Melbourne Water

This week, we hosted an event that brought together industry and government at our Mirabel, Quebec, facility highlighting a next-generation airborne early warning and control (AEW&C) solution designed to support Royal Canadian Air Force | Aviation royale canadienne evolving defense and NORAD modernization priorities. Built on the Canadian-made Bombardier Global 6500 platform, L3Harris Technologies AERIS X solution combines advanced mission systems, allied interoperability, in-country sustainment and long-term industrial growth opportunities for Canada. Learn more: https://lnkd.in/e8yMu8-w

In rainfall–runoff modelling, the most important decision often comes before parameter calibration: choosing the right modelling framework. In my latest video (link in the comments), I compare continuous vs. event-based rainfall–runoff modelling—starting with clear definitions, then breaking down the key characteristics, applications, common challenges, and best practices for each approach. Whether you’re doing urban drainage design, flood forecasting, reservoir yield analysis, drought/low-flow assessment, climate change impact studies, or water quality and sediment modelling, this guide will help you select the approach that matches your application—and avoid the most common pitfalls. #civilengineering #hydrologicalmodeling #continuousmodeling #eventbased #waterresourcesmanagement #climatechange #floodmodeling #designstorm #drainagedesign

Most finance apps help you track. Almost none help you decide. On Founder Mode , Shain Noor shares what changes when AI becomes a thinking partner, not just a dashboard: → Why people trust AI with things they’d never tell an advisor → The shift from “clicking” to actual decision-making → How trust is built through reasoning, not UI → And why the best products don’t act, they guide If it doesn’t change your decisions, it’s just another tool. CHAPTERS 00:00 - The judgment-free financial advisor 02:38 - Introducing Shain Noor and Silvia 03:51 - Why finance apps have always missed the reasoning layer 05:51 - Co-pilot vs. autopilot: trust, transparency, and guardrails 08:29 - What surprised Shain: users sharing what they hide from their advisors 12:42 - Measuring retention and the proactive alerts breakthrough 17:02 - Team size, the ProCap merger, and competing with legacy finance 19:41 - The future: everyone becomes a manager of AI agents

L3Harris Technologies in partnership with Yulista Aviation, Inc. and NASA - National Aeronautics and Space Administration delivered a next-generation research aircraft five weeks ahead of schedule. The upgraded aircraft will serve as NASA's flagship platform for airborne science research. The advanced platform represents a significant capability leap, accelerating scientific discovery and actionable insights for policymakers, emergency responders and communities worldwide. This delivery builds on our decades-long partnership with NASA and reinforces our proven leadership in complex aircraft modification and mission systems integration. Read more here: https://lnkd.in/g_jFSUqP

L3Harris Technologies and Lockheed Martin are partnering to establish F-35 depot capabilities at our MAS facility in Mirabel, Canada. This positions Canada as a key node in the global F-35 sustainment network, strengthening both operational sovereignty and economic growth. By combining L3Harris’ sustainment expertise with Lockheed Martin’s 5th generation leadership, we’re building long-term national readiness. This agreement advances Canada’s role to exercise greater in-country control over Royal Canadian Air Force | Aviation royale canadienne sustainment while creating high-skilled jobs. Read more:

I've helped build technology products used by hundreds of millions of people. The lessons that actually transferred into healthcare weren't about the tech itself. They were about what happens in the first 30 days: When you're building at scale, the product question is almost never "does this work?" It's "will people use it?" At Microsoft, we launched Outlook Mobile with 5 features when competitors had 50. We chose the 5 things users actually needed. Cut everything else. The result: hundreds of millions of users. At Instacart, we deployed AI coding tools across a 200+ person engineering team. Some engineers picked them up on day one. Others never did. The output gap between those two groups was enormous within six months. Now I'm building in healthcare and the same lesson applies. Healthcare operators are rightfully skeptical of new technology. They've been burned by EHR implementations that took 18 months and disrupted everything. By tools that promised transformation and delivered complexity. So when we built Pretty Good AI , we started with one question: "How do we make this impossible to resist using?" The answer: Go live in 30 days and show meaningful results in week one. Day one at a recent clinic: 38% of calls were handled automatically. By day three: over 50%. Staff weren't threatened. They were asking us to give the AI more to do. The tech is the easy part. The adoption is where the leverage is. After 25+ years of building products that actually get used, we know how to get the adoption part right. What's the hardest adoption lesson you've had to learn- where the technology worked but people didn't use it? — Enjoy this? ♻️ Repost it to your network and follow. Weekly frameworks on AI, startups, leadership, and scaling. Join 20,000+ subscribers today: https://gofoundermode.com/

Excited to be presenting at the Floodplain Management Australia 2026 Conference! I’ll be sharing insights from the Greater Melbourne Flood Mapping Program and how it has helped establish a community of practice to deliver flood information. The scale of the program is immense, bringing together councils, industry, and practitioners to improve consistency, collaboration, and outcomes. If you’re working in flood strategy or community engagement around natural hazards, I’d love to connect. Please feel free to come and say hello! #FMA26 #FloodStrategy #FloodMapping #FloodManagement

Evaluating hydrological model performance is not as simple as reporting a single metric. The model performance is inherently multi-dimensional. In my latest video (link in the comments), we take a comprehensive and practical look at performance metrics in hydrological modelling, explaining why relying on just one metric can be misleading. This video reviews 10 commonly used performance metrics (NSE, Log-NSE, R-squared, KGE, RMSE, MAE, PBIAS, VE, PFB, and TPE), and discusses their formulas, interpretation, and limitations. It is followed by a few numerical examples and a list of best practices. This video is essential for hydrologists, water engineers, researchers, and students working in rainfall–runoff modelling, flood analysis, and model calibration. #hydrology #hydrologicalmodeling #modelperformance #bias #performancemetrics #civilengineering #NSE #RMSE #KGE #PBIAS

Understanding how a flood wave changes shape and moves through a river or reservoir is one of the most fundamental skills in hydrology. This is what we call ROUTING. In the past few months, I have created a couple of technical videos on routing, which are now grouped in a new playlist (Link in the comments). This playlist covers the fundamentals of hydrologic and hydraulic routing, provides a few practical tutorials on well-known routing methods such as Muskingum and Level Pool Routing, and reviews the routing options available in commonly used software packages such as SWMM, HEC-HMS, and RORB. This is a helpful body of knowledge for students, engineers, and practitioners who want to learn and practice routing in their projects. #routing #hydrology #hydraulics #hydrologytutorial #civilengineering #floodmodeling