IoT-based automated system for water-related disease prediction
Abstract Having access to potable water is a fundamental right to well-being. Despite this, 3.4 million people die from diseases caused by water each year, and 1.1 billion people lack access to potable drinking water. Although industrialization, durable infrastructure, and rapid development have inc...
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Nature Portfolio
2024-11-01
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| Online Access: | https://doi.org/10.1038/s41598-024-79989-6 |
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| author | Bhushankumar Nemade Kiran Kishor Maharana Vikram Kulkarni Surajit mondal G S Pradeep Ghantasala Amal Al-Rasheed Masresha Getahun Ben Othman Soufiene |
| author_facet | Bhushankumar Nemade Kiran Kishor Maharana Vikram Kulkarni Surajit mondal G S Pradeep Ghantasala Amal Al-Rasheed Masresha Getahun Ben Othman Soufiene |
| author_sort | Bhushankumar Nemade |
| collection | DOAJ |
| description | Abstract Having access to potable water is a fundamental right to well-being. Despite this, 3.4 million people die from diseases caused by water each year, and 1.1 billion people lack access to potable drinking water. Although industrialization, durable infrastructure, and rapid development have increased living standards, the water problem has left humanity defenseless. As different human activities have contaminated these water reserves, according to an estimate, water is the cause of 80% of ailments. As a result, it is necessary to permit enough infrastructure to ensure the security of a reliable supply of potable water. Thus, a real-time WBPCB dataset with 17 features and a proposed IoT-based system to collect data are used in this research to address the issue. The research paper provides a system for predicting diseases and forecasting long-term trends. Classification is performed using Random Forest, XGBoost, and AdaBoost, which have accuracy rates of 99.66%, 99.52%, and 99.64%, respectively. Forecasting is performed using LSTM, which has an MSE value for the pH parameter of 0.1631. The paper introduces TS-SMOTE, a novel hybridized time-series SMOTE data augmentation approach. Additionally, it offers an IoT system that uses H-ANFIS to gather data in real-time and identify attacks. |
| format | Article |
| id | doaj-art-d2fa78ad5d4b48278d81f51b75d6231f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d2fa78ad5d4b48278d81f51b75d6231f2025-02-02T12:25:01ZengNature PortfolioScientific Reports2045-23222024-11-0114113010.1038/s41598-024-79989-6IoT-based automated system for water-related disease predictionBhushankumar Nemade0Kiran Kishor Maharana1Vikram Kulkarni2Surajit mondal3G S Pradeep Ghantasala4Amal Al-Rasheed5Masresha Getahun6Ben Othman Soufiene7Shree L.R. Tiwari Engineering College, Mumbai UniversityResearcher, ICICI Lombard GIC LtdMukesh Patel School of Technology, Management, and Engineering, SVKM’s NMIMS (Deemed to be University)Researcher, ICICI Lombard GIC LtdDepartment of Computer Science and Engineering, Alliance College of Engineering & Design, Alliance UniversityDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of Computer Science and Information Technology, College of Engineering and Technology, Kebri Dehar UniversityPRINCE Laboratory Research, ISITcom, Hammam Sousse, University of SousseAbstract Having access to potable water is a fundamental right to well-being. Despite this, 3.4 million people die from diseases caused by water each year, and 1.1 billion people lack access to potable drinking water. Although industrialization, durable infrastructure, and rapid development have increased living standards, the water problem has left humanity defenseless. As different human activities have contaminated these water reserves, according to an estimate, water is the cause of 80% of ailments. As a result, it is necessary to permit enough infrastructure to ensure the security of a reliable supply of potable water. Thus, a real-time WBPCB dataset with 17 features and a proposed IoT-based system to collect data are used in this research to address the issue. The research paper provides a system for predicting diseases and forecasting long-term trends. Classification is performed using Random Forest, XGBoost, and AdaBoost, which have accuracy rates of 99.66%, 99.52%, and 99.64%, respectively. Forecasting is performed using LSTM, which has an MSE value for the pH parameter of 0.1631. The paper introduces TS-SMOTE, a novel hybridized time-series SMOTE data augmentation approach. Additionally, it offers an IoT system that uses H-ANFIS to gather data in real-time and identify attacks.https://doi.org/10.1038/s41598-024-79989-6Water-borne diseases predictionWater quality predictionIoT-based diseases predictionTime-series dataForecasting diseasesTS-SMOTE |
| spellingShingle | Bhushankumar Nemade Kiran Kishor Maharana Vikram Kulkarni Surajit mondal G S Pradeep Ghantasala Amal Al-Rasheed Masresha Getahun Ben Othman Soufiene IoT-based automated system for water-related disease prediction Scientific Reports Water-borne diseases prediction Water quality prediction IoT-based diseases prediction Time-series data Forecasting diseases TS-SMOTE |
| title | IoT-based automated system for water-related disease prediction |
| title_full | IoT-based automated system for water-related disease prediction |
| title_fullStr | IoT-based automated system for water-related disease prediction |
| title_full_unstemmed | IoT-based automated system for water-related disease prediction |
| title_short | IoT-based automated system for water-related disease prediction |
| title_sort | iot based automated system for water related disease prediction |
| topic | Water-borne diseases prediction Water quality prediction IoT-based diseases prediction Time-series data Forecasting diseases TS-SMOTE |
| url | https://doi.org/10.1038/s41598-024-79989-6 |
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