Smart Water Harvester

Year : 2024 | Volume : 11 | Issue : 03 | Page : 1-7
    By

    Namratha Km,

  • Lekha Achuth,

Abstract

Smart water harvester is a wiser use of Data Science in the optimization of rainwater harvesting, taking into account the forecast of precipitation and ideal catchment areas, and basically image processing using machine learning. In that respect, the system, via predictive algorithms like Random Forests, predicts the amount of rainfall by taking into consideration historical and real-time data, while Digital Elevation Models (DEM) and visualization methodologies of images make geographical area and rooftop surfaces analyze to choose the best water collection surface area. Together with the identification of optimal locations for catchment areas, the system helps in assessing the amount of rainwater that can be collected, including ways of efficiently constructing and managing water storage systems. This tool is particularly useful in urban areas, as catchment rooftop rainfall is usually expensive and cumbersome to undertake without proper guidance. Therefore, the smart water harvester not only reduces these obstacles but also has an added advantage in encouraging the conservation of water supplies, since non-drinking water consumption is incentivized through possible tax credits. Meanwhile, it harnesses methods of data science, such as multivariate regression and watershed algorithms, for high-tech information on rainwater catchment. This helps decision-making at individual homeowners, business enterprises, and municipalities in terms of the installation of centralized rainwater storage systems that reduce dependency on the traditional water supplies and improve resource management. This is done with an overall view of realizing sustainability in water use practices and promoting widespread rainwater harvesting as a better practice of supporting water conservation.

Keywords: Catchment area, image visualization, random forest, digital evaluation model, data science

[This article belongs to Journal of Water Resource Engineering and Management ]

How to cite this article:
Namratha Km, Lekha Achuth. Smart Water Harvester. Journal of Water Resource Engineering and Management. 2024; 11(03):1-7.
How to cite this URL:
Namratha Km, Lekha Achuth. Smart Water Harvester. Journal of Water Resource Engineering and Management. 2024; 11(03):1-7. Available from: https://journals.stmjournals.com/jowrem/article=2024/view=186683



References

  1. Viet-Hung Dang, Tien Bui Dieu, Xuan-Linh Tran, and Nhat- Duc Hoang. Enhancing the accuracy of rainfall-induced land- slide prediction along mountain roads with a gis-based ran- dom forest classifier. Bulletin of Engineering Geology and the Environment, 78:2835–2849, 2019.
  2. Meike Ku¨hnlein, Tim Appelhans, Boris Thies,  and Thomas Nauss. Improving the accuracy of rainfall rates from optical satellite sensors with machine learning — a random forests- based approach applied to msg seviri. Remote Sensing of En- vironment, 141:129–143, 2014.
  3. Jie Dou, Ali P. Yunus, Dieu Tien Bui, Abdelaziz Merghadi, Mehebub Sahana, Zhongfan Zhu, Chi-Wen Chen, Khabat Khosravi, Yong Yang, and Binh Thai Pham. Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the izu- oshima volcanic island, japan. Science of The Total Environ- ment, 662:332–346, 2019.
  4. Han Sun, Jingyu Yang, and Mingwu Ren. A fast watershed algorithm based on chain code and its application in image segmentation. Pattern Recognition Letters, 26(9):1266–1274, 2005.
  5. Chalachew Muluken Liyew and Haileyesus Amsaya Melese. Machine learning techniques to predict daily rainfall amount. Journal of Big Data, 8:1–11, 2021.
  6. Xu Chu, Ihab F. Ilyas, Sanjay Krishnan, and Jiannan Wang. Data cleaning: Overview and emerging challenges. In Proceedings of the 2016 International Conference on Management of Data, SIGMOD ’16, page 2201–2206, New York, NY, USA, 2016. Association for Computing Machinery.
  7. Quoc Bao Pham, Tao-Chang Yang, Chen-Min Kuo, Hung-Wei Tseng, and Pao-Shan Yu. Combing random forest and least square support vector regression for improving extreme rain- fall downscaling. Water, 11(3), 2019.
  8. Dongxiao Niu, Keke Wang, Lijie Sun, Jing Wu, and Xiaomin Xu. Short-term photovoltaic power generation forecasting based on random forest feature selection and ceemd: A case study. Applied Soft Computing, 93:106389, 2020.
  9. Amirhossein Shadmehri Toosi, Erfan Ghasemi Tousi, Seyed Ali Ghassemi, Ali Cheshomi, and Sina Alaghmand. A multi-criteria decision analysis approach towards efficient rainwater harvesting. Journal of Hydrology, 582:124501, 2020.
  10. Ari Yair Barrera-Animas, Lukumon O. Oyedele, Muhammad Bilal, Taofeek Dolapo Akinosho, Juan Manuel Davila Delgado, and Lukman Adewale Akanbi. Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting. Machine Learning with Applications, 7:100204, 2022.
  11. Nan Wu and Yuan Xie. A survey of machine learning for computer architecture and systems. ACM Comput. Surv., 55(3), feb 2022.
  12. Dominik Kreuzberger, Niklas Ku¨hl, and  Sebastian  Machine learning operations (mlops): Overview, definition and architecture. IEEE Access, 11:31866–31879, 2023.
  13. Chukwuma J. Okolie and Julian L. Smit. A systematic re- view and meta-analysis of digital elevation model (dem) fusion: pre-processing, methods and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 188:1–29, 2022.
Regular Issue Subscription Original Research
Volume 11
Issue 03
Received 22/08/2024
Accepted 03/09/2024
Published 10/09/2024
Total Visits: 98


My IP

PlumX Metrics