Hydrolink 2020/4. Artificial intelligent

Topic: Artificial Intelligenc

Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system

HIGH BIOMAS BLOOMS CAUSING FISH KILLS AND OTHER ENVIRONMENTAL IMPACTS

Harmful algal blooms (HABs) have significant impacts on food safety and security through contamination or mass mortalities of aquatic organisms. Indeed, if not properly controlled, aquatic products contaminated with HAB biotoxins are responsible for potentially deadly foodborne diseases and when rapidly growing, HAB consequences include reduced dissolved oxygen in the ocean, dead zones, and mass mortalities of aquatic organisms. Improving HAB forecasting is an opportunity to develop early warning systems for HAB events such as food contamination, mass mortalities, or foodborne diseases. Surveillance systems have been developed to monitor HABs in many countries; however, the lead-time or the type of data (i.e. identification at the Species-level, determination of toxicity) may not be sufficient to take effective action for food safety management measures or other reasons, such as transfer of aquaculture products to other areas. Having early warning systems could help mitigate the impact of HABs and reduce the occurrence of HAB events. In this regard, FAO took the lead in the development of a Joint FAO-IOC-IAEA Technical Guidance for the Implementation of Early Warning Systems for HABs. The document will guide competent authorities and relevant institutions involved in consumer protection or environmental monitoring to implement early warning systems for HABs present in their areas (marine and brackish waters), specifically for those affecting food safety or food security (benthic HABs, fish-killing HABs, pelagic toxic HABs, and cyanobacteria HABs)

Assessing and Forecasting Chlorophyll Abundances in Minnesota Lake using Remote Sensing and Statistical Approaches

Harmful algae blooms (HABs) can negatively impact water quality, lake aesthetics, and can harm human and animal health. However, monitoring for HABs is rare in Minnesota. Detecting blooms which can vary spatially and may only be present briefly is challenging, so expanding monitoring in Minnesota would require the use of new and cost efficient technologies. Unmanned aerial vehicles (UAVs) were used for bloom mapping using RGB and near-infrared imagery. Real time monitoring was conducted in Bass Lake, in Faribault County, MN using trail cameras. Time series forecasting was conducted with high frequency chlorophyll-a data from a water quality sonde. Normalized Difference Vegetation Index (NDVI) was generally well correlated to chlorophyll-a measured by a sonde (R2 = 0.678 for all data from 5 flights, between 0.323-0.986 for individual flights), while Visible Water Residence Index (VWRI) showed a weaker and less consistent correlation with chlorophyll-a (R2 = 0.027 for all data from 5 flights, between 0.17-0.866 for individual flights). While RGB cameras (trail cameras or UAVs) were useful for visual inspection and spotting blooms, these results suggest that quantitative remote sensing of chlorophyll in Minnesota Lakes should use near-infrared cameras at a minimum. Univariate time series forecasts using sonde chlorophyll-a data were compared using classical (ARIMA, wavelet-ARIMA) and machine learning techniques (LSTM, wavelet-LSTM). Chlorophyll-a was positively correlated to temperature and precipitation, while negatively correlated to conductivity and turbidity. Peak summer chlorophyll concentrations also appeared to be positively correlated to recent precipitation totals. 10-day chlorophyll-a forecasts using univariate LSTM and ARIMA outperformed a multivariate forecast (using conductivity, turbidity, temperature, and precipitation as predictors), suggesting that lower cost monitoring setups (a single chlorophyll probe) may be practical. To assist in understanding meteorological factors impacting interannual variability of blooms in Bass Lake, the relationship between peak summer chlorophyll-a (from Sentinel-2 satellite imagery) and temperature and precipitation were analyzed at Bass Lake. The impact of meteorological factors on patterns in chlorophyll-a for lakes in the Western Corn Belt Plains (WCBP) was also examined, using Sentinel-2 imagery (imagery was available for 160 lakes in the WCBP during 2019 and 2020). Peak summer Chlorophyll-a (from Sentinel-2 imagery) at Bass Lake was positively correlated to 2-week precipitation totals, suggesting a potential role of precipitation induced nutrient loading in initiating blooms; a negative correlation between peak chlorophyll-a and 60-day precipitation totals also suggested that increased residence time during drier periods may be a driving factor as well. While a slight negative correlation between precipitation and peak summer chlorophyll-a was present in a larger scale analysis of 160 WCBP lakes, too many confounding factors were present to show the impact of precipitation on blooms at a broader scale in Minnesota

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

The role of ecological groups in the formation of cyanobacterial communities in the ecosystems of the North Azov region (Ukraine)

The role of Cyanoprokaryota ecological groups in the ecosystems of the North Azov region was revealed in this work. On the territory of Pryazovskyi National Nature Park, 9 experimental polygons were studied, which covered steppe areas or slopes, salt marshes, coastal sandy soils and water bodies (rivers, lakes, estuaries, sea bays, lagoons). As a result of research on the territory of Pryazovskyi National Nature Park, 124 species of cyanoprokaryotes were identified, which include 127 intraspecific taxa. It was proved that the procedure of canonical correspondence analysis is the most suitable for the analysis of the species matrix. The axes identified as a result of the ordination procedure, which indicate the coordinated dynamics of the species, correlated with both synecological characteristics, such as diversity indicators, and with autoecological characteristics, such as ecotypes of cyanoprokaryotes in relation to habitat types or types of adaptation to salinity conditions. The first four canonical axes together explain 47.5% of species matrix variability. Canonical axis 1 explains 18.0% of the variability of the species matrix and is mostly marked by aqual subaerophytes and eurybionts. This axis indicates the presence of a gradient of salinity conditions where the most saline conditions correspond to the positive values of the axis, and the negative values correspond to less saline. Canonical axis 2 describes 12.1% of species matrix variability. This axis differentiates aquatic ecosystems from others. Canonical axis 3 explains 10.0% of the communities’ variability. This axis distinguishes freshwater ecosystems from saline ecosystems. Markers of freshwater communities are stenotopic halotolerants, which are narrow-range, common mainly in the temperate zone of Europe. The canonical axis 4 explains 7.3% of variability of the matrix of species and is able to differentiate sand ecosystems. The ecotopic structure and geographic range width of community species have the greatest independent value among the considered sources of variation. The independent role of adaptation to the salinity conditions of the ecotope and the role of the type of ecosystems is somewhat smaller. The interaction between the sources of variation is important in the variation of the structure of communities. The interaction between the ecotopic structure and the geographic range width of species and the triple interaction between the ecotopic structure of a community, the width of the geographic range of species and the ecosystem type plays the greatest role in the variation of community structure. Ecotopic groups, which indicate the preference of a particular habitat, correlate with the species composition of the communities. It is shown that the ratio of ecototopic groups in a community is a characteristic that reveals the features of the community as a whole

Annual report 2007 - North Pacific Marine Science Organization (PICES). Sixteenth meeting, Victoria, Canada, October 26-November 5, 2007

Report of Opening Session (p. 1). Report of Governing Council (p. 15). Report of the Finance and Administration Committee (p. 65). Reports of Science Board and Committees: Science Board Inter-Sessional Meeting (p. 83); Science Board (p. 93); Biological Oceanography Committee (p. 105); Fishery Science Committee (p. 117); Marine Environmental Quality Committee (p. 129); Physical Oceanography and Climate Committee (p. 139); Technical Committee on Data Exchange (p. 145); Technical Committee on Monitoring (p. 153). Reports of Sections, Working and Study Groups: Section on Carbon and Climate (p. 161); Section on Ecology of Harmful Algal Blooms in the North Pacific (p. 167); Working Group 19 on Ecosystem-based Management Science and its Application to the North Pacific (p. 173); Working Group 20 on Evaluations of Climate Change Projections (p. 179); Working Group 21 on Non-indigenous Aquatic Species (p. 183); Study Group to Develop a Strategy for GOOS (p. 193); Study Group on Ecosystem Status Reporting (p. 203); Study Group on Marine Aquaculture and Ranching in the PICES Region (p. 213); Study Group on Scientific Cooperation between PICES and Non-member Countries (p. 225). Reports of the Climate Change and Carrying Capacity Program: Implementation Panel on the CCCC Program (p. 229); CFAME Task Team (p. 235); MODEL Task Team (p. 241). Reports of Advisory Panels: Advisory Panel for a CREAMS/PICES Program in East Asian Marginal Seas (p. 249); Advisory Panel on Continuous Plankton Recorder Survey in the North Pacific (p. 253); Advisory Panel on Iron Fertilization Experiment in the Subarctic Pacific Ocean (p. 255); Advisory Panel on Marine Birds and Mammals (p. 261); Advisory Panel on Micronekton Sampling Inter-calibration Experiment (p. 265). 2007 Review of PICES Publication Program (p. 269). Guidelines for PICES Temporary Expert Groups (p. 297). Summary of Scientific Sessions and Workshops (p. 313). Report of the ICES/PICES Conference for Early Career Scientists (p. 355). Membership (p. 367). Participants (p. 387). PICES Acronyms (p. 413). Acronyms (p. 415)