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.

洪水和干旱是全球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策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，该网络结合了新颖、低成本的传感器技术和计算机算法来管理网络。通过该网络，我们可以以比现有方法更具成本效益、稳健、灵活和环保的方式监测和推导各种河流流量参数。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、评估秘鲁水安全的绿色基础设施以及支持尼泊尔的社区洪水预警。这些应用程序使我们能够在现实条件下测试和完善该技术的性能和稳健性，并向潜在的最终用户展示该技术。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。根据这一经验，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来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来正式确定我们的最终用户参与并分析潜在市场。这将使我们能够了解最终用户的需求，并为该服务确定适当的业务模型。通过帝国理工学院 MBA Connect 项目，我们与帝国商学院学生 Henry Spethmann 合作。在开始帝国理工学院 MBA 学习之前，Henry 曾参与欧洲和美国的多家科技初创企业。因此，他在市场分析和商业模式开发方面拥有丰富的专业知识，这与 Buytaert 博士团队的技术和科学专业知识高度互补。我们打算利用探路者资金与 Henry 签约，制定最终用户参与战略，这将为我们在商业模式的开发中提供必要的投入。通过我们之前的互动和经验，我们确定了一组潜在客户（有关更多详细信息，请参阅“受益人”部分）。然而，他们的需求多种多样，并提出了不同的潜在商业模式，从销售传感器到提供与安装、操作和数据分析相关的一系列服务。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环境，例如。当前的需求、最困难的操作领域、已经到位的监测程度以及分配给水文数据生成的总预算比例。这些信息将使我们能够确定一个或多个合适的商业模式，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