A service for global water level and river flow data based on pervasive sensor networks

基于普适传感器网络的全球水位和河流流量数据服务

• 批准号: NE/S009051/1
• 负责人: Wouter Buytaert
• 金额: $ 1.33万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FS009051%2F1
• 关键词: service; global; water; level; river

Floods and droughts are among the most damaging and disruptive natural hazards worldwide. The development of adaptation and mitigation strategies such as flood forecasting systems relies on accurate observations of water level and river flow. However, the world's rivers are still very poorly monitored and existing monitoring networks are often very sparse in space and time. This is a major bottleneck for water resources management and risk reduction.We have developed a "smart" sensor network that consists of a combination of novel, low-cost sensor technologies and computer algorithms to manage the network. With this network we can monitor and derive various river flow parameters in a way that is far more cost-effective, robust, flexible, and environmentally friendly than existing methods.We have applied our technologies around the world and in a wide variety of applications, including groundwater well monitoring in Somalia, evaluating green infrastructure for water security in Peru, and supporting community-based flood early warning in Nepal. These applications have allowed us to test and refine the performance and robustness of the technology in real-world conditions, and has also exposed the technology to potential end-users. This has generated a broad interest, including various specific requests to purchase the sensors and related technologies.Based on this experience, we see an opportunity to commercialize our technology by building an innovative global service for application-tailored water level and river flow data and analytics. Before applying for an Innovation Follow-On award, we intend to implement a set of activities to formalize our end-user engagement and analyse the potential market. This will allow us to understand end-user needs, and to identify an appropriate business model for the service. Through the Imperial College MBA Connect programme, we have teamed up with the Imperial Business School student Henry Spethmann. Before embarking on MBA studies at Imperial College, Henry has been involved in several technology start-ups in Europe and the United States. As a result, he has extensive expertise on market analysis and business model development, which is highly complementary to the technological and scientific expertise of Dr Buytaert's group.We intend to use the Pathfinder funding to contract Henry to develop a strategy for end-user engagement that will provide us with the necessary input in the development of the business model. Through our previous interactions and experience, we identified a set of potential clients (see section on "Beneficiaries" for more details). However, their requirements are diverse and suggest different potential business models, ranging from selling sensors to providing a set of services related to installation, operation, and data analysis. Therefore, we aim to conduct interviews with potential customers to understand the business context in which they operate, e.g. current needs, most difficult areas of operation, how much monitoring is already in place, and proportion of total budget allocated for hydrological data generation. This information will allow us to identify one or more suitable business models, as a first step towards the development of a business plan.In conjunction to the end-user interaction and business model development, we also aim to explore the intellectual property context, implement a risk analysis, and identify relevant competitors.

洪水和干旱是全世界最具破坏性和破坏性的自然灾害之一。洪水预报系统等适应和缓解战略的制定依赖于对水位和河流流量的准确观测。然而，世界河流的监测仍然很差，现有的监测网络在空间和时间上往往非常稀疏。这是水资源管理和降低风险的主要瓶颈。我们开发了一种“智能”传感器网络，该网络由新颖的低成本传感器技术和计算机算法相结合来管理网络。通过该网络，我们能够以远比现有方法更具成本效益、更强大、更灵活和更环保的方式监测和获取各种河流流量参数。我们已将我们的技术应用于世界各地的各种应用中，包括索马里的地下水井监测、秘鲁的水安全绿色基础设施评估以及尼泊尔基于社区的洪水预警支持。这些应用程序使我们能够在现实条件下测试和改进该技术的性能和鲁棒性，并将该技术展示给潜在的最终用户。这引起了广泛的兴趣，包括购买传感器和相关技术的各种具体要求。基于这些经验，我们看到了通过构建针对应用定制的水位和河流流量数据和分析的创新全球服务，将我们的技术商业化的机会。在申请创新后续奖之前，我们打算实施一系列活动，以正式确定我们的最终用户参与度并分析潜在市场。这将使我们能够了解最终用户的需求，并为服务确定适当的商业模式。通过帝国理工学院MBA Connect项目，我们与帝国商学院学生亨利·斯佩思曼合作。在帝国理工学院攻读MBA之前，亨利曾参与欧洲和美国的几家科技初创企业。因此，他在市场分析和商业模式开发方面拥有广泛的专业知识，这与Buytaert博士团队的技术和科学专业知识具有很强的互补性。我们打算利用开拓者基金与亨利签订合同，制定一项最终用户参与战略，为我们的商业模式开发提供必要的投入。通过我们之前的互动和经验，我们确定了一系列潜在客户（有关更多详细信息，请参阅“受益人”部分）。然而，他们的需求是多样化的，并提出了不同的潜在商业模式，从销售传感器到提供一系列与安装，操作和数据分析相关的服务。因此，我们的目标是与潜在客户进行访谈，以了解他们运营的业务背景，例如当前的需求，最困难的运营领域，已有多少监测以及用于水文数据生成的总预算比例。这些信息将使我们能够识别一种或多种合适的商业模式，作为制定商业计划的第一步。结合最终用户互动和商业模式开发，我们还旨在探索知识产权背景，实施风险分析，并识别相关竞争对手。

项目成果

A Technical Evaluation of Lidar-Based Measurement of River Water Levels

• DOI: 10.1029/2019wr026810
• 发表时间: 2020-04-01
• 期刊: WATER RESOURCES RESEARCH
• 影响因子: 5.4
• 作者: Paul, Jonathan D.;Buytaert, Wouter;Sah, Neeraj
• 通讯作者: Sah, Neeraj