Using convolutional neural network for predicting cyanobacteria concentrations in river water

Pyo, Juncheol; Park, Lan Joo; Pachepsky, Yakov; Baek, Sang-Soo; Kim, Kyunghyun; Cho, Kyung Hwa

doi:10.1016/j.watres.2020.116349

Cited by 75 publications

(15 citation statements)

References 40 publications

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“…(v) When images are available at a high temporal frequency, CNN's applicability to prediction tasks can be extended [58] e limitation of CNN has been underlined, due to the prerequisite of the high amount of data [46]. It may be a waste of time to interpret the data processing in a given large watershed area.…”

Section: Advantages Of Convolutional Neural Networkmentioning

confidence: 99%

Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones

Tegegne

2022

Journal of Engineering

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In the field of groundwater engineering, a convolutional neural network (CNN) has become a great role to assess the spatial groundwater potentiality zones and land use/land cover changes based on remote sensing (RS) technology. CNN can be offering a great potential to extract complex spatial features with multiple high levels of generalization. However, geometric distortion and fuzzy entity boundaries as well as a huge data preparation severance may be the main constraint and affect the spatial potential of CNN application for land cover classification. This study aims to recognize the proficiency of deep learning algorithms, i.e., CNN, for spatial assessment of groundwater potential zones and land cover. Among the groundwater influencing factors, classification of land cover (agriculture, built-up, water bodies, forests, and bare land) has been reported by several researchers for different purposes and they approved the CNN capability for the prediction of spatial groundwater potentiality zones like very high, high, moderate, poor, and very poor areas. In this study, CNN is recommended as a very essential algorithm for the identification of groundwater potential zones and classification of land use/land cover change. CNN gives a better option for scholars regarding when the limited data sets are available for validation.

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Section: Advantages Of Convolutional Neural Networkmentioning

confidence: 99%

Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones

Tegegne

2022

Journal of Engineering

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“…A convolutional neural network (CNN) has been proposed to exploit the feature hierarchies of multi-dimensional data from low to high levels. (LeCun and Bengio, 1998;Pyo et al, 2020). Compared to a standard neural network, this method can more easily train the model by requiring fewer parameters and connections between elements (Krizhevsky et al, 2012).…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Developing a deep learning model for the simulation of micro-pollutants in a watershed

Yun

Abbas

Jeon

et al. 2021

Journal of Cleaner Production

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In recent years, as agricultural activities and types of crops have become diverse, the occurrence of micro-pollutants has been reported more frequently in rural areas. These pollutants have detrimental effects on human health and ecological systems; thus, it is important to manage and monitor their presence in the environment. The modeling approach could be an effective way to understand and manage these pollutants. This study predicts the concentrations of micro-pollutants (MPs) using deep learning (DL) models, and the results are then compared with simulation results obtained from the soil water assessment tool (SWAT) model. The SWAT model showed an unacceptable performance owing to the resulting negative NasheSutcliffe efficiency (NSE) values for the simulations. This may be caused by the limitations of SWAT, which pertains to adopting simplified equations to simulate micro-pollutants. In addition, the ambiguous plan of pesticide application increased the model uncertainty, thereby deteriorating the model result. Here, we developed two different DL models: long short-term memory (LSTM) and convolutional neural network (CNN). LSTM exhibited the highest model performance, with NSE values of 0.99 and 0.75 for the training and validation steps, respectively. In the multi-target MP model, the error decreased as the number of simulated pollutants increased. The simulation of the four pollutants had the highest error, while the six-target simulation had the lowest error. In conclusion, this study demonstrated that the LSTM model has the potential to improve the prediction of MPs in aquatic systems.

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“…Shen et al (2019) estimated cyanobacteria blooms in river waters using a support vector machine. Additionally, Pyo et al (2020) simulated algal blooms in freshwater systems using a convolutional neural network (CNN). However, these studies focused on HABs in inland waters, which further access is necessary to undergo more dynamic and complex hydrological and ecological cycles in seawater.…”

Section: Introductionmentioning

confidence: 99%

“…Prior studies have reported a decline in model performance with increasing lead time Chattopadhyay et al (2020). reported a decrease in the model performance from 73 to 47% while predicting a cold-spell class as the lead time changed from 1 to 5 days Pyo et al (2020). observed the validation accuracy to decrease with increasing lead time when simulating Microcystis-a causative algal taxon of freshwater HAB.…”

mentioning

confidence: 97%

Deep Learning for Simulating Harmful Algal Blooms Using Ocean Numerical Model

et al. 2021

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In several countries, the public health and fishery industries have suffered from harmful algal blooms (HABs) that have escalated to become a global issue. Though computational modeling offers an effective means to understand and mitigate the adverse effects of HABs, it is challenging to design models that adequately reflect the complexity of HAB dynamics. This paper presents a method involving the application of deep learning to an ocean model for simulating blooms of Alexandrium catenella. The classification and regression convolutional neural network (CNN) models are used for simulating the blooms. The classification CNN determines the bloom initiation while the regression CNN estimates the bloom density. GoogleNet and Resnet 101 are identified as the best structures for the classification and regression CNNs, respectively. The corresponding accuracy and root means square error values are determined as 96.8% and 1.20 [log(cells L–1)], respectively. The results obtained in this study reveal the simulated distribution to follow the Alexandrium catenella bloom. Moreover, Grad-CAM identifies that the salinity and temperature contributed to the initiation of the bloom whereas NH4-N influenced the growth of the bloom.

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Using convolutional neural network for predicting cyanobacteria concentrations in river water

Cited by 75 publications

References 40 publications

Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones

Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones

Developing a deep learning model for the simulation of micro-pollutants in a watershed

Deep Learning for Simulating Harmful Algal Blooms Using Ocean Numerical Model

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