting the response [19]. Table 7 indicates that the quadratic model was not aliased and has a comparatively low standard deviation of 3.361 and relatively high R

^{2}value of 0.9985, which is in reasonable agreement with the predicted R

^{2}(0.9966). Also, the PRESS of the quadratic equation is low (169.23), which revealed the reliability and better precision of the experimental results. Hence, the results indicate that the quadratic model can be used to describe the relationship between the response (WGR) and the interacting variables. Hence, the codified quadratic equation after eliminating the insignificant terms is shown in Equation (11):

^{2}, B

^{2}and E

^{2}, while C

^{2}, BC and AE are insignificant. The model term having the most significant effect on the response is B with an F-value of 679.87. The “Lack of Fit” F-value of 1.76 signifies that it is not significant relative to the pure error and there is a 65.88% chance that its F-value being this large could be due to noise [22]. The non-significant “Lack of fit” for WGR indicated the good predictability of the model.

#### 3.4. Estimation of Waste Generated and Comparison of Predictive Performance of Models

^{2}, MAE and SEP (Equations (4)–(7); Table 8).

^{2}compared to the CCD and MLR models. Based on the obtained results, the ANN architecture is more reliable and accurate in terms of predictive capability and fitting to the non-linear relationship between the variables and HSW generation rate. On the other hand, one of the most significant advantages of the CCD-based model is its ability to clarify the interactive effect of the variables on the response (WGR), which highlights its usefulness in predicting the rate of HSW generation. Hence, combining the abilities of CCD and ANN models in a hybrid fashion could result in powerful modelling and predictive models.

#### 3.5. Sensitivity Analysis and Relative Importance of Input Variables

## 4. Conclusions

## Author Contributions

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Optimised ANN structure of 5-11-6-1 selected for forecasting WGR in accommodation sectors.

**Figure 2.**Type and amount of waste generated per day in each season (

**a**–

**c**), and actual and predicted average daily waste generated per (

**d**) small hotel, (

**e**) medium hotel, and (

**f**) large hotel.

**Figure 3.**Waste generation rate (%)/day based on tourist nationality (

**a**–

**c**) and based on the type of waste management practice in each facility (

**d**–

**f**) during the peak season.

**Figure 4.**Response surface plots for interactive influence of independent parameters on the WGR. Waste generation rate (%) as a function of (

**a**) season and management practice (

**b**) management practice and tourist nationality (

**c**) season and nationality (

**d**) accommodation type and nationality

Nationality | Season | Accomm. Type | Waste Type | Waste Manage. Practice |
---|---|---|---|---|

British (1) | Lean (0.5) | Guesthouse (0.5) | Food waste (1) | None (2.5) |

Russian (2) | Mid (1.5) | Small hotel (2.5) | Glass (2) | Recycle (5.5) |

Asian (3) | Peak (2.5) | Medium hotel (4.5) | Cooking oil (3) | Reuse (9.0) |

Scandinavian (4) | Large hotel (8.5) | Garden waste (4) | Landfill (12.5) | |

German (5) | Aluminium (5) | Incineration (16.5) | ||

French (6) | Organic waste (6) | |||

Turkish (7) | Wood (7) | |||

Arabs (8) | Paper (8) | |||

African (9) | Plastic (9) |

Independent Variables | Symbol | Level of Factors | |||||||
---|---|---|---|---|---|---|---|---|---|

−α (−2) | −1 | 0 | 1 | α (2) | |||||

Nationality | A | 1.0 | 4.0 | 7.0 | 11.0 | 14.0 | |||

Accommodation type | B | 0.5 | 2.5 | 4.5 | 6.5 | 8.5 | |||

Season | C | 0.5 | 1 | 1.5 | 2 | 2.5 | |||

Type of waste | D | 1.0 | 4.0 | 7.0 | 11.0 | 14.0 | |||

Waste manag. practice | E | 1.5 | 5.5 | 9.0 | 12.5 | 16.5 | |||

Run order | A | B | C | D | E | Waste generation rate (%) | |||

CCD | ANN | MLR | Actual | ||||||

1 | 0 | 0 | 0 | 0 | 0 | 73.41 | 68.32 | 89.23 | 61.34 |

2 | 0 | 0 | −1 | 0 | 0 | 69.23 | 89.32 | 78.01 | 92.56 |

3 | 0 | 2 | 0 | 0 | 0 | 67.67 | 52.11 | 50.98 | 56.41 |

4 * | 1 | 0 | 0 | 0 | 0 | 49.89 | 47.55 | 46.89 | 48.55 |

5 | −2 | 0 | 0 | 0 | 0 | 75.01 | 94.99 | 89.23 | 96.19 |

6 | 2 | 0 | 0 | 0 | 0 | 83.41 | 90.11 | 90.89 | 89.33 |

7 | 0 | 2 | −2 | 0 | 0 | 71.13 | 82.88 | 72.88 | 92.01 |

8 * | 0 | −2 | 2 | 0 | 0 | 96.63 | 95.66 | 93.66 | 94.23 |

9 | 0 | 0 | 2 | −2 | 0 | 76.29 | 75.99 | 75.23 | 75.11 |

10 | −2 | 0 | 0 | 2 | 0 | 78.46 | 89.23 | 64.99 | 88.21 |

11 * | 0 | 2 | 0 | 0 | −2 | 78.22 | 77.01 | 75.55 | 77.46 |

12 | 2 | 2 | 2 | 0 | 2 | 74.21 | 87.98 | 79.98 | 87.21 |

13 | 1 | −1 | 1 | 1 | 1 | 96.99 | 76.89 | 86.89 | 97.99 |

14 * | −1 | 1 | 1 | 1 | 1 | 90.04 | 88.11 | 91.11 | 90.67 |

15 | 1 | 1 | −1 | 1 | 1 | 75.66 | 88.66 | 80.23 | 87.12 |

16 | −1 | 1 | −1 | 1 | 1 | 75.23 | 83.41 | 96.19 | 81.23 |

17 | 1 | 0 | 1 | −1 | 1 | 86.44 | 89.23 | 59.99 | 90.11 |

18 | −1 | 2 | 1 | −1 | 1 | 78.01 | 87.67 | 92.01 | 88.11 |

19 | −1 | −1 | −1 | −1 | 1 | 95.66 | 79.89 | 94.23 | 80.05 |

20 * | −1 | 1 | −1 | −1 | 1 | 54.99 | 55.81 | 54.11 | 56.99 |

*** Bold**indicates the run orders with desirability function greater than 0.9 and standard deviation less than 0.3.

Predicted WGR | Parameter | Parameter Coefficient | T-Value | α-Level | Standard Error |
---|---|---|---|---|---|

Total | Intercept | −89.56 | −1.41 | 0.32 | 63.7 |

A | 3.578 | 1.89 | 0.56 | 1.89 | |

B | 13.11 | 0.95 | 0.04 | 13.8 | |

C | −4.987 | −1.25 | 0.08 | −3.98 | |

D | −11.92 | 3.41 | 0.16 | 3.5 | |

E | 7.781 | −0.88 | 0.02 | −8.89 | |

Peak season | Intercept | −29.56 | −0.84 | 0.18 | 34.8 |

A | 2.254 | 1.63 | 0.96 | 1.38 | |

B | 6.89 | 1.16 | 0.00 | 5.96 | |

C | 1.855 | 0.77 | 0.07 | 2.39 | |

D | −6.365 | −0.31 | 0.28 | 20.3 | |

E | 4.332 | −0.38 | 0.05 | −11.3 | |

Lean season | Intercept | −59.89 | 5.04 | 0.68 | −11.89 |

A | 1.324 | 1.14 | 0.16 | 1.16 | |

B | 6.21 | 1.71 | 0.03 | 3.63 | |

C | −3.134 | −0.34 | 0.07 | 9.32 | |

D | −5.555 | 1.14 | 0.32 | −4.89 | |

E | 3.449 | 0.055 | 0.00 | 62.8 |

Backpropagation (BP) Algorithm | MSE | Epoch | R^{2} | Best Linear Equation |
---|---|---|---|---|

Fletcher–Reeves conjugate gradient BP | 0.0323 | 0.867 | y = 0.0671x + 0.0678 | |

Batch gradient descent | 0.0098 | 1000 | 0.918 | y = 0.6612x + 0.0893 |

Scaled conjugate gradient BP | 0.0167 | 78 | 0.456 | y = 0.7905x + 0.0698 |

One step secant backpropagation | 0.0088 | 32 | 0.789 | y = 0.0622x + 0.0256 |

Powell–Beale conjugate gradient BP | 0.0433 | 56 | 0.908 | y = 0.5623x + 0.0998 |

* Levenberg-Marquardt backpropagation | 0.0014 | 11 | 0.989 | y = 0.9017x + 0.0219 |

BFGS quasi-Newton backpropagation | 0.0093 | 30 | 0.965 | y = 0.7221x + 0.0083 |

Variable learning rate backpropagation | 0.007 | 178 | 0.671 | y = 0.9323x + 0.0044 |

Polak–Ribi’ere conjugate gradient BP | 0.0091 | 32 | 0.379 | y = 0.9011x + 0.0391 |

Batch gradient descent with momentum | 0.0205 | 1000 | 0.881 | y = 0.8312x + 0.6733 |

**Bold**indicates the best and selected BP.

Parameter | Value |
---|---|

Input layer neurons | 5 |

Output layer neurons | 1 |

Hidden layers | 2 |

Hidden layer neurons | 17 |

Training method | Levenberg–Marquardt backpropagation |

Error goal | 0.015% |

Epochs | 1000 |

Data division | Random |

Momentum (mu) | 0.001 |

Transfer function of hidden layer | Logsig |

Learning rate | 0.2 |

Number of Neurons | Training Set | Testing Set | ||||
---|---|---|---|---|---|---|

MAE | MSE | R^{2} | MAE | MSE | R^{2} | |

1 | 0.1278 | 0.3219 | 0.7865 | 0.0899 | 0.7012 | 0.6821 |

2 | 0.1806 | 0.3131 | 0.6679 | 0.0986 | 0.6131 | 0.6178 |

3 | 0.1311 | 0.3094 | 0.9012 | 0.0911 | 0.5694 | 0.8611 |

4 | 0.1155 | 0.2308 | 0.8694 | 0.0888 | 0.5308 | 0.7389 |

5 | 0.0969 | 0.1396 | 0.6311 | 0.0814 | 0.4388 | 0.6398 |

6 | 0.0889 | 0.1131 | 0.8332 | 0.0768 | 0.4098 | 0.8798 |

7 | 0.0835 | 0.1094 | 0.5694 | 0.0732 | 0.3994 | 0.6873 |

8 | 0.0811 | 0.1088 | 0.6377 | 0.0711 | 0.3768 | 0.7997 |

9 | 0.0678 | 0.1046 | 0.8296 | 0.0689 | 0.3288 | 0.7654 |

10 | 0.0561 | 0.0991 | 0.7156 | 0.0605 | 0.3109 | 0.5679 |

11 | 0.0458 | 0.0934 | 0.8967 | 0.0598 | 0.2987 | 0.6899 |

12 | 0.0449 | 0.0808 | 0.7855 | 0.0534 | 0.2855 | 0.6656 |

13 | 0.0328 | 0.0623 | 0.8987 | 0.0511 | 0.2616 | 0.8202 |

14 | 0.0389 | 0.0431 | 0.0934 | 0.0109 | 0.0934 | 0.8144 |

15 | 0.0356 | 0.0334 | 0.0557 | 0.0098 | 0.0557 | 0.6098 |

16 | 0.0298 | 0.0308 | 0.9131 | 0.0082 | 0.0198 | 0.7813 |

17 | 0.0211 | 0.0131 | 0.9933 | 0.0067 | 0.0125 | 0.8989 |

18 | 0.0469 | 0.0134 | 0.8131 | 0.0096 | 0.0139 | 0.5199 |

19 | 0.0209 | 0.0308 | 0.8694 | 0.0734 | 0.0394 | 0.7656 |

20 | 0.0668 | 0.0396 | 0.7366 | 0.0611 | 0.0108 | 0.8088 |

**Bold**show optimum number of hidden layer neuron.

Source | Std Dev. | R^{2} | Adj. R^{2} | Pred. R^{2} | PRESS | Remark |
---|---|---|---|---|---|---|

Linear | 20.93 | 0.5617 | 0.6541 | 0.6045 | 6489.65 | |

2FI | 9.956 | 0.9014 | 0.7681 | 0.8988 | 5109.99 | |

Quadratic | 3.361 | 0.9985 | 0.9864 | 0.9966 | 169.23 | Suggested |

Cubic | 2.951 | 0.8941 | 0.8679 | 0.7899 | 3705.82 | Aliased |

Source | Sum of squares | df | Mean square | F-value | Prob > F | |

Model | 16283.54 | 9 | 1809.282 | 329.55 | 0.0012 | Significant |

A | 738.38 | 1 | 738.38 | 6.5161 | <0.0001 | |

B | 10544.33 | 1 | 10,544.33 | 679.87 | <0.0001 | |

C | 6318.32 | 1 | 6318.32 | 14.788 | <0.0001 | |

D | 307.43 | 1 | 307.43 | 2.0871 | 0.0494 | |

E | 189.66 | 1 | 189.66 | 298.34 | <0.0001 | |

AB | 987.11 | 1 | 987.11 | 679.87 | <0.0001 | |

AC | 133.68 | 1 | 133.68 | 14.788 | 0.0496 | |

AD | 1569.56 | 1 | 1569.56 | 24.661 | 0.0586 | |

AE | 1875.55 | 1 | 1875.55 | 98.343 | 0.3556 | |

BC | 334.38 | 1 | 334.38 | 19.878 | 0.0001 | |

BD | 564.99 | 1 | 564.99 | 3.7881 | 0.0589 | |

BE | 68.55 | 1 | 68.55 | 4.6619 | <0.0001 | |

CD | 348.99 | 1 | 348.99 | 8.3466 | 0.1558 | |

CE | 167.57 | 1 | 167.57 | 6.8745 | 0.2596 | |

DE | 1038.44 | 1 | 1038.44 | 1.7889 | 0.3856 | |

A^{2} | 568.66 | 1 | 568.66 | 4.0911 | <0.0001 | |

B^{2} | 568.77 | 1 | 568.77 | 8.3421 | <0.0001 | |

C^{2} | 38.38 | 1 | 38.38 | 9.8731 | 0.6018 | |

D^{2} | 138.55 | 1 | 138.55 | 14.788 | 0.2429 | |

E^{2} | 1705.38 | 1 | 1705.38 | 150.88 | <0.0001 | |

Residual | 1239.345 | 11 | 112.667 | |||

Lack of fit | 678.4321 | 8 | 84.804 | 1.758 | 0.6588 | Not significant |

Pure error | 560.9128 | 5 | 112.183 | |||

Total | 17522.89 | 20 |

Accommodation Type | Season | Per Day | Predicted for Next 3 Years (kg) | ||
---|---|---|---|---|---|

Observed (kg) | ANN | CCD | MLR | ||

Small hotel | Peak | 864.1 | 3092.3 | 2292.3 | 2679.8 |

Lean | 399.5 | 1848.5 | 1679.6 | 1799.5 | |

Medium hotel | Peak | 479.5 | 1870.1 | 1822.1 | 1987.9 |

Lean | 233.7 | 934.8 | 978.9 | 698.7 | |

Large hotel | Peak | 2727.8 | 12275.1 | 11891.7 | 12098.5 |

Lean | 1377.3 | 7898.9 | 9873.9 | 88761.8 | |

Guesthouse | Peak | 88.5 | 389.8 | 334.9 | 278.6 |

Lean | 52.9 | 198.6 | 160.7 | 256.8 | |

Statistical parameters | ANN | CCD | MLR | ||

R^{2} | 0.9982 | 0.8982 | 0.9054 | ||

MAE | 1.378 | 1.469 | 3.981 | ||

SEP | 2.153 | 4.71 | 9.891 | ||

HYBRID | 98.781 | 103.4 | 145.9 |

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**MDPI and ACS Style**

Azarmi, S.L.; Oladipo, A.A.; Vaziri, R.; Alipour, H. Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus. *Sustainability* **2018**, *10*, 2965.
https://doi.org/10.3390/su10092965

**AMA Style**

Azarmi SL, Oladipo AA, Vaziri R, Alipour H. Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus. *Sustainability*. 2018; 10(9):2965.
https://doi.org/10.3390/su10092965

**Chicago/Turabian Style**

Azarmi, Soolmaz L., Akeem Adeyemi Oladipo, Roozbeh Vaziri, and Habib Alipour. 2018. "Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus" *Sustainability* 10, no. 9: 2965.
https://doi.org/10.3390/su10092965