Long short-term memory models of water quality in inland water environments.

Water quality is substantially influenced by a multitude of dynamic and interrelated variables, including climate conditions, landuse and seasonal changes. Deep learning models have demonstrated predictive power of water quality due to the superior ability to automatically learn complex patterns and relationships from variables. Long short-term memory (LSTM), one of deep learning models for water quality prediction, is a type of recurrent neural network that can account for longer-term traits of time-dependent data. It is the most widely applied network used to predict the time series of water quality variables. First, we reviewed applications of a standalone LSTM and discussed its calculation time, prediction accuracy, and good robustness with process-driven numerical models and the other machine learning. This review was expanded into the LSTM model with data pre-processing techniques, including the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise method and Synchrosqueezed Wavelet Transform. The review then focused on the coupling of LSTM with a convolutional neural network, attention network, and transfer learning. The coupled networks demonstrated their performance over the standalone LSTM model. We also emphasized the influence of the static variables in the model and used the transformation method on the dataset. Outlook and further challenges were addressed. The outlook for research and application of LSTM in hydrology concludes the review.

Effect of hyperspectral image-based initial conditions on improving short-term algal simulation of hydrodynamic and water quality models.

Hydrodynamic and water quality modeling have provided valuable simulation results that have enhanced the understanding of the spatial and temporal distribution of algal blooms. Typical model simulations are performed with point-based observational data that are used to configure initial and boundary conditions, and for parameter calibration. However, the application of such conventional modeling approaches is limited due to cost, labor, and time constraints that preclude the retrieval of high-resolution spatial data. Thus, the present study applied fine-resolution algal data to configure the initial conditions of a hydrodynamic and water quality model and compared the accuracy of short-term algal simulations with the results simulated using conventional point-based initial conditions. The environmental fluid dynamics code (EFDC) model was calibrated to simulate Chlorophyll-a (Chl-a) concentrations. Hyperspectral images were used to generate Chl-a maps based on a two-band ratio algorithm for configuring the initial condition of the EFDC model. The model simulation with hyperspectral-based initial conditions returned relatively accurate results for Chl-a, compared to the simulation based on point-based initial conditions. The simulations exhibited percent bias values of 9.93 and 14.23, respectively. Therefore, the results of this study demonstrate how hyperspectral-based initial conditions could improve the reliability of short-term algal bloom simulations in a hydrodynamic model.

Identification of influencing factors of A. catenella bloom using machine learning and numerical simulation.

Alexandrium catenella (A. catenella) is a notorious algal species known to cause paralytic shellfish poisoning (PSP) in Korean coastal waters. There have been numerous studies on its temporal and spatial blooms in Korea. However, its bloom dynamics have not been fully understood because of the complexity in physical, chemical, and biological environments. This study aims to identify the factors that influence A. catenella blooms by applying a numerical model and machine learning. Intensive monitoring of A. catenella was conducted to investigate temporal variations in its population and its spatial distribution in the area with frequent occurrences of PSP bloom initiation. Moreover, a numerical model was built to analyze the ocean physical factors related to the bloom of A. catenella. Based on the information obtained from the monitored and simulated results, the decision tree (DT) method was applied to identify factors that caused the bloom. The outbreak of A. catenella was observed in the eastern coastal water of Geoje Island in 2017, recording a peak density of 4 × 104 (cell L-1). Retention time and particle scattering demonstrated that the physical force in 2017 was weaker than that in 2018, as shown by the smaller effects of advection and dispersion in 2017. The decision tree model showed that (1) water temperature below 17.21 °C was ideal for the growth of A. catenella, (2) phosphate influenced the growth of the species, and (3) cell density was accelerated with increasing retention time. The results from DT can contribute to the prediction of A. catenella blooms by determining the conditions that cause bloom initiation. Further, they can be used as a practical approach for mitigating HABs. Thus, machine learning and numerical simulation in this study can be a potential approach for effectively managing the bloom of A. catenella.

Designing a marine outfall to reduce microbial risk on a recreational beach: Field experiment and modeling.

A marine outfall can be a wastewater management system that discharges sewage and stormwater into the sea; hence, it is a source of microbial pollution on recreational beaches, including antibiotic resistant genes (ARGs), which lead to an increase in untreatable diseases. In this regard, a marine outfall must be efficiently located to mitigate these risks. This study aimed to 1) investigate the spatiotemporal variability of Escherichia coli (E. coli) and ARGs on a recreational beach and 2) design marine outfalls to reduce microbial risks. For this purpose, E. coli and ARGs with influential environmental variables were intensively monitored on Gwangalli beach, South Korea in this study. Environmental fluid dynamic code (EFDC) was used and calibrated using the monitoring data, and 12 outfall extension scenarios were explored (6 locations at 2 depths). The results revealed that repositioning the marine outfall can significantly reduce the concentrations of E. coli and ARGs on the beach by 46-99%. Offshore extended outfalls at the bottom of the sea reduced concentrations of E. coli and ARGs on the beach more effectively than onshore outfalls at the sea surface. These findings could be helpful in establishing microbial pollution management plans at recreational beaches in the future.

Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil.

Heavy metal contamination in soil disturbs the chemical, biological, and physical soil conditions and adversely affects the health of living organisms. Visible and near-infrared spectroscopy (VNIRS) shows a potential feasibility for estimating heavy metal elements in soil. Moreover, deep learning models have been shown to successfully deal with complex multi-dimensional and multivariate nonlinear data. Thus, this study implemented a deep learning method on reflectance spectra of soil samples to estimate heavy metal concentrations. A convolutional neural network (CNN) was adopted to estimate arsenic (As), copper (Cu), and lead (Pb) concentrations using measured soil reflectance. In addition, a convolutional autoencoder was utilized as a joint method with the CNN for dimensionality reduction of the reflectance spectra. Furthermore, artificial neural network (ANN) and random forest regression (RFR) models were built for heavy metal estimation. Principal component analysis was utilized for dimensionality reduction of the ANN and RFR models. Among these models, the CNN model with convolutional autoencoder showed the highest accuracies for As, Cu, and Pb estimates, having R2 values of 0.86, 0.74, and 0.82, respectively. The convolutional autoencoder disentangled the relevant features of multiple heavy metal elements and delivered robust features to the CNN model, thereby generating relatively accurate estimates.

Comparative Evaluation of Six Traditional Fermented Soybean Products in East Asia: A Metabolomics Approach.

Many ethnic fermented soybean products (FSPs) have long been consumed as seasoning and protein sources in East Asia. To evaluate the quality of various FSPs in East Asia, non-targeted metabolite profiling with multivariate analysis of six traditional FSPs (Natto; NT, Cheonggukjang; CG, Doenjang; DJ, Miso; MS, Doubanjiang; DB, Tianmianjiang; TM) was performed. Six FSPs could be clearly distinguished by principle component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Amino acid contents were relatively higher in NT and CG, sugar and sugar alcohol contents were relatively higher in MS and TM, isoflavone glycoside contents were relatively highest in CG, isoflavone aglycon contents were the highest in DJ, and soyasaponin contents were the highest in CG. Antioxidant activity and physicochemical properties were determined to examine the relationships between the FSPs and their antioxidant activities. We observed a negative correlation between isoflavone aglycon contents and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) activity. Furthermore, the order of ABTS activity of FSPs has a positive correlation with the order of soybean content in the six FSPs. Herein it was found that primary metabolites were affected by the main ingredients and secondary metabolites were most influenced by the fermentation time, and that soybean content contributed more to antioxidant activity than fermentation time.

Untargeted and targeted metabolomics analyses of blackberries - Understanding postharvest red drupelet disorder.

Red drupelet is a postharvest disorder of blackberries with several drupelets turning back to red. This affects visual quality and thus marketability and consumers' acceptance. However, the cause of this disorder as well as metabolite changes during color reversion have not been fully understood. Anthocyanins, cyanidin 3-glucoside, cyanidin 3-malonylglucoside, cyanidin 3-dioxalylglucoside, and total anthocyanin, were significantly lower in red drupelets than in black drupelets after 7 days of storage. Sugars and organic acids, lipids, and free amino acids also changed with storage and by color reversion. The untargeted metabolomics analyses indicated that red drupelets were generally differentiated from berries at harvest or black drupelets at metabolite level. The results of this study help better understand the red drupelet disorder. To our knowledge, this is the first study investigating red drupelet disorder by comparing black and red drupelets at metabolite level.

Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea.

Understanding harmful algal blooms is imperative to protect aquatic ecosystems and human health. This study describes the spatial and temporal distributions of cyanobacterial blooms to identify the relations between blooms and environmental factors in the Baekje Reservoir. Two-year cyanobacterial cell data at one fixed station and four remotely sensed distributions of phycocyanin (PC) concentrations based on hyperspectral images (HSIs) were used to describe the relation between the spatial and temporal variations in the blooms and the affecting factors. An artificial neural network model and a three-dimensional hydrodynamic model were implemented to estimate the PC concentrations using remotely sensed HSIs and simulate the hydrodynamics, respectively. The statistical test results showed that the variations in the cyanobacterial biomass depended significantly on variations in the water temperature (slope = 0.13, p-value < 0.01), total nitrogen (slope = -0.487, p-value < 0.01), and total phosphorus (slope = 20.7, p-value < 0.05), whereas the variation in the biomass was moderately dependent on the variation in the outflow (slope = -0.0097, p-value = 0.065). Water temperature was the main factor affecting variations in the PC concentrations for the three months from August to October and was significantly different for the three months (p-value < 0.01). Hydrodynamic parameters also had a partial effect on the variations in the PC concentrations in those three months. Overall, this study helps to describe spatial and temporal variations in cyanobacterial blooms and identify the factors affecting the variation in the blooms. This study may play an important role as a basis for developing strategies to reduce bloom frequency and severity.