Sub-monthly time scale forecasting of harmful algal blooms intensity in Lake Erie using remote sensing and machine learning

Lake Erie summer chlorophyll phenology: a Bayesian additive regression trees comparison of growth and decay phases

Synergistic effects of abiotic and biotic factors determine chlorophyll, a proxy for algal biomass in lakes. With harmful algal blooms (HABs) increasingly threatening water quality, it is important to determine the extent to which lake abiotic conditions contribute to chlorophyll that generally exhibits a growth phase leading to a summer peak followed by a decay phase. Here an ensemble-tree model implemented in the Bayesian additive regression trees (BART) was used to investigate the effects of seven potential drivers on chlorophyll concentrations during both growth and decay phases in western Lake Erie in the years 2012-2022. Our findings revealed that total phosphorus (TP) consistently emerged as the dominant driver, exhibiting a positive saturating relationship. During growth, interactions between TP and nitrogen forms dominated, while beam attenuation emerged as the central interacting variable during decay phase. The TP-chlorophyll relationship was similar between the growth and decay phases of the bloom. Overall, while the TP-chlorophyll relationship is well established in freshwater lakes, the fact that TP emerges as the most important factor in an exploration that includes other nutrients, temperature, and light underscores the idea that management strategies focused on phosphorus control should be effective in reducing HABs in Lake Erie.

Comparing the performance of 10 machine learning models in predicting Chlorophyll a in western Lake Erie

Algal blooms, which have substantial adverse effects, are increasingly occurring worldwide in the context of global warming and eutrophication. Machine learning models (MLMs) are emerging as efficient and promising tools for predicting algal blooms. However, the performance of MLMs in directly simulating algal blooms has seldom been reported, particularly in the world's largest freshwater system, the Great Lakes. To address this gap, we compared the prediction performance of Chlorophyll a (Chl a, a proxy for algal biomass) concentration in western Lake Erie among 10 popular MLMs using 15 measured water quality data collected from 2012 to 2022. Results have shown that outlier removal is essential, as it can noticeably improve prediction accuracy such as increasing the coefficient of determination (R2) from 0.35 to 0.84 (140 %) for the optimal Gradient Boosting Decision Trees (GBDT) model. All 32,767 feature combinations of measured water quality parameters were exhaustively tested for each MLM and the best feature combinations are identified. MLMs benefit from this feature selection, with the Polynomial Regression model showing notable improvements: the R2 increased from 0.71 to 0.82 (15 %) compared to no feature selection. The tree-based ensemble models, including the GBDT (R2 = 0.84) and Random Forest (R2 = 0.82) models, show the top two performances in predicting Chl a. Based on feature importance analysis, particulate organic nitrogen (PON) is determined to be the most critical water quality parameter for predicting Chl a. These results establish a benchmark for the performance of common MLMs in predicting Chl a in western Lake Erie. The determined best feature combinations potentially make water quality observations more effective and targeted, thereby benefiting sustainable water quality management.

A framework for developing a real-time lake phytoplankton forecasting system to support water quality management in the face of global change

Phytoplankton blooms create harmful toxins, scums, and taste and odor compounds and thus pose a major risk to drinking water safety. Climate and land use change are increasing the frequency and severity of blooms, motivating the development of new approaches for preemptive, rather than reactive, water management. While several real-time phytoplankton forecasts have been developed to date, none are both automated and quantify uncertainty in their predictions, which is critical for manager use. In response to this need, we outline a framework for developing the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty, thereby enabling managers to adapt operations and mitigate blooms. Implementation of this system calls for new, integrated ecosystem and statistical models; automated cyberinfrastructure; effective decision support tools; and training for forecasters and decision makers. We provide a research agenda for the creation of this system, as well as recommendations for developing real-time phytoplankton forecasts to support management.

Early Detection Methods for Toxic Cyanobacteria Blooms

Harmful cyanobacterial blooms produce cyanotoxins which can adversely affect humans and animals. Without proper monitoring and detection programs, tragedies such as the loss of pets or worse are possible. Multiple factors including rising temperatures and human influence contribute to the increased likelihood of harmful cyanobacteria blooms. Current approaches to monitoring cyanobacteria and their toxins include microscopic methods, immunoassays, liquid chromatography coupled with mass spectrometry (LCMS), molecular methods such as qPCR, satellite monitoring, and, more recently, machine learning models. This review highlights current research into early detection methods for harmful cyanobacterial blooms and the pros and cons of these methods.

Mapping agricultural tile drainage in the US Midwest using explainable random forest machine learning and satellite imagery

There has been an increase in tile drained area across the US Midwest and other regions worldwide due to agricultural expansion, intensification, and climate variability. Despite this growth, spatially explicit tile drainage maps remain scarce, which limits the accuracy of hydrologic modeling and implementation of nutrient reduction strategies. Here, we developed a machine-learning model to provide a Spatially Explicit Estimate of Tile Drainage (SEETileDrain) across the US Midwest in 2017 at a 30-m resolution. This model used 31 satellite-derived and environmental features after removing less important and highly correlated features. It was trained with 60,938 tile and non-tile ground truth points within the Google Earth Engine cloud-computing platform. We also used multiple feature importance metrics and Accumulated Local Effects to interpret the machine learning model. The results show that our model achieved good accuracy, with 96 % of points classified correctly and an F1 score of 0.90. When tile drainage area is aggregated to the county scale, it agreed well (r2 = 0.69) with the reported area from the Ag Census. We found that Land Surface Temperature (LST) along with climate- and soil-related features were the most important factors for classification. The top-ranked feature is the median summer nighttime LST, followed by median summer soil moisture percent. This study demonstrates the potential of applying satellite remote sensing to map spatially explicit agricultural tile drainage across large regions. The results should be useful for land use change monitoring and hydrologic and nutrient models, including those designed to achieve cost-effective agricultural water and nutrient management strategies. The algorithms developed here should also be applicable for other remote sensing mapping applications.

Advancing Solar Power Forecasting: Integrating Boosting Cascade Forest and Multi-Class-Grained Scanning for Enhanced Precision

Accurate solar power generation forecasting is paramount for optimizing renewable energy systems and ensuring sustainability in our evolving energy landscape. This study introduces a pioneering approach that synergistically integrates Boosting Cascade Forest and multi-class-grained scanning techniques to enhance the precision of solar farm power output predictions significantly. While Boosting Cascade Forest excels in capturing intricate, nonlinear variable interactions through ensemble decision tree learning, multi-class-grained scanning reveals fine-grained patterns within time-series data. Evaluation with real-world solar farm data demonstrates exceptional performance, reflected in low error metrics (mean absolute error, 0.0016; root mean square error 0.0036) and an impressive R-squared score of 99.6% on testing data. This research represents the inaugural application of these advanced techniques to solar generation forecasting, highlighting their potential to revolutionize renewable energy integration, streamline maintenance, and reduce costs. Opportunities for further refinement of ensemble models and exploration of probabilistic forecasting methods are also discussed, underscoring the significance of this work in advancing solar forecasting techniques for a sustainable energy future.

Recent advances in algal bloom detection and prediction technology using machine learning

Harmful algal blooms (HAB) including red tides and cyanobacteria are a significant environmental issue that can have harmful effects on aquatic ecosystems and human health. Traditional methods of detecting and managing algal blooms have been limited by their reliance on manual observation and analysis, which can be time-consuming and costly. Recent advances in machine learning (ML) technology have shown promise in improving the accuracy and efficiency of algal bloom detection and prediction. This paper provides an overview of the latest developments in using ML for algal bloom detection and prediction using various water quality parameters and environmental factors. First, we introduced ML for algal bloom prediction using regression and classification models. Then we explored image-based ML for algae detection by utilizing satellite images, surveillance cameras, and microscopic images. This study also highlights several real-world examples of successful implementation of ML for algal bloom detection and prediction. These examples show how ML can enhance the accuracy and efficiency of detecting and predicting algal blooms, contributing to the protection of aquatic ecosystems and human health. The study also outlines recent efforts to enhance the field applicability of ML models and suggests future research directions. A recent interest in explainable artificial intelligence (XAI) was discussed in an effort to understand the most influencing environmental factors on algal blooms. XAI facilitates interpretations of ML model results, thereby enhancing the models' usability for decision-making in field management and improving their overall applicability in real-world settings. We also emphasize the significance of obtaining high-quality, field-representative data to enhance the efficiency of ML applications. The effectiveness of ML models in detecting and predicting algal blooms can be improved through management strategies for data quality, such as pre-treating missing data and integrating diverse datasets into a unified database. Overall, this paper presents a comprehensive review of the latest advancements in managing algal blooms using ML technology and proposes future research directions to enhance the utilization of ML techniques.

Daily process and key characteristics of phytoplankton bloom during a low-water level period in a large subtropical reservoir bay

Reservoirs, heavily influenced by artificial management, often harbor phytoplankton assemblages dominated by cyanobacteria or dinoflagellates, triggering significant changes in aquatic ecosystems. However, due to limited sampling frequency and insufficient attention to species composition, the bloom processes and key characteristics of phytoplankton community structure have not been systematically elucidated. During the low-water level period when blooms are most likely to occur (June to September) in a tributary bay of the Three Gorges Reservoir, daily sampling was conducted to investigate phytoplankton community composition, identify significant environmental factors, and evaluate important structure characteristics of phytoplankton community. The results showed that Microcystis aeruginosa maintained a clear dominance for almost a month in stage 1, with low Shannon and evenness but a high dominance index. Phytoplankton total density and biomass decreased drastically in stage 2, but Microcystis aeruginosa still accounted for some proportion. The highest Shannon and evenness but the lowest dominance index occurred in stage 3. Peridiniopsis niei occurred massively in stage 4, but its dominant advantages lasted only one to two days. NH4-N was responsible for the dominance of Microcystis aeruginosa, while TP and PO4-P was responsible for the dominance of Peridiniopsis niei; however, precipitation contributed to their drastic decrease or disappearance to some extent. The TN : TP ratio could be considered as an important indicator to determine whether Microcystis aeruginosa or Peridiniopsis niei dominated the phytoplankton community. Throughout the study period, physiochemical factors explained more variation in phytoplankton data than meteorological and hydrological factors. Pairwise comparisons revealed an increase in average β diversity with stage progression, with higher β diversities based on abundance data than those based on presence/absence data. Repl had a greater effect on β diversity differences based on presence/absence data, whereas RichDiff had a greater effect on β diversity differences based on species abundance data. Co-occurrence networks for stage 1 showed the most complex structure, followed by stage 4, while the network for stage 3 was relatively sparse, although the overall community division remained compact. This study provides a useful attempt to explore the status and changes in phytoplankton community structure during the bloom process through high-resolution investigation.

Coupling harmful algae derived nitrogen and sulfur co-doped carbon nanosheets with CeO2 to enhance the photocatalytic degradation of isothiazolinone biocide

Removing the algae from water bodies is an effective treatment toward the worldwide frequently occurred harmful algae blooms (HAB), but processing the salvaged algae waste without secondary pollution places another burden on the economy and environment. Herein, a green hydrothermal process without any chemical addition was developed to resource the HAB algae (Microcystis sp.) into autogenous nitrogen and sulfur co-doped carbon nanosheet materials C-CNS and W-CNS, whose alga precursors were collected from pure culture and a wild bloom pond, respectively. After coupling with CeO2, the obtained optimal C-CNS/CeO2 and W-CNS/CeO2 composites photocatalytically degraded 95.4% and 88.2% of the marine pollutant 4,5-Dichloro-2-n-octyl-4-isothiazolin-3-one (DCOIT) in 90 min, significantly higher than that of pure CeO2 (63.15%). DCOIT degradation on CNS/CeO2 was further conducted under different conditions, including pH value, coexisting cations and anions, and artificial seawater. Although different influences were observed, the removal efficiencies were all above 76%. Along with the ascertained good stability and reusability in five consecutive runs, the great potential of CNS/CeO2 for practical application was validated. UV-vis DRS showed the increased light absorption of CNS/CeO2 in comparison to pure CeO2. PL spectra and photoelectrochemical measurements suggested the lowered charge transfer resistance and thereby inhibited charge recombination of CNS/CeO2. Meanwhile, trapping experiments and electron paramagnetic resonance (EPR) detection verified the primary roles of hydroxyl radical (OH) and superoxide radical (O2-) in DCOIT degradation, as well as their notably augmented generation by CNS. Consequently, a mechanism of CNS enhanced photocatalytic degradation of DCOIT was proposed. The intermediates involved in the reaction were identified by LC-QTOF-MS, giving rise to a deduced degradation pathway for DCOIT. This study offers a new approach for resourceful utilization of the notorious HAB algae waste. Besides that, photocatalytic degradation has been explored as an effective measure to remove DCOIT from the ocean.

Algae development in rivers with artificially constructed weirs: Dominant influence of discharge over temperature

Algal blooms contribute to water quality degradation, unpleasant odors, taste issues, and the presence of harmful substances in artificially constructed weirs. Mitigating these adverse effects through effective algal bloom management requires identifying the contributing factors and predicting algal concentrations. This study focused on the upstream region of the Seungchon Weir in Korea, which is characterized by elevated levels of total nitrogen and phosphorus due to a significant influx of water from a sewage treatment plant. We employed four distinct machine learning models to predict chlorophyll-a (Chl-a) concentrations and identified the influential variables linked to local algal bloom events. The gradient boosting model enabled an in-depth exploration of the intricate relationships between algal occurrence and water quality parameters, enabling accurate identification of the causal factors. The models identified the discharge flow rate (D-Flow) and water temperature as the primary determinants of Chl-a levels, with feature importance values of 0.236 and 0.212, respectively. Enhanced model precision was achieved by utilizing daily average D-Flow values, with model accuracy and significance of the D-Flow amplifying as the temporal span of daily averaging increased. Elevated Chl-a concentrations correlated with diminished D-Flow and temperature, highlighting the pivotal role of D-Flow in regulating Chl-a concentration. This trend can be attributed to the constrained discharge of the Seungchon Weir during winter. Calculating the requisite D-Flow to maintain a desirable Chl-a concentration of up to 20 mg/m3 across varying temperatures revealed an escalating demand for D-Flow with rising temperatures. Specific D-Flow ranges, corresponding to each season and temperature condition, were identified as particularly influential on Chl-a concentration. Thus, optimizing Chl-a reduction can be achieved by strategically increasing D-Flow within these specified ranges for each season and temperature variation. This study highlights the importance of maintaining sufficient D-Flow levels to mitigate algal proliferation within river systems featuring weirs.