(Faculty of Civil Engineering, Industrial University of Ho Chi Minh City (IUH), Ho Chi Minh City, Vietnam, Viet Nam)(2) N. Pham-Phuong
(Faculty of Civil Engineering, HUTECH University, Vietnam, Viet Nam)(3) N. Luu-Huu
(Faculty of Civil Engineering, HUTECH University, Vietnam, Viet Nam)(4) * L. Nguyen-Son
(Faculty of Civil Engineering, Industrial University of Ho Chi Minh City (IUH), Ho Chi Minh City, Vietnam, Viet Nam)*corresponding author
AbstractDisparities in sanitation coverage between urban and rural Vietnam remain a major barrier to sustainable development and public health, especially in rural areas with high poverty and limited clean water access. This study integrates machine learning and sustainable infrastructure to forecast sanitation coverage and address urban-rural disparities. Using data from 2018 to 2023, socio-economic factors (poverty rate, per capita income) and technical indicators (clean water access) were incorporated into Artificial Neural Network (ANN) and Support Vector Machine (SVM) models, evaluated via five-fold cross-validation. Results showed strong correlations (clean water-sanitation: r = 0.99; poverty-sanitation: r = -0.97), with ANN significantly outperforming SVM (validation R² = 0.999972, RMSE = 0.018912%, MAE = 0.1474%). Rural sanitation coverage increased slowly from 84.7% to 94.7%, while urban areas approached optimal levels. The findings demonstrate that machine learning effectively captures non-linear patterns in heterogeneous data for accurate forecasting. To tackle persistent rural gaps, urine-diverting sanitation systems are proposed as a sustainable solution that conserves water, reuses urine as nitrogen-rich fertilizer, and supports circular economy principles aligned with SDG 6. This approach provides practical insights for policymakers to bridge urban-rural inequalities, improve public health, and promote resilient infrastructure in developing contexts.
KeywordsMachine Learning; Sanitation Coverage; Urban-Rural Disparities; Urine-Diverting Sanitation; Sustainable Infrastructure
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DOIhttps://doi.org/10.26555/ijain.v12i3.2403 |
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References
[1] N. Nasim, A. El-Zein, and J. Thomas, “A review of rural and peri-urban sanitation infrastructure in South-East Asia and the Western Pacific: Highlighting regional inequalities and limited data,” Int. J. Hyg. Environ. Health, vol. 244, p. 113992, 2022, doi: 10.1016/j.ijheh.2022.113992.
[2] N. M. W. Satyawati, I. M. Candiasa, and N. M. S. Mertasari, “Prediksi Penduduk Miskin di Indonesia menggunakan Analisis Dekomposisi,” Volume, vol. 9, p. 80, 2021, doi: 10.31941/delta.v9i1.1248.
[3] M. E. Hacker and J. A. Kaminsky, “Cultural preferences for the methods and motivation of sanitation infrastructure development,” J. Water, Sanit. Hyg. Dev., vol. 7, no. 3, pp. 407–415, 2017, doi: 10.2166/washdev.2017.188.
[4] H. H. Al-Ahmadi, “The significance of software engineering to forecast the public health issues: a case of Saudi Arabia,” Front. Public Heal., vol. 10, p. 900075, 2022, doi: 10.3389/fpubh.2022.900075.
[5] C. Alda-Vidal, M. Lawhon, D. Iossifova, and A. L. Browne, “Living with fragile infrastructure: The gendered labour of preventing, responding to and being impacted by sanitation failures,” Geoforum, vol. 141, p. 103724, 2023, doi: 10.1016/j.geoforum.2023.103724.
[6] A. F. Prabasari, C. Jesica, F. K. Arsandha, and N. Kusumawati, “Peramalan Jumlah Rumah Tidak Layak Huni (Rtlh) Guna Mengatasi Kemiskinan Di Daerah Istimewa Yogyakarta,” J. Akuntansi, Ekon. dan Manaj. Bisnis, vol. 3, no. 2, pp. 176–185, 2023, doi: 10.55606/jaemb.v3i2.1612.
[7] O. K. Pal, “The quality of drinkable water using machine learning techniques,” Int. J. Adv. Eng. Res. Sci., vol. 9, no. 6, pp. 16–23, 2022, doi: O. K. Pal, “The Quality of Drinkable Water using Machine Learning Techniques,” International Journal of Advanced Engineering Research and Science, vol. 9, no. 6, pp. 016-023, 2022, doi: 10.22161/ijaers.96.2.
[8] X.-J. Tian and Z.-J. Ge, “Collaborative governance of rural relative poverty under blockchain and back propagation neural network,” Adv. Multimed., vol. 2022, no. 1, p. 5344163, 2022, doi: 10.1155/2022/5344163.
[9] Q. Zhou, N. Chen, and S. Lin, “A poverty measurement method incorporating spatial correlation: A case study in Yangtze River Economic Belt, China,” ISPRS Int. J. Geo-Information, vol. 11, no. 1, p. 50, 2022, doi: 10.3390/ijgi11010050.
[10] J. Mundonde and P. L. Makoni, “Determinants of the successful signing of public private partnership contracts for water and sanitation infrastructure financing in Zimbabwe,” AQUA Water Infrastructure, Ecosyst. Soc., vol. 72, no. 12, pp. 2229–2238, 2023, doi: 10.2166/aqua.2023.130.
[11] P. Tiwari, R. D. Tirumala, and J. Shukla, “Household choices of sanitation infrastructure and impact on disease in India,” Environ. Plan. B Urban Anal. City Sci., vol. 49, no. 8, pp. 2054–2071, 2022, doi: 10.1177/23998083221088293.
[12] M. O’Gorman, “Mental and physical health impacts of water/sanitation infrastructure in First Nations communities in Canada: An analysis of the Regional Health Survey,” World Dev., vol. 145, p. 105517, 2021, doi: 10.1016/j.worlddev.2021.105517.
[13] A. Marques Arsénio, I. Câmara Salim, M. Hu, N. Pedro Matsinhe, R. Scheidegger, and L. Rietveld, “Mitigation potential of sanitation infrastructure on groundwater contamination by nitrate in Maputo,” Sustainability, vol. 10, no. 3, p. 858, 2018, doi: 10.3390/su10030858.
[14] F. Andrianus and K. Alfatih, “Pengaruh infrastruktur terhadap kemiskinan dengan menggunakan data panel 34 provinsi di Indonesia,” J. Inform. Ekon. Bisnis, pp. 56–62, 2023, doi: 10.37034/infeb.v5i1.206.
[15] Sushant Malik and Nisha Bharti, “Models of Financing Sanitation Infrastructure Initiatives in India: Challenges and Opportunities,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. No. 11, pp. 392–400, 2019, doi: 10.35940/ijitee.K1070.09811S19.
[16] K. J. Setshedi, N. Mutingwende, and N. P. Ngqwala, “The use of artificial neural networks to predict the physicochemical characteristics of water quality in three district municipalities, eastern cape province, South Africa,” Int. J. Environ. Res. Public Health, vol. 18, no. 10, p. 5248, 2021, doi: 10.3390/ijerph18105248.
[17] C. Peña-Guzmán, P. Balaguera, N. Hernandez, and R. Sierra, “Redesign of Water Quality Network for the Urban Rivers in Salitre in Bogotá, Colombia, Using an Artificial Neural Network,” in International Conference on Urban Drainage Modelling, 2018, pp. 915–919, doi: 10.1007/978-3-319-99867-1_157.
[18] M. Yanto, “Deep learning approach analysis model prediction and classification poverty status,” IAES Int. J. Artif. Intell., 2023, doi: 10.11591/ijai.v12.i1.pp459-468.
[19] D. L. Peña-Orozco, M. E. Londoño-Escobar, A. M. Paredes Rodríguez, J. Gonzalez-Feliu, and G. Navarrete Meneses, “Prioritizing public policy implementation for rural development in a developing country via multicriteria classification,” Economies, vol. 12, no. 1, p. 3, 2023, doi: 10.3390/economies12010003.
[20] N. S. Sabet and S. Khaksar, “The performance of local government, social capital and participation of villagers in sustainable rural development,” Soc. Sci. J., vol. 61, no. 1, pp. 1–29, 2024, doi: 10.1080/03623319.2020.1782649.
[21] E. C. Wells et al., “Meeting the water and sanitation challenges of underbounded communities in the US,” Environ. Sci. Technol., vol. 56, no. 16, pp. 11180–11188, 2022, doi: 10.1021/acs.est.2c03076.
[22] E. Sampelalong and N. M. Sukartini, “Infrastructure And Development,” Gorontalo Dev. Rev., pp. 14–27, 2020, doi: 10.32662/golder.v3i1.838.
[23] E. Mustakangas and H. Vihinen, “How do municipal strategies work in rural development: A critical realist case study,” J. Rural Stud., vol. 106, p. 103215, 2024, doi: 10.1016/j.jrurstud.2024.103215.
[24] S. Zheng and X. Hu, “Early warning method for public health emergency under artificial neural network in the context of deep learning,” Front. Psychol., vol. 12, p. 594031, 2021, doi: 10.3389/fpsyg.2021.594031.
[25] S. Kishore, U. Venkatesh, S. K. Verma, S. K. Verma, and P. Walia, “Water, sanitation, and hygiene: a global imperative for health,” Indian J. Community Heal., vol. 35, no. 3, pp. 367–371, 2023, doi: 10.47203/IJCH.2023.v35i03.022.
[26] A. Zahrunnisa, R. D. Nafalana, I. A. Rosyada, and E. Widodo, “Perbandingan Metode Exponential Smoothing dan ARIMA pada peramalan garis kemiskinan provinsi Jawa Tengah,” J. Lebesgue J. Ilm. Pendidik. Mat. Mat. dan Stat., vol. 2, no. 3, pp. 300–314, 2021, doi: 10.46306/lb.v2i3.91.
[27] T. P. Bui and K. S. Imai, “Determinants of rural-urban inequality in Vietnam: Detailed decomposition analyses based on unconditional quantile regressions,” J. Dev. Stud., vol. 55, no. 12, pp. 2610–2625, 2019, doi: 10.1080/00220388.2018.1536265.
[28] R. S. Mallek, A. Awad, I. Ozturk, and I. ben Douissa, “Infrastructure development and poverty eradication in sub-Saharan Africa; Its effect and transmission channels,” Cities, vol. 144, p. 104658, 2024, doi: 10.1016/j.cities.2023.104658.
[29] D. K. Silalahi, “Forecasting of poverty data using seasonal ARIMA modeling in west java province,” J. Teor. dan Apl. Mat., vol. 4, no. 1, pp. 76–86, 2020, doi: 10.31764/jtam.v4i1.1888.
[30] K. T. Swe et al., “Impact of poverty reduction on access to water and sanitation in low‐and lower middle income countries: country specific Bayesian projections to 2030,” Trop. Med. Int. Heal., vol. 26, no. 7, pp. 760–774, 2021, doi: 10.1111/tmi.13580.
[31] Z. Bolatova, K. Tussupova, B. Toleubekov, K. Sukhanberdiyev, K. Sharapatova, and M. Stafström, “Challenges of access to WASH in schools in low-and middle-income countries: case study from rural central Kazakhstan,” Int. J. Environ. Res. Public Health, vol. 18, no. 18, p. 9652, 2021, doi: 10.3390/ijerph18189652.
[32] S. Schramm and L. Wright-Contreras, “Beyond passive consumption: Dis/ordering water supply and sanitation at Hanoi’s urban edge,” Geoforum, vol. 85, pp. 299–310, 2017, doi: 10.1016/j.geoforum.2017.06.016.
[33] V. Grigor’ev, V. L. Nugen, R. Nizamov, and I. Grigorev, “Policy Of The Socialist Republic Of Vietnam On Rural Development,” Vestn. Kazan State Agrar. Univ., vol. 16, no. 1, pp. 112–118, 2021, doi: 10.12737/2073-0462-2021-112-118.
[34] S. M. Ammar and S. Kouser, “Exploring the Impact of Improved Access to Safe Drinking Water and Sanitation on Subjective Wellbeing of Adults in Punjab,” NUST J. Soc. Sci. Humanit., vol. 8, no. 2, pp. 235–252, 2022, doi: 10.51732/njssh.v8i2.160.
[35] T. T. Truc, “The Tourism Poverty Nexus: Does Tourism Spending Influence Poverty Rates? Empirical Evidence from Regional Vietnamese Data,” J. Poverty, vol. 27, no. 2, pp. 153–165, 2023, doi: 10.1080/10875549.2021.2010870.
[36] N. P. Le, “Education, employment, and income: An overview of rural-urban gaps in Vietnam,” Vietnam J. Agric. Sci., vol. 3, no. 2, pp. 647–661, 2020, doi: 10.31817/vjas.2020.3.2.08.
[37] L. Braun et al., “Associations between shared sanitation, stunting and diarrhoea in low-income, high density urban neighbourhoods of Maputo, Mozambique-a cross-sectional study,” Matern. Child Health J., vol. 28, no. 4, pp. 775–784, 2024, doi: 10.1007/s10995-024-03924-4.
[38] M. R. Assel, B. R. A. Hanoeboen, A. A. Laitupa, and F. Saptenno, “Forecasting Macroeconomic Variables and their Effect on Poverty,” J. Econ. Business, Account. Ventur., vol. 25, no. 3, pp. 322–337, 2022, doi: 10.14414/jebav.v25i3.3451.
[39] A. Bouabid and G. Louis, “Decision support system for selection of appropriate water supply and sanitation technologies in developing countries,” J. Water, Sanit. Hyg. Dev., vol. 11, no. 2, pp. 208–221, 2021, doi: 10.2166/washdev.2021.203.
[40] A. Kusumawardana and N. Hidayati, “Data forecasting model to know the social impact of poverty in the era of globalization in West Java Province, Indonesia,” Es Econ. Entrep., vol. 1, no. 02, pp. 49–57, 2022, doi: 10.58812/esee.v1i02.45.
[41] D. Biswas and S. Joshi, “Sanitation and gendered psychosocial stress in peri-urban Bangalore,” Dev. Pract., vol. 31, no. 4, pp. 548–557, 2021, doi: 10.1080/09614524.2020.1862762.
[42] G. Charles, S. Otieno, M. Kimaro, E. Heriel, T. Mwakasisi, and J. Mganga, “Determinants of multidimensional poverty in rural Tanzania,” Rural Plan. J., vol. 25, no. 1, pp. 81–96, 2023, doi: 10.59557/kavham44.
[43] S. Setyadi, S. Didu, L. Indriyani, A. K. Fitri, and A. Wiidiastuti, “Modeling life expectancy in Indonesia using system GMM model,” Rev. Appl. Socio-Economic Res., vol. 25, no. 1, pp. 83–98, 2023, doi: 10.54609/reaser.v25i1.338.
[44] P. P. Min, Y. W. Gan, S. N. B. Hamzah, T. S. Ong, and M. S. Sayeed, “Poverty prediction using machine learning approach,” J. Southwest Jiaotong Univ., vol. 57, no. 1, pp. 136–146, 2022, doi: 10.35741/issn.0258-2724.57.1.12.
[45] R. Gibb et al., “Interactions between climate change, urban infrastructure and mobility are driving dengue emergence in Vietnam,” Nat. Commun., vol. 14, no. 1, p. 8179, 2023, doi: 10.1038/s41467-023-43954-0.
[46] M. J. Rudolph and E. Muchesa, “Integrated and sustainable water and sanitation systems at two rural sites in South Africa,” Water, vol. 15, no. 22, p. 3974, 2023, doi: 10.3390/w15223974.
[47] D. Y. Dalimunthe, “Fit of Statistical Forecasting Model Berdasarkan Variabel Angka Kemiskinan Di Provinsi Kepulauan Bangka Belitung,” E-Jurnal Mat, vol. 9, no. 2, p. 117, 2020, doi: 10.24843/MTK.2020.v09.i02.p288.
[48] P. J. Atapattu, “Poverty Reduction Effect of Infrastructure: A Cross Country Study of Developing Countries in Asia,” J. Bus. Technol., vol. 4, no. 1–2, 2020, doi: 10.4038/jbt.v4i1-2.38.
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