Research

Dr. Piasecki's area of research has been on engineering informatics and its application to GeoSciences in particular Hydrology, i.e. HydroInformatics. His interest is in the development of technologies that aid in resolving syntactic and semantic heterogeneities among disparate data sources, a pervasive hurdle and strong impediment when trying to discover scientific data. More specifically he seeks to deploy web technologies such as the use of Ontological Representations of knowledge in addition to standards for metadata and SOA to solve these heterogeneity problems.

A recent addition to his research interests focuses on data mangement for large sensor networks where he is involved in the Trans-African HydroMeteorological Observatory (TAHMO) initiaitve as well as work he is carrying out in the Dominican Republic and Haiti where he also has deployed sensor networks. The development of low cost hydro met sensors and their deployability in large networks with easy-to-use application coupled to a ready made data management is a current focus area.

He also has an interest in the area of Water Resources and the development of Sustainable and Resilient Solutions for its development and restoring ecosystem services. He has been focusing his work on anthropogenic effects such as deforestation in the Caribbean and resulting reduced ececosystem services. He is not only interested in researching the impacts of reforestation on the hydrological cycle but also the connectedness to other dimensions such as economics for rural populations. A related interest is the Sustainable construction of Water Infrastructure (water supply, sanitation, and rainfall/runoff) in developing countries both in non-disaster and post-disaster circumstances using participatory processes.

NSF CSE Directorate, Div of Info and Intelli. Systems

Advances in sensor technology are greatly expanding the range of quantities that can be measured while simultaneously reducing the cost. However, deployed sensors drift out of calibration and fail, so every sensor network requires quality control (QC) procedures to promptly detect these failures. Existing QC methods rely on human experts to carefully examine the data, which means that when the number of sensors in a network doubles, the number of experts must double too. This project will develop algorithms and software to increase the level of automation in sensor QC so that a smaller number of experts can manage a much larger network of sensors. The methods will be tested on weather data from Oklahoma (the Oklahoma Mesonet), Oregon (the Andrews Long-Term Ecological Network site), the US (the Earth Networks "WeatherBug" network), and sub-Saharan Africa (the TAHMO project), and if the methods are found to work well, they will be deployed in these networks at at the CUAHSI Water Data Center. Accurate weather data could significantly increase the productivity of farms and improve food security, particularly in Africa.

The project will develop an open-source standards-compliant system, SENSOR-DX, that implements automated data QC. Existing probabilistic QC methods assume that correct sensor readings are jointly Gaussian and readings from broken sensors obey a uniform distribution. These assumptions lead to many QC mistakes. This project will develop a new approach in which novel nonparametric anomaly detection algorithms analyze the sensor data. Correct sensor readings have low anomaly scores, while broken sensor readings have high scores; both follow parametric distributions. Probabilistic methods can therefore model the distribution of the resulting anomaly scores instead of the joint distribution of the original sensor readings and infer (probabilistically) whether each sensor is working correctly. To enhance the fault-detection capability of the anomaly detection algorithms, the raw sensor data will be detrended and assembled into multiple views that highlight various correlations among sensor values. The project will develop a novel View-Anomaly-Diagnosis (VAD) framework in which anomaly detection algorithms are applied to the tuples in each view, and then the anomaly scores are combined via a probabilistic diagnostic model to infer which sensors are broken and which are functioning correctly. The project will study how good the detrending models need to be in order to enhance the accuracy of anomaly detection.  

The focus of this research is on the national grand challenge known as the "Energy-Water-Climate Nexus." Specifically, this research is on the reliability of electric power sector infrastructure and operations (electric power grids) and climate change adaptation when viewed from the perspective of strategic water issues.This research unites three ongoing efforts that have been addressing the Energy-Water-Climate Nexus: (a) NREL/Sandia national energy-water assessment, (b) NSF EaSM-funded research on integrated assessment/policy engagement work expanded to national scale, and (c ) UCSB strategic energy economic assessment using input-output models. The principal science goal is to create a National Energy-Water System assessment framework (NEWS) to evaluate, in the context of anticipated climate and economic change: (i) the performance of the nation's electricity sector; (ii) the feasibility of alternative pathways to improve climate adaptation; and, (iii) the impacts of energy technology and investment tradeoffs on the productivity of the economy, water availability and aquatic ecosystem condition. NEWS will be applied in a series of scenario studies to 2050 using AR5 RCP/SSP climate realizations to drive: technology assessment models for renewable and non-renewable power production and their water requirements; hydrology and thermal impact simulations; and input/output economic models at national and sub-national levels. Future climate and changing energy demands will test the limits of current energy infrastructure in the context of environmental regulations and national climate adaptation and mitigation policies. New energy technologies, including renewables, hold the promise of off-loading substantial water limitations in the electricity sector, that is, if sufficient knowledge, economic realities and political will are in hand. 

The Enriquillo (DR) and Saumatre (HAiti) are saltwater lakes located in a rift valley that is a former marine strait created around 1 million years ago when water levels fell and the strait was filled in by river sediments. Separated by just a few kilometers at thier closest points these are the largest lakes in the Dominican Republic (DR) and Haiti, respectively, with Lake Enriquillo being the lowest point in the Caribbean. The lakes, part of the Enriquillo closed water basin in the southwestern region of the Caribbean island of Hispaniola, have been experiencing drastic changes in total lake-surface area coverage during the period 1980-2010. The socio-economic impacts of these changing lakes’ levels have been dramatic. Since the lakes began their recent rapid growth, more than 15,000 hectares of agricultural and grass land around the lake were flooded, having a strong impact in 16 communities with total estimates of 10,000 individuals affected. This has prompted recent urgent actions from the government of Dominican Republic to identify causes and determine action plans if applicable. The City College of New York (CCNY) in collaboration with the Instituto Tecnológico de Santo Domingo (INTEC) and Drexel University are leading scientific studies since early 2011 to determine possible causes for the flooding and have embarked now on a more detailed investigation to examine the causes for the lake expansions focusing on the hydrologic aspects.

The Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) has been involved in the development of Water Data Services (WDS) through the CUAHSI Hydrologic Information Systems (HIS) project. The vision for WDS is to bring together the nation’s (and, potentially, the earths’s) water data in a federated system of servers linked using a services-oriented architecture. A critical challenge in achieving this vision is understanding and reconciling structural and semantic differences across publishers of hydrologic data. The HIS project made a breakthrough in achieving interoperability between different data repositories, by developing a prototype ontology of hydrologic concepts that is used for data discovery purposes. Organizing hydrologic concepts in a way that allows publishers to describe their data unambiguously and helps users to discover data simply yet with a precise understanding of the properties measured, is an inherent component of this larger challenge.We propose to extend this effort by harmonizing the system of hydrologic concepts and existing information models with existing federal information sources. This includes the development of a community process for evolving hydrologic ontology , with contributions from the CUAHSI community, its federal partners, and international collaborators. This research is a collaborative effort with Dr. Rick Hooper from the CUAHSI and Drs. Ilya Zaslavsky and David Valentine from the San Diego Supercomputer Center. 

To find out more visit the Project Web-Site at http://www.cbeo.org

This CI-TEAMs Implementation proposal builds the team’s current CI-TEAMs Demonstration project, the objective of which is the creation of a comprehensive, multi-disciplinary approach to teaching engineering modeling. For the CI-TEAM Implementation, the team will use biologically-inspired robotic systems as representative of other complex, emerging engineering domains. The investigators aim to create novel ways in which we train future generations of engineering and computer science students to build physically realized systems for important applications in medicine, civil engineering, search and rescue, and homeland security. CI-TEAM Demonstration results include a program of education around the creation of comprehensive engineering models for biologically-inspired robots. Specifically, we propose to (1) advance a set of educational modules to be the basis for a general educational program in Engineering Informatics and (2) develop and deploy software tools needed to advance the state of the art in bio-inspired robotic systems. This work is a collaborative effort with Dr. Bill Regli (Drexel U.), Dr. Ming Ling (U of NC), Dr. Vadim Shapiro (U. of Wiscosin), and Dr. S.K. Gupta (U of Maryland).

A significant role of HIS is establishing the cyberinfrastructure foundation, or digital environment required to support experimental watersheds, or hydrologic or environmental observatories.  The capability for HIS to do this will be evaluated through testing at hydrologic observatory test bed projects that are being supported by the NSF. The specific goals of this project are to provide better Data Access, to support for Hydrologic Observatories, i.e. to develop a digital watershed framework for synthesizing data and models for a hydrologic region, to advance Hydrologic Science, i.e. to strengthen place-based hydrologic science by supporting the representation of hydrologic processes with equations by an enhanced capacity to describe hydrologic environments quantitatively with data, and to enable Hydrologic Education – education through application of HIS tools to quantify and visualize the movement of water and chemicals in a hydrologic environment continuously in space and time. This effort is a collaboration with Dr. David Maidment (U of Texas, Dr. David Tarboton (Utah State U.), Dr. Jonathan Goodall (U. of South Carolina), and Dr. Ilya Zaslavsky, San Diego Supercomputing Center.

see here for a 2-page write-up for the IM2 system (PDF)