A service-driven approach to assist water management during extreme events

Water shortages and flooding have caused large property losses and endangered human lives in many areas. Rapid and informed response is needed to ensure effective water management, including reliable and immediate data synthesis, near-real-time forecasting, and model-based decision support for water operations. A structure to rapidly process heterogeneous information and models needed for near-real-time water management is critical for decision makers. This dissertation develops a service-driven approach to decision support in water management, focusing on case studies related to drought and flooding. For flood management, real-time reservoir management is a critical component of decision support. Estimating and predicting reservoir inflows is particularly essential for water managers, given that flood conditions change rapidly. We propose a data-driven framework for real-time reservoir inflow prediction, using a service-oriented approach, that enables ease of access through a Web browser. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics-based model that estimates errors in the flow predictions from a physics-based model. The performances of these two models are compared for short-term prediction. In addition, both the statistical and hybrid models have been published as Web services through Microsoft’s Azure Machine Learning (AzureML) service, and are accessible through a browser-based Web application. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short-term forecasts. However, for longer-term prediction (2 hours or more), the hybrid model fits the observations more closely than the purely statistical or physics-based prediction models alone. The second case study focuses on droughts, developing methods to better manage significant imbalances between water supply and demand. A service-driven approach is used to couple river modeling services with optimization services for determining optimal water allocation strategies under daily drought scenarios in a permit system. An accurate and computationally efficient meta-model approach is then developed to relieve the computational burden of the simulation-optimization model. This work uses a drought event in the Upper Guadalupe River Basin, Texas, in April 2015 as a case study to illustrate the benefits of the approach. Weather and water demand uncertainty are considered through scenario-based optimization. The results have demonstrated that the simulation-optimization model services can easily be coupled using DataWolf workflow tool and AzureML service, providing improved water allocation strategies relative to the current approach. The scenario analysis shows that the permit grouping system, which organizes water right permit holders into groups rather than considers each water user individually, is an easy and manageable approach for water allocation. In addition, the adaptive meta-model approach is efficient to relieve the computational burden in simulation-optimization model, thereby enabling large-scale real-time Web services for decision support

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea