Abstract
Su tüketiminin tahmini, küresel sürdürülebilirlik hedeflerine ulaşmada ve vatandaşların uzun vadeli su ihtiyaçlarını karşılamada çok önemli bir görevdir. Bireysel su ayak izlerini tahmin etmek için bazı çalışmalar yapılmış olsa da, bu alanda hala sınırlı miktarda araştırma bulunmaktadır. Bu sınırı gidermeye yönelik olarak, bu makale, su ayak izi göstergesi aracılığıyla dolaylı ve doğrudan su kullanımını dikkate alarak bireylerin su tüketim puanlarını tahmin etmek için WaterAI adlı yeni bir yapay zeka tabanlı model önermektedir. Su tüketimi tahmini için en iyi modeli belirlemek adına dört farklı makine öğrenme algoritmasını (doğrusal regresyon, LASSO regresyonu, gradyan artırma ve aşırı gradyan artırma) karşılaştırmaktadır. Veriler bir anket çalışması ile toplanmıştır. Deneysel sonuçlar, önerilen modelin, kişisel su tüketim skorunu etkili bir şekilde tahmin etmek için başarılı bir şekilde kullanılabileceğini göstermektedir.
References
- Alropy I., Kotb A., Al-Hindi A. An economic study of the role of foreign trade in water demand management in the Arab Republic of Egypt according to the concept of virtual water. Egyptian Journal of Agricultural Economics 2015; 25(1): 219-232.
- Alqahtani SH., Alropy ET., Kotb AA., Alaagib SEB. Estimation of the standard model of the water footprint of individuals in the Kingdom of Saudi Arabia. Arabian Journal of Geosciences 2021; 14: 1-12.
- Bhagwat VR. Food Safety and Human Health - Safety of Water Used in Food Production. India: Academic Press; 2019.
- Brindha K. Virtual water flows, water footprint and water savings from the trade of crop and livestock products of Germany. Water and Environment Journal 2020; 34: 656-668.
- ElFetyany M., Farag H., Ghany SHAE. Assessment of national water footprint versus water availability - Case study for Egypt. Alexandria Engineering Journal 2021; 60: 3577–3585.
- Ewing BR., Hawkins TR., Wiedmann TO., Galli A., Ercin AE., Weinzettel J., Steen-Olsen K. Integrating ecological and water footprint accounting in a multi-regional input–output framework. Ecological Indicators 2012; 23: 1-8.
- Gómez-Llanos E., Durán-Barroso P., Robina-Ramírez R. Analysis of consumer awareness of sustainable water consumption by the water footprint concept. Science of The Total Environment 2020; 721: 1-11.
- Haida, C., Chapagain, AK., Rauch, W., Riede, M., Schneider, K. From water footprint to climate change adaptation: Capacity development with teenagers to save water. Land Use Policy 2019; 80: 456-463.
- Harris F., Green RF., Joy EJM., Kayatz B., Haines A., Dangour AD. The water use of Indian diets and socio-demographic factors related to dietary blue water footprint. Science of The Total Environment 2017; 587: 128-136.
- Hoekstra AY., Chapagain AK., Aldaya MM., Mekonnen MM. The water footprint assessment manual: Setting the global standard, UK: Earthscan; 2011.
- Hoekstra AY., Hung PQ. Virtual water trade: a quantification of virtual water flows between nations in relation to international crop trade. UNESCO-IHE Delft - Value of Water Research Report Series 2002; 11: 1-120.
- Kandananond K. The application of water footprint and six-sigma method to reduce the water consumption in an organization. International Journal of GEOMATE 2019; 17(61): 21-27.
- Lares-Michel M., Housni FE., Cervantes VGA., Carrillo P., Nava RMM., Cañedo CL. Eat well to fight obesity… and save water: the water footprint of different diets and caloric intake and its relationship with adiposity. Frontiers in Nutrition 2021; 8: 1-18.
- Lee Y-J. Ecological footprint and water footprint of Taipei, Sustainability 2019; 11: 1-16.
- Lee Y-J., Tung C-M., Lee P-R., Lin S-C. Personal water footprint in taiwan: a case study of Yunlin county. Sustainability 2016; 8: 1-12.
- Li X., Ren J., Wu Z., Wu X., Ding X. Development of a novel process-level water footprint assessment for ettextile production based on modularity. Journal of Cleaner Production 2021; 291: 1-12.
- Mahjabin T., Garcia S., Grady C., Mejia A. Large cities get more for less: Water footprint efficiency across the US. PloS One 2018; 13(8): 1-17.
- Mokhtar A., He H., He W., Elbeltagi A., Maroufpoor S., Azad N., He H., Alsafadi K., Gyasi-Agyei, Y., He W. Estimation of the rice water footprint based on machine learning algorithms. Computers and Electronics in Agriculture 2021; 191: 1-15.
- Özbaş EE., Akın Ö., Güneysu S., Özcan HK., Öngen A. Changes occurring in consumption habits of people during COVID-19 pandemic and the water footprint. Environment, Development and Sustainability 2022; 24(6): 8504-8520.
- Pang Z., Yan D., Wang T., Kong Y. Disparities and drivers of the water footprint of food consumption in China. Environmental science and pollution research international 2021; 28(44): 62461-62473.
- Sobhani SR., Rezazadeh A., Omidvar N., Eini-Zinab H. Healthy diet: a step toward a sustainable diet by reducing water footprint. Journal of the Science of Food and Agriculture 2019; 99(8): 3769-3775.
- Stanic S., Spetic M., Buzov I. The water footprint of an individual: a hidden dimension of sustainability. International Journal of Interdisciplinary Environmental Studies 2015; 10(3): 13-25.
Abstract
The estimation of water consumption is a crucial task in achieving global sustainability targets and addressing the long-term water needs of citizens. While some efforts have been done to estimate individual water footprints, there is still limited research in this area. To address this limitation, this article proposes a new artificial intelligence-based model, called WaterAI, to predict individuals’ water consumption scores by taking into account indirect and direct water use through the water footprint indicator. It compares four different machine learning algorithms (linear regression, LASSO regression, gradient boosting, and extreme gradient boosting) to determine the best one for water consumption estimation. The data were collected with a questionnaire survey. The experimental results show that the proposed model can be successfully used to predict personal water consumption scores in an effective way.
References
- Alropy I., Kotb A., Al-Hindi A. An economic study of the role of foreign trade in water demand management in the Arab Republic of Egypt according to the concept of virtual water. Egyptian Journal of Agricultural Economics 2015; 25(1): 219-232.
- Alqahtani SH., Alropy ET., Kotb AA., Alaagib SEB. Estimation of the standard model of the water footprint of individuals in the Kingdom of Saudi Arabia. Arabian Journal of Geosciences 2021; 14: 1-12.
- Bhagwat VR. Food Safety and Human Health - Safety of Water Used in Food Production. India: Academic Press; 2019.
- Brindha K. Virtual water flows, water footprint and water savings from the trade of crop and livestock products of Germany. Water and Environment Journal 2020; 34: 656-668.
- ElFetyany M., Farag H., Ghany SHAE. Assessment of national water footprint versus water availability - Case study for Egypt. Alexandria Engineering Journal 2021; 60: 3577–3585.
- Ewing BR., Hawkins TR., Wiedmann TO., Galli A., Ercin AE., Weinzettel J., Steen-Olsen K. Integrating ecological and water footprint accounting in a multi-regional input–output framework. Ecological Indicators 2012; 23: 1-8.
- Gómez-Llanos E., Durán-Barroso P., Robina-Ramírez R. Analysis of consumer awareness of sustainable water consumption by the water footprint concept. Science of The Total Environment 2020; 721: 1-11.
- Haida, C., Chapagain, AK., Rauch, W., Riede, M., Schneider, K. From water footprint to climate change adaptation: Capacity development with teenagers to save water. Land Use Policy 2019; 80: 456-463.
- Harris F., Green RF., Joy EJM., Kayatz B., Haines A., Dangour AD. The water use of Indian diets and socio-demographic factors related to dietary blue water footprint. Science of The Total Environment 2017; 587: 128-136.
- Hoekstra AY., Chapagain AK., Aldaya MM., Mekonnen MM. The water footprint assessment manual: Setting the global standard, UK: Earthscan; 2011.
- Hoekstra AY., Hung PQ. Virtual water trade: a quantification of virtual water flows between nations in relation to international crop trade. UNESCO-IHE Delft - Value of Water Research Report Series 2002; 11: 1-120.
- Kandananond K. The application of water footprint and six-sigma method to reduce the water consumption in an organization. International Journal of GEOMATE 2019; 17(61): 21-27.
- Lares-Michel M., Housni FE., Cervantes VGA., Carrillo P., Nava RMM., Cañedo CL. Eat well to fight obesity… and save water: the water footprint of different diets and caloric intake and its relationship with adiposity. Frontiers in Nutrition 2021; 8: 1-18.
- Lee Y-J. Ecological footprint and water footprint of Taipei, Sustainability 2019; 11: 1-16.
- Lee Y-J., Tung C-M., Lee P-R., Lin S-C. Personal water footprint in taiwan: a case study of Yunlin county. Sustainability 2016; 8: 1-12.
- Li X., Ren J., Wu Z., Wu X., Ding X. Development of a novel process-level water footprint assessment for ettextile production based on modularity. Journal of Cleaner Production 2021; 291: 1-12.
- Mahjabin T., Garcia S., Grady C., Mejia A. Large cities get more for less: Water footprint efficiency across the US. PloS One 2018; 13(8): 1-17.
- Mokhtar A., He H., He W., Elbeltagi A., Maroufpoor S., Azad N., He H., Alsafadi K., Gyasi-Agyei, Y., He W. Estimation of the rice water footprint based on machine learning algorithms. Computers and Electronics in Agriculture 2021; 191: 1-15.
- Özbaş EE., Akın Ö., Güneysu S., Özcan HK., Öngen A. Changes occurring in consumption habits of people during COVID-19 pandemic and the water footprint. Environment, Development and Sustainability 2022; 24(6): 8504-8520.
- Pang Z., Yan D., Wang T., Kong Y. Disparities and drivers of the water footprint of food consumption in China. Environmental science and pollution research international 2021; 28(44): 62461-62473.
- Sobhani SR., Rezazadeh A., Omidvar N., Eini-Zinab H. Healthy diet: a step toward a sustainable diet by reducing water footprint. Journal of the Science of Food and Agriculture 2019; 99(8): 3769-3775.
- Stanic S., Spetic M., Buzov I. The water footprint of an individual: a hidden dimension of sustainability. International Journal of Interdisciplinary Environmental Studies 2015; 10(3): 13-25.
Details
| Primary Language | English |
|---|---|
| Subjects | Computer Software |
| Journal Section | RESEARCH ARTICLES |
| Authors | |
| Publication Date | July 5, 2023 |
| Submission Date | June 3, 2022 |
| Acceptance Date | February 10, 2023 |
| Published in Issue | Year 2023 Volume: 6 Issue: 2 |
Cite
*This journal is an international refereed journal
*Our journal does not charge any article processing fees over publication process.
* This journal is online publishes 5 issues per year (January, March, June, September, December)
*This journal published in Turkish and English as open access.
* This work is licensed under a Creative Commons Attribution 4.0 International License.