Prediction model of algal blooms using logistic regression and confusion matrix

Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis

Harmful and toxic algae

The chapter provides basic facts about harmful and toxic algae. It also discusses the conditions that stimulate their occurrence, different types of harmful and toxic algal blooms and their effects to fish and marine environment. The different strategies in coping with the problem of harmful and toxic algal blooms are also discussed

Image processing for smarter browsing of ocean color data products: investigating algal blooms

Remote sensing technology continues to play a significant role in the understanding of our environment and the investigation of the Earth. Ocean color is the water hue due to the presence of tiny plants containing the pigment chlorophyll, sediments, and colored dissolved organic material and so can provide valuable information on coastal ecosystems. We propose to make the browsing of Ocean Color data more efficient for users by using image processing techniques to extract useful information which can be accessible through browser searching. Image processing is applied to chlorophyll and sea surface temperature images. The automatic image processing of the visual level 1 and level 2 data allow us to investigate the occurrence of algal blooms. Images with colors in a certain range (red, orange etc.) are used to address possible algal blooms and allow us to examine the seasonal variation of algal blooms in Europe (around Ireland and in the Baltic Sea). Yearly seasonal variation of algal blooms in Europe based on image processing for smarting browsing of Ocean Color are presented

Cyanobacterial Harmful Algal Blooms: Chapter 1: An Overview of the Interagency, International Symposium on Cyanobacterial Harmful Algal Blooms (ISOC-HAB): Advancing the Scientific Understanding of Freshwater Harmful Algal Blooms

There is growing evidence that the spatial and temporal incidence of harmful algal blooms is increasing, posing potential risks to human health and ecosystem sustainability. Currently there are no US Federal guidelines, Water Quality Criteria and Standards, or regulations concerning the management of harmful algal blooms. Algal blooms in freshwater are predominantly cyanobacteria, some of which produce highly potent cyanotoxins. The US Congress mandated a Scientific Assessment of Freshwater Harmful Algal Blooms in the 2004 reauthorization of the Harmful Algal Blooms and Hypoxia Research and Control Act. To further the scientific understanding of freshwater harmful algal blooms, the US Environmental Protection Agency (EPA) established an interagency committee to organize the Interagency, International Symposium on Cyanobacterial Harmful Algal Blooms (ISOC-HAB). A theoretical framework to define scientific issues and a systems approach to implement the assessment and management of cyanobacterial harmful algal blooms were developed as organizing themes for the symposium. Seven major topic areas and 23 subtopics were addressed in Workgroups and platform sessions during the symposium. The primary charge given to platform presenters was to describe the state of the science in the subtopic areas, whereas the Workgroups were charged with identifying research that could be accomplished in the short- and long-term to reduce scientific uncertainties. The proceedings of the symposium, published in this monograph, are intended to inform policy determinations and the mandated Scientific Assessment by describing the scientific knowledge and areas of uncertainty concerning freshwater harmful algal blooms

Methods for Mapping Algal Blooms: Do They Produce Similar Results?

Algal blooms occur when there is an overabundance of algae in a freshwater or saltwater body. Algal blooms often have negative effects on human health, the environment, and the economy. They increase during summer months due to heightened water temperatures. With the climate warming gradually, the occurrence of algal blooms will likely increase. Mapping algal blooms using geospatial data and analysis methods is incredibly important to understanding where algal blooms happen and how they have increased over time. In my research project, I use geospatial data to map an algal bloom in Lake St. Clair, Michigan. My data originate from the satellite Landsat 8 and were collected on July 14, 2019. I use the Blue Normalized Difference Vegetation Index (BNDVI) and the Surface Algal Bloom Index (SABI) for my analysis of the data. I combine each of these, as well as the original data, with a supervised classification. The purpose is to determine whether similar results can be derived from each of these methods

Phytoplankton Community and Algal Toxicity at a Recurring Bloom in Sullivan Bay, Kabetogama Lake, Minnesota, USA

Kabetogama Lake in Voyageurs National Park, Minnesota, USA suffers from recurring late summer algal blooms that often contain toxin-producing cyanobacteria. Previous research identified the toxin microcystin in blooms, but we wanted to better understand how the algal and cyanobacterial community changed throughout an open water season and how changes in community structure were related to toxin production. Therefore, we sampled one recurring bloom location throughout the entire open water season. The uniqueness of this study is the absence of urban and agricultural nutrient sources, the remote location, and the collection of samples before any visible blooms were present. Through quantitative polymerase chain reaction (qPCR), we discovered that toxin-forming cyanobacteria were present before visible blooms and toxins not previously detected in this region (anatoxin-a and saxitoxin) were present, indicating that sampling for additional toxins and sampling earlier in the season may be necessary to assess ecosystems and human health risk

A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading

Algal blooms often occur in the tidal freshwater (TF) of the James River estuary, a tributary of the Chesapeake Bay. The timing of algal blooms correlates highly to a summer low-flow period when residence time is long and nutrients are available. Because of complex interactions between physical transport and algal dynamics, it is challenging to predict interannual variations of bloom correctly using a complex eutrophication model without having ahigh-resolution model gridto resolve complexgeometryand anaccurate estimate of nutrientloading to drive the model. In this study, an approach using long-term observational data (from 1990 to 2013) and the Support vector machine (LS-SVM) for simulating algal blooms was applied. The Empirical Orthogonal Function was used to reduce the data dimension that enables the algal bloom dynamics for the entire TF to be modeled by one model. The model results indicate that the data-driven model is capable of simulating interannual algal blooms with good predictive skills and is capable of forecasting algal blooms responding to the change of nutrient loadings and environmental conditions. This study provides a link between a conceptual model and a dynamic model, and demonstrates that the data-driven model is a good approach for simulating algal blooms in this complex environment of the James River. The method is very efficient and can be applied to other estuaries as wel

An Analysis of Environmental Conditions Impacting Cyanobacterial Algal Blooms in Drinking Water Sources in Upstate South Carolina

Maintaining water quality in reservoirs used for drinking water has been an issue in recent years due to the presence of algal blooms. Algal blooms are a perennially recurring problem that can have negative impacts on tourism, recreation, and overall water quality. Additionally, algal blooms will often produce an assortment of chemicals, some of which are hazardous to the health of humans, and some of which that, while relatively innocuous, result in unpleasant tastes and odors in water. Geosmin and 2-methylisoborneol are two taste and odor compounds that are notoriously difficult to treat out of drinking water sources by traditional methods like flocculation and screening. It is important to establish the time frame that odor causing algal blooms occur, and to determine the environmental conditions that drive excessive algal growth. Four freshwater lakes in Upstate South Carolina, Lake Whelchel, Lake Bowen, Lake Greenwood, and Lake Rabon, have had problems with cyanobacterial taste and odor compound causing blooms in recent years. Subsequently the goal of this research was to 1) establish any seasonal peaks in algal growth 2) establish any relationships between total algal growth and the presence of cyanobacteria 3) determine what, if any, environmental conditions influenced the growth of said blooms in each lake. The fourth and final project objective was to develop an ultraperformance liquid chromatography method for determining total geosmin and 2-methylisoborneol in environmental samples. While phosphorus is typically identified as the limiting nutrient in algal bloom growth, it was determined that Nitrogen, specifically ammonium and nitrates, were the primary drivers of algal blooms in sites with substantial algal growth, providing insight into how potentially harmful blooms can be managed in these drinking water sources in the future