Drinking Water Infrastructure Assessment with Teleconnection Signals, Satellite Data Fusion and Mining

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  author =       "Sanaz Imen",
  title =        "Drinking Water Infrastructure Assessment with
                 Teleconnection Signals, Satellite Data Fusion and
  school =       "Civil Engineering, University of Central Florida",
  year =         "2015",
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, Water quality, water quantity, remote
                 sensing, data fusion, nowcasting, forecasting, lake
  URL =          "http://purl.fcla.edu/fcla/etd/40-Sanaz_Imen-Dissertation-After_Changes_From_Final_Format_Check.pdf",
  URL =          "http://purl.fcla.edu/fcla/etd/CFE0005632",
  size =         "162 pages",
  abstract =     "Adjustment of the drinking water treatment process as
                 a simultaneous response to climate variations and water
                 quality impact has been a grand challenge in water
                 resource management in recent years. This desired and
                 preferred capability depends on timely and quantitative
                 knowledge to monitor the quality and availability of
                 water. This issue is of great importance for the
                 largest reservoir in the United States, Lake Mead,
                 which is located in the proximity of a big metropolitan
                 region - Las Vegas, Nevada. The water quality in Lake
                 Mead is impaired by forest fires, soil erosion, and
                 land use changes in nearby watersheds and waste water
                 effluents from the Las Vegas Wash. In addition, more
                 than a decade of drought has caused a sharp drop by
                 about 100 feet in the elevation of Lake Mead. These
                 hydrological processes in the drought event led to the
                 increased concentration of total organic carbon (TOC)
                 and total suspended solids (TSS) in the lake. TOC in
                 surface water is known as a precursor of disinfection
                 by-products in drinking water, and high TSS
                 concentration in source water is a threat leading to
                 possible clogging in the water treatment process. Since
                 Lake Mead is a principal source of drinking water for
                 over 25 million people, high concentrations of TOC and
                 TSS may have a potential health impact. Therefore, it
                 is crucial to develop an early warning system which is
                 able to support rapid forecasting of water quality and
                 availability. In this study, the creation of the
                 nowcasting water quality model with satellite remote
                 sensing technologies lays down the foundation for
                 monitoring TSS and TOC, on a near real-time basis. Yet
                 the novelty of this study lies in the development of a
                 forecasting model to predict TOC and TSS values with
                 the aid of remote sensing technologies on a daily
                 basis. The forecasting process is aided by an iterative
                 scheme via updating the daily satellite imagery in
                 concert with retrieving the long-term memory from the
                 past states with the aid of non-linear autoregressive
                 neural network with external input on a rolling basis
                 onward. To account for the potential impact of
                 long-term hydrological droughts, telecommunication
                 signals were included on a seasonal basis in the Upper
                 Colorado River basin which provides 97percent of the
                 inflow into Lake Mead. Identification of teleconnection
                 patterns at a local scale is challenging, largely due
                 to the coexistence of non-stationary and non-linear
                 signals embedded within the ocean-atmosphere system.
                 Empirical mode decomposition as well as wavelet
                 analysis are used to extract the intrinsic trend and
                 the dominant oscillation of the sea surface temperature
                 (SST) and precipitation time series. After finding
                 possible associations between the dominant oscillation
                 of seasonal precipitation and global SST through lagged
                 correlation analysis, the statistically significant
                 index regions in the oceans are extracted. With these
                 characterized associations, individual contribution of
                 these SST forcing regions that are linked to the
                 related precipitation responses are further quantified
                 through the use of the extreme learning machine.
                 Results indicate that the non-leading SST regions also
                 contribute saliently to the terrestrial precipitation
                 variability compared to some of the known leading SST
                 regions and confirm the capability of predicting the
                 hydrological drought events one season ahead of time.
                 With such an integrated advancement, an early warning
                 system can be constructed to bridge the current gap in
                 source water monitoring for water supply.",
  notes =        "Public - Allow Worldwide Access

                 Supervisor: Ni-bin Chang",

Genetic Programming entries for Sanaz Imen