{"product_id":"kit-pi-5-water-network-leak-detection","title":"Pi 5 Water Network Leak Detection","description":"\u003ch1\u003eRaspberry Pi 5 Water Network Leak Detection Kit: Train a GNN to Pinpoint Leaks\u003c\/h1\u003e\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Intermediate\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 5–6 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 15–21\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Graph neural network leak detection\u003c\/span\u003e\n\u003c\/div\u003e\n\u003cp\u003eWater utilities lose up to 40% of water to leaks. This kit gives you everything to build a real-time monitoring system that deploys a graph neural network on Raspberry Pi 5. Four flow sensors and pressure modules feed data into a GNN model that learns pressure differential patterns—then instantly identifies the leaking pipe segment. Perfect for ECE, EEE students and Smart India Hackathon teams moving from theory to a working AI IoT solution.\u003c\/p\u003e\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eA complete IoT pipeline: four YF-S201 flow sensors and BMP280 pressure modules connect to your Raspberry Pi 5 over I2C. High‑speed sensor data streams onto an NVMe SSD via the M.2 HAT+. The trained GNN processes the network graph in real time, and you’ll visualise leak locations on a local dashboard or trigger remote alerts. It’s a real‑world leak detection prototype you can demo, extend, or even deploy in a campus water network simulation.\u003c\/p\u003e\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eDeploy a graph neural network on Raspberry Pi 5 for live sensor data inference\u003c\/li\u003e\n  \u003cli\u003eIntegrate YF-S201 flow sensors and BMP280 pressure sensors over I2C\u003c\/li\u003e\n  \u003cli\u003eBuild a multi‑node pipe network and collect pressure differential training data\u003c\/li\u003e\n  \u003cli\u003eTrain a GNN to classify leak location from pressure signature patterns\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 5 4GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eYF-S201 Flow Sensor\u003c\/td\u003e\n\u003ctd\u003e4\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBMP280\u003c\/td\u003e\n\u003ctd\u003e4\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 128GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi 5 M.2 HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-M Wires\u003c\/td\u003e\n\u003ctd\u003e30\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eIdeal for B.Tech ECE\/EEE students working on Smart India Hackathon or mini‑project demonstrations, CBSE Class 12 students building AI‑powered physics experiments, and makers hacking water conservation solutions at ATL Tinkering Labs. If you’re exploring graph neural networks at IITs, NITs, or VIT, this kit turns theory into a deployable system you can show off.\u003c\/p\u003e\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.\u003c\/p\u003e\n\u003cdetails\u003e\u003csummary\u003eHow does the GNN detect leak location?\u003c\/summary\u003e\u003cp\u003eThe graph neural network learns pressure differential patterns across four measurement nodes. A leak causes a pressure drop that propagates differently depending on its location, and the model maps that to the most likely pipe segment.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need prior Python or AI experience?\u003c\/summary\u003e\u003cp\u003eThis is an intermediate kit — basic Python and Linux familiarity helps, but our AI companion provides line‑by‑line code guidance and model training scripts.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat water network setup is needed?\u003c\/summary\u003e\u003cp\u003eYou can simulate a 4‑branch pipe network with household plastic tubing and a small submersible pump (not included). We provide a network schematic and sensor mounting guide.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I log data to the cloud?\u003c\/summary\u003e\u003cp\u003eYes, the Pi can upload sensor logs to Firebase or AWS IoT. Our AI companion walks you through the integration step by step.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eFlow sensors at 4 network points feed a GNN on Pi 5 that identifies leak location from pressure differential pattern.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-model-b-4gb-technical-specs-projects\"\u003eRaspberry Pi 5 4GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/yf-s201-water-flow-sensor-1-30lmin-hall-effect-compoden-0371\"\u003eYF-S201 Flow Sensor\u003c\/a\u003e x4\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/bmp280-barometric-pressure-temperature-sensor-module-compodenin\"\u003eBMP280\u003c\/a\u003e x4\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/official-raspberry-pi-m2-hat-nvme-ssd-add-on-board-for-pi-5\"\u003eNVMe SSD 128GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eM-M Wires x30\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Pi 5 Water Network Leak Detection?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Water Network Leak Detection includes all components needed: Raspberry Pi 5 4GB, YF-S201 Flow Sensor, BMP280, NVMe SSD 128GB, Pi 5 M.2 HAT+ and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Pi 5 Water Network Leak Detection?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Intermediate level makers, suitable for ages 15-21. Flow sensors at 4 network points feed a GNN on Pi 5 that identifies leak location from pressure differential pattern. Estimated build time is 5-6 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Water Network Leak Detection online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Water Network Leak Detection is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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