Combination of artificial neural network and clustering techniques for predicting phytoplankton biomass of Lake Poyang, China

Lee

et al. 2019

Water

An excessive increase in algae often has various undesirable effects on drinking water supply systems, thus proper management is necessary. Algal monitoring and classification is one of the fundamental steps in the management of algal blooms. Conventional microscopic methods have been most widely used for algal classification, but such approaches are time-consuming and labor-intensive. Thus, the development of alternative methods for rapid, but reliable algal classification is essential where an advanced machine learning technique, known as deep learning, is considered to provide a possible approach for rapid algal classification. In recent years, one of the deep learning techniques, namely the convolutional neural network (CNN), has been increasingly used for image classification in various fields, including algal classification. However, previous studies on algal classification have used CNNs that were arbitrarily chosen, and did not explore possible CNNs fitting algal image data. In this paper, neural architecture search (NAS), an automatic approach for the design of artificial neural networks (ANN), is used to find a best CNN model for the classification of eight algal genera in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis sp., Oscillatoria sp., and Anabaena sp.), three diatoms (Fragilaria sp., Synedra sp., and two green algae (Staurastrum sp. and Pediastrum sp.). The developed CNN model effectively classified the algal genus with an F1-score of 0.95 for the eight genera. The results indicate that the CNN models developed from NAS can outperform conventional CNN development approaches, and would be an effective tool for rapid operational responses to algal bloom events. In addition, we introduce a generic framework that provides a guideline for the development of the machine learning models for algal image analysis. Finally, we present the experimental results from the real-world environments using the framework and NAS.

Section: Introductionmentioning

confidence: 99%

Algal Morphological Identification in Watersheds for Drinking Water Supply Using Neural Architecture Search for Convolutional Neural Network

Lee

et al. 2019

Water

“…Thus, the recent development of these high technologies regarding field monitoring, data transmission, and analyses promotes the optimization of water quality management. However, many parts of water quality monitoring systems still rely on regular-basis manual sample collection and monitoring even though the collected data are analyzed by novel machine learning techniques [16,134,137,140]. Thus, it is essential to develop and apply in situ real-time monitoring systems using sensor technologies, along with high-tech data analysis techniques, such as deep learning, to find better solutions in water quality management.…”

Section: Discussionmentioning

confidence: 99%

“…The class of machine learning models, examples of application cases, and water quality parameters measured at various time intervals are summarized in Table 3. Artificial neural networks (ANNs) are one of the most conventional and widely used machine learning techniques for water quality management [133][134][135][136]. The conventional ANN structure consists of input, hidden, and output layers; the hidden layer contains two or more layers of nodes [128,137].…”

Section: Advanced Data Analysis With Machine Learning For Water Qualimentioning

confidence: 99%

Recent Advances in Information and Communications Technology (ICT) and Sensor Technology for Monitoring Water Quality

Kim

Lee

2020

Water

Water quality control and management in water resources are important for providing clean and safe water to the public. Due to their large area, collection, analysis, and management of a large amount of water quality data are essential. Water quality data are collected mainly by manual field sampling, and recently real-time sensor monitoring has been increasingly applied for efficient data collection. However, real-time sensor monitoring still relies on only a few parameters, such as water level, velocity, temperature, conductivity, dissolved oxygen (DO), and pH. Although advanced sensing technologies, such as hyperspectral images (HSI), have been used for the areal monitoring of algal bloom, other water quality sensors for organic compounds, phosphorus (P), and nitrogen (N) still need to be further developed and improved for field applications. The utilization of information and communications technology (ICT) with sensor technology shows great potential for the monitoring, transmission, and management of field water-quality data and thus for developing effective water quality management. This paper presents a review of the recent advances in ICT and field applicable sensor technology for monitoring water quality, mainly focusing on water resources, such as rivers and lakes, and discusses the challenges and future directions.

Geo-spatial Information Science

“…However, no unified model exists for SPM retrieval in Poyang Lake; when referring to chlorophyll, very few studies were focused on CHL retrieval, the empirical model developed by Feng et al (2015) could not be used in highly turbid area in northern Poyang Lake. Huang, Gao, and Zhang (2015) combined artificial neural network and clustering techniques to predict CHL separately, as no simple and common model has yet been achieved. Since the results from these studies are limited to exploring remote sensing-based methods for retrieving the water quality parameters of this huge and dynamic lake system, this study aims to calibrate and validate the retrieval models for estimating the concentrations of CHL (C CHL ), SPM (C SPM ), and DOC (C DOC ) with the in situ hyperspectral measurements in Poyang Lake, which may lay foundations for retrieval models selection in the airborne or satellite hyperspectral image-based water quality parameter estimations.…”

Section: Open Accessmentioning

confidence: 99%

Developing water quality retrieval models with in situ hyperspectral data in Poyang Lake, China

Chen

Xiao

2016

Applying remote sensing techniques to develop the retrieval models and further to obtain the spatiotemporal information of water quality parameters is necessary for understanding, managing, and protecting lake ecosystems. This study aimed to calibrate and validate the retrieval models for estimating the concentrations of chlorophyll a (C CHL), suspended particulate matter (C SPM), and dissolved organic carbon (C DOC) with the in situ hyperspectral measurements in Poyang Lake, China in 2010 and 2011. The model calibration and validation results indicated that: (1) for C CHL retrieval, significantly strong and moderate correlations existed between the measured and estimated values (with the correlation coefficient r = 0.92 and r = 0.76) using the exponential model and the three-band model, respectively, with biased estimation observed for the exponential model; (2) for retrieving C SPM , there was a strong correlation between the measured and estimated values (r = 0.95) using the exponential model; and (3) no significant correlation between measured and estimated C DOC values was found with our developed models. More work is needed to allow the water quality of Poyang Lake to be accurately and steadily estimated, especially for C CHL and C DOC .