Evaluation of Machine Learning Predictions of a Highly Resolved Time Series of Chlorophyll-a Concentration

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.

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization-Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.

Does water temperature influence in microcystin production? A case study of Billings Reservoir, São Paulo, Brazil

We investigated the relationship between some water quality parameters and microcystin, chlorophyll-a, and cyanobacteria in different conditions of water temperature. We also proposed to predict chlorophyll-a concentration in the Billings Reservoir using three machine learning techniques. Our results indicate that in the condition of higher water temperatures with high density of cyanobacteria, microcystin concentration can increase severely (>10² μg/L). Besides the magnitude observed in higher concentrations, in water temperatures above 25.3 °C (classified as high extreme event), higher frequencies of inadequate values of microcystin (87.5%), chlorophyll-a (70%), and cyanobacteria (82.5%) compared to cooler temperatures (<19.6 °C) were observed. The prediction of chlorophyll-a in Billings Reservoir presented good results (0.76 ≤ R² ≤ 0.82; 0.17 ≤ RMSE≤0.20) using water temperature, total phosphorus, and cyanobacteria as predictors, with the best result using Support Vector Machine.

Machine Learning and Multiple Imputation Approach to Predict Chlorophyll-a Concentration in the Coastal Zone of Korea

The concentration of chlorophyll-a (Chl-a) is an integrative bio-indicator of aquatic ecosystems and a direct indicator that evaluates the ecological status of water bodies. In this study, we focused on predicting the Chl-a concentration in seawater using machine learning (after replacing missing values). To replace the missing values among marine environment observation data, a comparison experiment was performed using multiple built-in imputation methods (i.e., pmm, cart, rf, norm, norm.nob, norm.boot, and norm.predict) of the mice package in R. The cart method was selected as the most suitable. We generated each regression model using six machine learning algorithms (regression tree, support vector regression (SVR), bagging, random forest, gradient boosting machine (GBM), and extreme gradient boosting (XGBoost)) to predict the Chl-a concentration based on the complete imputed dataset. The prediction performance of the models was evaluated by four evaluation criteria using 10-fold cross-validation tests. XGBoost, an ensemble learning approach, outperformed other models in predicting the Chl-a concentration; SVR, a single model, also showed a good performance. The most important environmental factor in predicting the Chl-a concentration was an organic carbon particulate; however, dissolved oxygen also showed potential. This study was conducted with field observations in the spring and summer in the coastal zone of Korea. There exists a limit in machine learning applications, which excludes temporal and spatial factors. However, extensions to time series forecasting for deep learning or machine learning can lead to meaningful regional and seasonal analysis. It can also improve prediction performance as a result of the long-term data accumulation of field observations of more varied features (such as meteorological and hydrodynamic) besides water quality.