Neural network and genetic programming for modelling coastal algal blooms

2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Large-scale dynamic observation planning for unmanned surface vessels

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007.Includes bibliographical references (p. 129-134).With recent advances in research and technology, autonomous surface vessel capabilities have steadily increased. These autonomous surface vessel technologies enable missions and tasks to be performed without the direction of human operators, and have changed the way scientists and engineers approach problems. Because these robotic devices can work without manned guidance, they can execute missions that are too difficult, dangerous, expensive, or tedious for human operators to attempt. The United States government is currently expanding the use of autonomous surface vessel technologies through the United States Navy's Spartan Scout unmanned surface vessel (USV) and NASA's Ocean-Atmosphere Sensor Integration System (OASIS) USV. These USVs are well-suited to complete monotonous, dangerous, and time-consuming missions. The USVs provide better performance, lower cost, and reduced risk to human life than manned systems. In this thesis, we explore how to plan multiple USV observation schedules for two significant notional observation scenarios, collecting water temperatures ahead of the path of a hurricane, and collecting fluorometer readings to observe and track a harmful algal bloom.(cont.) A control system must be in place that coordinates a fleet of USVs to targets in an efficient manner. We develop three algorithms to solve the unmanned surface vehicle observation-planning problem. A greedy construction heuristic runs fastest, but produces suboptimal plans; a 3-phase algorithm which combines a greedy construction heuristic with an improvement phase and an insertion phase, requires more execution time, but generates significantly better plans; an optimal mixed integer programming algorithm produces optimal plans, but can only solve small problem instances.by John V. Miller.S.M

Numerical modeling of thermal bar and stratification pattern in Lake Ontario using the EFDC model

Thermal bar is an important phenomenon in large, temperate lakes like Lake Ontario. Spring thermal bar formation reduces horizontal mixing, which in turn, inhibits the exchange of nutrients. Evolution of the spring thermal bar through Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers, and lake bathymetry. The simulation is performed from April to July, 2011; on a 2-km grid. The numerical model has been calibrated by specifying: appropriate initial temperature and solar radiation attenuation coefficients. The existing evaporation algorithm in EFDC is updated to modified mass transfer approach to ensure correct simulation of evaporation rate and latent heatflux. Reasonable values for mixing coefficients are specified based on sensitivity analyses. The model simulates overall surface temperature profiles well (RMSEs between 1-2°C). The vertical temperature profiles during the lake mixed phase are captured well (RMSEs < 0.5°C), indicating that the model sufficiently replicates the thermal bar evolution process. An update of vertical mixing coefficients is under investigation to improve the summer thermal stratification pattern. Keywords: Hydrodynamics, Thermal BAR, Lake Ontario, GIS

Environmental management and restoration under unified risk and uncertainty using robustified dynamic Orlicz risk

Environmental management and restoration should be designed such that the risk and uncertainty owing to nonlinear stochastic systems can be successfully addressed. We apply the robustified dynamic Orlicz risk to the modeling and analysis of environmental management and restoration to consider both the risk and uncertainty within a unified theory. We focus on the control of a jump-driven hybrid stochastic system that represents macrophyte dynamics. The dynamic programming equation based on the Orlicz risk is first obtained heuristically, from which the associated Hamilton-Jacobi-Bellman (HJB) equation is derived. In the proposed Orlicz risk, the risk aversion of the decision-maker is represented by a power coefficient that resembles a certainty equivalence, whereas the uncertainty aversion is represented by the Kullback-Leibler divergence, in which the risk and uncertainty are handled consistently and separately. The HJB equation includes a new state-dependent discount factor that arises from the uncertainty aversion, which leads to a unique, nonlinear, and nonlocal term. The link between the proposed and classical stochastic control problems is discussed with a focus on control-dependent discount rates. We propose a finite difference method for computing the HJB equation. Finally, the proposed model is applied to an optimal harvesting problem for macrophytes in a brackish lake that contains both growing and drifting populations

Course Manual Winter School on Structure and Functions of Marine Ecosystem: Fisheries

Marine ecosystems comprises of diverse organisms and their ambient abiotic components in varied relationships leading to an ecosystem functioning. These relationships provides the services that are essential for marine organisms to sustain in the nature. The studies examining the structure and functioning of these relationships remains unclear and hence understanding and modelling of the ecological functioning is imperative in the context of the threats different ecosystem components are facing. The relationship between marine population and their environment is complex and is subjected to fluctuations which affects the bottom level of an ecosystem pyramid to higher trophic levels. Understanding the energy flow within the marine ecosystems with the help of primary to secondary producers and secondary consumers are potentially important when assessing such states and changes in these environments. Many of the physiological changes are known to affect the key functional group, ie. the species or group of organisms, which play an important role in the health of the ecosystem. In marine environment, phytoplankton are the main functional forms which serves as the base of marine food web. Any change in the phytoplankton community structure may lead to alteration in the composition, size and structure of the entire ecosystem. Hence, it is critical to understand how these effects may scale up to population, communities, and entire marine ecosystem. Such changes are difficult to predict, particularly when more than one trophic level is affected. The identification and quantification of indicators of changes in ecosystem functioning and the knowledge base generated will provide a suitable way of bridging issues related to a specific ecosystem. New and meaningful indicators, derived from our current understanding of marine ecosystem functioning, can be used for assessing the impact of these changes and can be used as an aid in promoting responsible fisheries in marine ecosystems. Phytoplantkon is an indicator determining the colour of open Ocean. In recent years, new technologies have emerged which involves multidisciplinary activities including biogeochemistry and its dynamics affecting higher trophic levels including fishery. The winter school proposed will provide the insights into background required for such an approach involving teaching the theory, practical, analysis and interpretation techniques in understanding the structure and functioning of marine ecosystems from ground truth measurements as well as from satellite remote sensing data. This is organized with the full funding support from Indian council of Agricultural Research (ICAR) New Delhi and the 25 participants who are attending this programme has been selected after scrutiny of their applications based on their bio-data. The participants are from different States across Indian subcontinent covering north, east, west and south. They are serving as academicians such as Professors/ scientists and in similar posts. The training will be a feather in their career and will enable them to do their academic programmes in a better manner. Selected participants will be scrutinized initially to understand their knowledge level and classes will be oriented based on this. In addition, all of them will be provided with an e-manual based on the classes. All selected participants are provided with their travel and accommodation grants. The faculty include the scientists who developed this technology, those who are practicing it and few user groups who do their research in related areas. The programme is coordinated by the Fishery Resources Assessment Division of CMFRI. This programme will generate a team of elite academicians who can contribute to sustainable management of marine ecosystem and they will further contribute to capacity building in the sector by training many more interested researchers in the years to come

Multiscale metabolic modeling of C4 plants: connecting nonlinear genome-scale models to leaf-scale metabolism in developing maize leaves

C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, here the predicted fluxes achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems. We suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data.Comment: 57 pages, 14 figures; submitted to PLoS Computational Biology; source code available at http://github.com/ebogart/fluxtools and http://github.com/ebogart/multiscale_c4_sourc

Investigating summer thermal stratification in Lake Ontario

Summer thermal stratification in Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). Summer temperature differences establish strong vertical density gradients (thermocline) between the epilimnion and hypolimnion. Capturing the stratification and thermocline formation has been a challenge in modeling Great Lakes. Deviating from EFDC's original Mellor-Yamada (1982) vertical mixing scheme, we have implemented an unidimensional vertical model that uses different eddy diffusivity formulations above and below the thermocline (Vincon-Leite, 1991; Vincon-Leite et al., 2014). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers; and lake bathymetry is interpolated on a 2-km grid. The model has 20 vertical layers following sigma vertical coordinates. Sensitivity of the model to vertical layers' spacing is thoroughly investigated. The model has been calibrated for appropriate solar radiation coefficients and horizontal mixing coefficients. Overall the new implemented diffusivity algorithm shows some successes in capturing the thermal stratification with RMSE values between 2-3°C. Calibration of vertical mixing coefficients is under investigation to capture the improved thermal stratification

A GIS modelling approach to assess lake eutrophication

Large proportion of the world’s readily available water supply is at risk due to the rapidly increasing populations of certain types of harmful algae. During the photosynthesis, species like blue-green algae and cyanobacteria consume nutrients and produce toxins that have potential adverse effects to humans and animals. This thesis focuses on developing a GIS-based statistical approach to explore the water quality parameters facilitating the algae bloom, and to geographically map the extent and spread of these parameters to enable tracking and prediction of potential algae outbreaks. The relationship between Chlorophyll-a, which represents the concentration of algae biomass, and the water quality parameters such as depth, phosphorus, nitrogen, alkalinity, suspended solids, pH, temperature, electrical conductivity, dissolved oxygen and secchi depth is analyzed though correlation matrix then by utilizing modeling techniques including multiple linear, nonlinear regression, neural network and data mining prediction models are developed to quantify the contribution from essential water quality parameters to eutrophication. The developed GIS and statistical analysis approaches have been applied to the Lake Champlain. The performance for the developed statistical, neural network and data mining chlorophyll-a models has been examined through the comparison with the observed field data and through statistical error analysis. Two new techniques have been examined in this thesis study. First, data mining has helped to reveal the nonlinear behavior of algae growth in some parts of the case study area. Second, the GIS spatial analysis is employed to visualize the spread and extent of the water quality parameters and the algae chlorophyll-a, which graphically present the location-based impact of eutrophication on important lake water resources. For example, the analysis of the GIS-based impact maps suggests that the algae is affecting the Vermont section of Lake Champlain mainly the Northern and Southern section. The developed models suggest that algae production is affected by nutrients particularly phosphorus. When phosphorus is encountered at low to mild concentrations, the nutrient is linearly affecting algae production, however, at extreme concentrations of the nutrient the relationship between nutrient and algae production become nonlinear. The developed GIS model along with the statistical analysis applied on lake Champlain suggest that Extreme levels of Nitrogen in north and Chloride in the South caused deviations in the models prediction accurac