Predicting the Welfare Cost of Premature Deaths Based on Unsafe Sanitation Risk using SutteARIMA and Comparison with Neural Network Time Series and Holt-Winters

Suwardi Annas - Universitas Negeri Makassar, Makassar, 90223, Indonesia
Ansari Saleh Ahmar - Universitas Negeri Makassar, Makassar, 90223, Indonesia
Rahmat Hidayat - Politeknik Negeri Padang, Padang, 25164, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.7.1.1685

Abstract


Unhealthy and unsafe sanitation will make it easier for various diseases to attack the body. In addition, unsafe sanitation will also affect a country's economy, including declining welfare, tourism losses, and environmental losses due to the loss of productive land. The research aimed to estimate the welfare cost of premature deaths based on unsafe sanitation risks using the SutteARIMA, Neural Network Time Series, and Holt-Winters. The study analyzed estimates and projections of the welfare cost of premature deaths based on the risks of unsafe sanitation of BRICS countries (Brazil, Russia, Indonesia, China, and South Africa). The data in this research used secondary data. Secondary time series data was taken from the Environment Database of the OECD. Stat. (Mortality and welfare cost from exposure to environmental risks). The data on the study was based on variables: welfare cost of premature deaths, % GDP equivalent, risk: unsafe sanitation, age: all, sex: both, unit: percentage, and data from 2005 to 2019. The three forecasting methods (SutteARIMA, Neural Network Time Series, and Holt-Winters) were juxtaposed in fitting data to see the forecasting methods' reliability and accuracy. The accuracy of forecasting results was compared based on MAPE and MSE values. The results of the research showed that the SutteARIMA and NNAR(1,1) methods were best used to predict the welfare cost of premature deaths in view of unsafe sanitation risks for BRICS countries.

Keywords


Forecasting; welfare cost; premature deaths; unsafe sanitation; SutteARIMA; NNAR; holt-winters.

Full Text:

PDF

References


National Research Council, Soil and water quality: An agenda for agriculture. National Academies Press, 1993.

WHO, “Sanitation,†2019. https://www.who.int/news-room/fact-sheets/detail/sanitation (accessed Oct. 01, 2021).

H. Van Minh and N. V. Hung, “Economic Aspects of Sanitation in Developing Countries,†Environ. Health Insights, vol. 5, p. EHI.S8199, Jan. 2011, doi: 10.4137/EHI.S8199.

A. Tyagi and G. Hutton, “Economic impacts of sanitation in India,†2008.

L. Napitupulu and G. Hutton, “Economic impacts of sanitation in Indonesia,†2008.

OECD, Gender and the Environment: Building Evidence and Policies to Achieve the SDGs. Paris: OECD Publishing, 2021.

A. S. Ahmar and E. Boj, “The date predicted 200.000 cases of COVID-19 in Spain,†J. Appl. Sci. Eng. Technol. Educ., vol. 2, no. 2, Jun. 2020, doi: 10.35877/454RI.asci22102.

A. S. Ahmar, E. Boj, M. A. El Safty, S. AlZahrani, and H. El-Khawaga, “SutteARIMA: A Novel Method for Forecasting the Infant Mortality Rate in Indonesia,†Comput. Mater. Contin., vol. 70, no. 3, pp. 6007–6022, 2022, doi: 10.32604/cmc.2022.021382.

P. K. Singh, A. Chouhan, R. K. Bhatt, R. Kiran, and A. S. Ahmar, “Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA,†Qual. Quant., Jul. 2021, doi: 10.1007/s11135-021-01207-6.

D.-H. Shih, T.-W. Wu, M.-H. Shih, M.-J. Yang, and D. C. Yen, “A Novel βSA Ensemble Model for Forecasting the Number of Confirmed COVID-19 Cases in the US,†Mathematics, vol. 10, no. 5, p. 824, Mar. 2022, doi: 10.3390/math10050824.

GBD, “Global Burden of Disease Study 2019 Results,†Seattle, United States, 2019. [Online]. Available: http://ghdx.healthdata.org/gbd-results-tool.

OCDC, “The Rising Cost of Ambient Air Pollution thus far in the 21st Century: Results from the BRIICS and the OECD Countries,†Paris, 2017. doi: 10.1787/d1b2b844-en.

Institute for Health Metrics and Evaluation, “Unsafe water, sanitation, and handwashing — Level 2 risk,†2019. http://www.healthdata.org/results/gbd_summaries/2019/unsafe-water-sanitation-and-handwashing-level-2-risk (accessed Oct. 01, 2021).

A. S. Ahmar, M. Botto-Tobar, A. Rahman, and R. Hidayat, “Forecasting the Value of Oil and Gas Exports in Indonesia using ARIMA Box-Jenkins,†JINAV J. Inf. Vis., vol. 3, no. 1, pp. 35–42, Jul. 2022, doi: 10.35877/454RI.jinav260.

Y. Duan, H. Wang, M. Wei, L. Tan, and T. Yue, “Application of ARIMA-RTS optimal smoothing algorithm in gas well production prediction,†Petroleum, vol. 8, no. 2, pp. 270–277, Jun. 2022, doi: 10.1016/j.petlm.2021.09.001.

Y. Ning, H. Kazemi, and P. Tahmasebi, “A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet,†Comput. Geosci., vol. 164, p. 105126, Jul. 2022, doi: 10.1016/j.cageo.2022.105126.

A. Alsharef, K. Aggarwal, Sonia, M. Kumar, and A. Mishra, “Review of ML and AutoML Solutions to Forecast Time-Series Data,†Arch. Comput. Methods Eng., vol. 29, no. 7, pp. 5297–5311, Nov. 2022, doi: 10.1007/s11831-022-09765-0.

W. W. S. Wei, Time Series Analysis: Univariate and Multivariate Methods. New York: Addison-Wesley Publishing Company, 1994.

T. R. G. et al., “A deep neural networks based model for uninterrupted marine environment monitoring,†Comput. Commun., vol. 157, pp. 64–75, May 2020, doi: 10.1016/j.comcom.2020.04.004.

S. Jafarianâ€Namin, S. M. T. Fatemi Ghomi, M. Shojaie, and S. Shavvalpour, “Annual forecasting of inflation rate in Iran: Autoregressive integrated moving average modeling approach,†Eng. Reports, vol. 3, no. 4, Apr. 2021, doi: 10.1002/eng2.12344.

R. Jamil, “Hydroelectricity consumption forecast for Pakistan using ARIMA modeling and supply-demand analysis for the year 2030,†Renew. Energy, vol. 154, pp. 1–10, Jul. 2020, doi: 10.1016/j.renene.2020.02.117.

M. M. H. Khan, N. S. Muhammad, and A. El-Shafie, “Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting,†J. Hydrol., vol. 590, p. 125380, Nov. 2020, doi: 10.1016/j.jhydrol.2020.125380.

A. S. Ahmar, A. Rahman, and U. Mulbar, “α- Sutte Indicator: a new method for time series forecasting,†J. Phys. Conf. Ser., vol. 1040, no. 1, p. 012018, 2018.

A. S. Ahmar, “A Comparison of α-Sutte Indicator and ARIMA Methods in Renewable Energy Forecasting in Indonesia,†Int. J. Eng. Technol., vol. 7, no. 1.6, p. 20, Jan. 2018, doi: 10.14419/ijet.v7i1.6.12319.

Y. Wang, C. Xu, S. Yao, and Y. Zhao, “Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α -Sutte Indicator,†Epidemiol. Infect., vol. 148, p. e236, Oct. 2020, doi: 10.1017/S095026882000237X.

A. M. C. H. Attanayake and S. S. N. Perera, “Forecasting COVID-19 Cases Using Alpha-Sutte Indicator: A Comparison with Autoregressive Integrated Moving Average (ARIMA) Method,†Biomed Res. Int., vol. 2020, pp. 1–11, Dec. 2020, doi: 10.1155/2020/8850199.

F. Gao and X. Shao, “Forecasting annual natural gas consumption via the application of a novel hybrid model,†Environ. Sci. Pollut. Res., vol. 28, no. 17, pp. 21411–21424, May 2021, doi: 10.1007/s11356-020-12275-w.

A. Preiss et al., “Incorporation of near-real-time hospital occupancy data to improve hospitalization forecast accuracy during the COVID-19 pandemic,†Infect. Dis. Model., vol. 7, no. 1, pp. 277–285, Mar. 2022, doi: 10.1016/j.idm.2022.01.003.

United Nations, SDG 6 Synthesis Report 2018 on Water and Sanitation. New York: United Nations, 2018.

R. Thoplan, “Simple v/s Sophisticated Methods of Forecasting for Mauritius Monthly Tourist Arrival Data,†Int. J. Stat. Appl., vol. 4, no. 5, pp. 217–223, 2014, doi: 10.5923/j.statistics.20140405.01.