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<\/b><\/h1>\nSmart Water Leak Detection product is an IoT-based smart solution designed to monitor water flow and automatically detect leaks in piping systems. This product combines flow sensors, pressure sensors, and artificial intelligence algorithms to detect flow anomalies that indicate leaks. Equipped with cloud connectivity and a monitoring dashboard, this product provides real-time notifications to users to prevent further damage and save water usage.<\/span><\/p>\n This project falls under the IoT\u2011based smart water management domain, focusing on real\u2011time detection and localization of water leaks in PDAM pipelines. By integrating flow sensors, pressure sensors, and an ESP32 microcontroller with embedded AI, it provides accurate leak detection, seamless connectivity to AWS IoT, and remote monitoring capabilities through the Blynk app, making it ideal for supporting PDAM operators and field technicians.<\/span><\/p>\n Water leaks in pipe distribution systems, both in household and industrial environments, are often not detected early. This can lead to waste of water resources, damage to infrastructure, and increase operational costs. Conventional methods for detecting leaks, such as manual inspections or periodic meter readings, are not effective in providing a quick response to sudden or hidden leaks. Therefore, a water leak detection system is needed that is automatic, real-time, and able to provide early notification to users, so that preventive measures can be taken immediately to minimize the negative impacts of the leak.<\/span><\/p>\n To overcome the problem of water leaks that often go undetected and cause waste and damage to infrastructure, a system called Smart Water Leak Detection based on the Internet of Things (IoT) was developed. This system automatically monitors water flow and pressure using integrated sensors, and applies artificial intelligence algorithms to detect leaks in real time. Data is sent to a cloud platform and displayed via a monitoring dashboard, allowing users to receive early notifications and take quick action before the damage becomes more severe.<\/span><\/p>\n You can watch a demo of the Smart Water Leak Detection System here<\/span><\/a><\/p>\n \ud83d\udd2c <\/span>Evaluation<\/b>:<\/span><\/p>\n Leak Detection Model (FNN):<\/strong> Achieved an F1\u2011score of approximately 88.8%<\/strong> for multi\u2011class leak detection, reliably distinguishing between:<\/p>\n No leak (normal conditions)<\/p>\n<\/li>\n Leak at point 1<\/p>\n<\/li>\n Leak at point 2<\/p>\n<\/li>\n Leaks at both points (1 and 2)<\/p>\n<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<\/li>\n Local Model Inference (ESP32):<\/strong> ~1 second<\/strong> average for sensor data acquisition, feature scaling, AI inference, and leak detection.<\/p>\n<\/li>\n Blynk Notification Response Time:<\/strong> Under 2 seconds<\/strong> from detection, allowing quick alerting and action.<\/p>\n<\/li>\n AWS IoT Message Delivery:<\/strong> Approximately 1\u20132 seconds<\/strong>, providing near\u2011real\u2011time cloud availability for remote monitoring.<\/p>\n<\/li>\n<\/ul>\n<\/li>\n The system provides seamless real\u2011time alerts through the Blynk app<\/strong>, making it easy for PDAM operators and field technicians to respond quickly.<\/p>\n<\/li>\n Alerts and status data are reliably transmitted to AWS IoT<\/strong>, making long\u2011term trend analysis and leak detection possible.<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n This project delivers a smart, reliable, and efficient solution for detecting and locating water leaks in PDAM pipeline networks. By combining real\u2011time data from flow and pressure sensors with a Feedforward Neural Network embedded in an ESP32 microcontroller, the system can accurately identify both the presence and location of leaks. Its seamless integration with AWS IoT and the Blynk platform allows for remote monitoring and instant notifications, making it an invaluable tool for PDAM operators and field technicians. Future enhancements could focus on increasing detection sensitivity through ensemble learning methods, introducing data buffering to maintain reliability during connectivity interruptions, and designing IP\u2011rated enclosures for improved durability in challenging environments.<\/span><\/p>\n Our Git: <\/i><\/strong>https:\/\/github.com\/RifkyHadi7\/water-leak-detection<\/span><\/i><\/a><\/p>\n Our Linkedin :<\/p>\n <\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Smart Water Leak Detection Based on AIoT Smart Water Leak Detection product is an IoT-based smart solution designed to monitor water flow and automatically detect leaks in piping systems. This product combines flow sensors, pressure sensors, and artificial intelligence algorithms to detect flow anomalies that indicate leaks. Equipped with cloud connectivity and a monitoring dashboard,…<\/p>\n","protected":false},"author":349,"featured_media":4550,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_kad_post_transparent":"default","_kad_post_title":"default","_kad_post_layout":"default","_kad_post_sidebar_id":"","_kad_post_content_style":"default","_kad_post_vertical_padding":"default","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[9,1],"tags":[],"class_list":["post-4445","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence-of-thing-aiot","category-capstone"],"_links":{"self":[{"href":"https:\/\/filkom.ub.ac.id\/project\/wp-json\/wp\/v2\/posts\/4445","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/filkom.ub.ac.id\/project\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/filkom.ub.ac.id\/project\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/filkom.ub.ac.id\/project\/wp-json\/wp\/v2\/users\/349"}],"replies":[{"embeddable":true,"href":"https:\/\/filkom.ub.ac.id\/project\/wp-json\/wp\/v2\/comments?post=4445"}],"version-history":[{"count":2,"href":"https:\/\/filkom.ub.ac.id\/project\/wp-json\/wp\/v2\/posts\/4445\/revisions"}],"predecessor-version":[{"id":4789,"href":"https:\/\/filkom.ub.ac.id\/project\/wp-json\/wp\/v2\/posts\/4445\/revisions\/4789"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/filkom.ub.ac.id\/project\/wp-json\/wp\/v2\/media\/4550"}],"wp:attachment":[{"href":"https:\/\/filkom.ub.ac.id\/project\/wp-json\/wp\/v2\/media?parent=4445"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/filkom.ub.ac.id\/project\/wp-json\/wp\/v2\/categories?post=4445"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/filkom.ub.ac.id\/project\/wp-json\/wp\/v2\/tags?post=4445"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"https:\/\/filkom.ub.ac.id\/project\/wp-content\/uploads\/sites\/3\/2025\/06\/WhatsApp-Image-2025-06-20-at-20.19.51.jpeg\")
<\/p>\n\ud83c\udf10 <\/span>Project Domain<\/b>\u00a0<\/span><\/h1>\n
\ud83e\uddd1\u200d\ud83e\udd1d\u200d\ud83e\uddd1 Meet Our Team<\/b><\/h1>\n
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<\/p>\n\u2757 <\/span>Problem Statements<\/b><\/h1>\n
\ud83c\udfaf <\/span>Goals<\/b><\/h1>\n
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\ud83d\udca1 <\/span>Solution Statements<\/b><\/h1>\n
\ud83e\uddf0 <\/span>Prerequisites \u2013 Component Preparation<\/b><\/h1>\n
\u26a1 Hardware<\/strong><\/h4>\n
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\ud83d\udcbb Software & Services<\/strong><\/h4>\n
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\ud83e\udde9 <\/span>Schematic<\/b><\/h1>\n
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<\/p>\n\ud83c\udfac <\/span>Demo<\/b><\/h1>\n
Run water through the pipeline, simulate leak conditions by opening the leak valves, and observe the ESP32 detecting anomalies. The Blynk app displays flow and pressure data in real\u2011time and triggers instant leak alerts when anomalies occur.<\/p>\n\n
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\u2705 <\/span>Conclusion<\/b>\u00a0<\/span><\/h1>\n
\ud83d\udcf2 <\/span>Contact us!<\/b><\/h1>\n
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