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M3mlf: Mapping, measuring and monitoring using the machine learning framework for dynamic water source classification

Publication Type : Conference Paper

Publisher : IEEE

Source : 2022 IEEE Global Humanitarian Technology Conference (GHTC), pp. 421-428. IEEE, 2022

Url : https://ieeexplore.ieee.org/document/9911054

Campus : Amritapuri

Center : Amrita Center for Wireless Networks and Applications (AmritaWNA)

Year : 2022

Abstract : Water is the most valuable natural resource for all living organisms. Ever-increasing population, climatic variability, and anthropogenic activities are exacerbating the world’s water crisis. However, the community members and local administration lack the knowledge of dynamically varying water contamination levels and public health impacts due to this change. The proposed work is aimed to design an IoT system for real-time monitoring of water quality in geographically separated heterogeneous water sources. Support vector-based classification is implemented to understand the dynamic water quality variability of spatially distributed heterogeneous water resources from Thiruvananthapuram, Kerala, India. The sources were mapped into any of the three classes such as Good, Poor, and Unsuitable, and SVM based classification is performed. The results show very good precision and recall in predicting the water quality index class for the test data. To enhance community empowerment, a public portal is developed for easy visualization and information dissemination to multiple stakeholders. The portal provides spatial trends, temporal trends for an area or a particular location, area-wise prevalence of selected parameters, determination of water quality index, classification of specific sources based on water quality index, and proposing the required treatments for the sources based on its water quality index.

Cite this Research Publication : Amritanand, S., Ashwin Shenoy, C. Megha Dev, A. S. Reshma, and P. Rekha. "M3mlf: Mapping, measuring and monitoring using the machine learning framework for dynamic water source classification." In 2022 IEEE Global Humanitarian Technology Conference (GHTC), pp. 421-428. IEEE, 2022

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