Deep neural networks are a class of powerful machine learning model that uses successive layers of non-linear processing units to extract features from data However, the training process of such networks is quite computationally intensive and uses commonly used optimization methods that do not guarantee optimum performance Furthermore, deep learning methods are often sensitive to noise in data and do not operate well in areas where data are incomplete An alternative, yet little explored, method in enhancing deep learning performance is the use of fuzzy systems Fuzzy systems have been previously used in conjunction with neural networks This survey explores the different ways in which deep learning is improved with fuzzy logic systems The techniques are classified based on how the two paradigms are combined Finally, the real-life applications of the models are also explored

A Survey on Fuzzy Deep Neural Networks

Artificial neural networks model for estimating growth of polyculture microalgae in an open raceway pond

https://hal.archives-ouvertes.fr/hal-03025770/document

Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva.

• Water quality (WQ) management using deep learning (DL) approaches reviewed critically. • DL-based forecasting of WQ parameters reviewed for different water bodies. • Publications on hybrid DL models for WQ management assessed comprehensively. • Importance of the IoT and cloud computing towards DL-based WQ management outlined. Excellent water quality (WQ) is an indispensable element in ensuring sustainable water resource development. It is highly associated with the 3rd (good health and well-being), the 6th (clean water and sanitation), and the 14th (life below water) listed items of the United Nations’ Sustainable Development Goals. Thus, policymakers have always been seeking strategies to manage WQ efficiently. Recent advancements in computational technologies have created enthusiasm for using artificial intelligence, particularly deep learning (DL), in WQ management. This review provides a comprehensive overview of the application of DL in WQ management, covering developments from 2011 to 2022, in maintaining the temporal relevance of this review. In this paper, a brief description of different variants of DL models, including the recurrent neural network (RNN), long short-term memory network (LSTM), convolutional neural network (CNN), etc, are presented. The distinct approaches in the optimization, hybridization and relevant data pre-processing techniques suitable for the DL models, are also discussed. This is the first review paper that extensively discusses the application of DL models for forecasting WQ parameters in various water bodies, such as rivers, lakes, coastal areas, etc. The emergence of the Internet of Things (IoT) and cloud computing that revolutionized DL approaches in WQ management are also presented. This review paper serves as a complete easy guideline for the researchers in the field of DL-based WQ management. The findings of this review paper may help policymakers to enhance their decision-making process with the hope that regional environmental welfare can drastically be improved. 

Applications of Deep Learning in Water Quality Management: A State-of-the-Art Review

In this study, we design an intelligent model to predict chlorophyll-a concentration, which is the primary indicator of algal blooms, using extreme learning machine (ELM) models. Modeling algal blooms is important for environmental management and ecological risk assessment. For this purpose, the performance of the designed models was evaluated for four artificial weirs in the Nakdong River, Korea. The Nakdong River has harmful annual algal blooms that can affect health due to exposure to toxins. In contrast to conventional neural network (NN) that use backpropagation (BP) learning methods, ELMs are fast learning, feedforward neural networks that use least square estimates (LSE) for regression. The weights connecting the input layer to the hidden nodes are randomly assigned and are never updated. The dataset used in this study includes air temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a concentration, which were collected on a weekly basis from January 2013 to December 2016. Here, upstream chlorophyll-a concentration data was used in our ELM2 model to improve algal bloom prediction performance. In contrast, the ELM1 model only uses downstream chlorophyll-a concentration data. The experimental results revealed that the ELM2 model showed better performance in comparison to the ELM1 model. Furthermore, the ELM2 model showed good prediction and generalization performance compared to multiple linear regression (LR), conventional neural network with backpropagation (NN-BP), and adaptive neuro-fuzzy inference system (ANFIS).

https://www.mdpi.com/1660-4601/15/10/2078/pdf

Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea.

Algal blooms have recently become a critical global environmental concern which might put economic development and sustainability at risk. However, the accurate prediction of algal blooms remains a challenging scientific problem. In this study, a novel prediction approach for algal blooms based on deep learning is presented—a powerful tool to represent and predict highly dynamic and complex phenomena. The proposed approach constructs a five-layered model to extract detailed relationships between the density of phytoplankton cells and various environmental parameters. The algal blooms can be predicted by the phytoplankton density obtained from the output layer. A case study is conducted in coastal waters of East China using both our model and a traditional back-propagation neural network for comparison. The results show that the deep-learning-based model yields better generalization and greater accuracy in predicting algal blooms than a traditional shallow neural network does.

https://www.mdpi.com/2071-1050/8/10/1060/pdf

Deep-Learning-Based Approach for Prediction of Algal Blooms

The invention discloses a map service static caching local dynamic update method applicable to on-line edit. The map service static caching local dynamic update method applicable to on-line edit comprises the steps that (1) the organization mode of map service static caching files is defined, and a server caching with a layered and segmented map service is generated; (2) monitoring on on-line edit events of map elements is carried out, user edit block information and map service information of the user edit block information are captured in real time, and an edit and update request is sent to a map service static caching server; (3) the map service static caching server receives the edit and update request to carry out a local caching replacement operation; (4) a client browser learns about a replacement accomplishment message of the map service static caching server, and carries out a caching refresh operation. The map service static caching local dynamic update method applicable to on-line edit maintains synchronism and consistency reflected by map data after edit and web publishing, automatic interactive computer processing is carried out on the premise that a map data edit operation is not influenced, working efficiency is improved, and the map service static caching local dynamic update method applicable to on-line edit is quite wide in application prospect of the field of space information map service publishing, and can meet the actual service requirements for frequent edit of space data.

Tile-type map service caching real-time dynamic update method applicable to on-line edit of user

The invention discloses a web publishing method of quantified remote sensing data. The method comprises the following steps of: 1), confirming the data structure commonly published by a remote sensing data visual color blending information and the real physical magnitude quantity; 2), confirming the source data organization structure published by the quantified remote sensing data; 3), defining the web publishing interface of the data, wherein the interface is used for obtaining the service grade element data, the remote sensing real value data element data information, the remote sensing real value data, the remote sensing data color blending information and the remote sensing data counting information; 4), expressing the principle on the basis of the layered and blocked space image via transforming the remote sensing real value data to the special rendered color blending scheme; performing the visual express on the remote sensing real value data via the method of texture mapping; and 5), performing the local caching management on the remote sensing real value data so as to improve the data displaying and processing efficiency of the remote sensing image data publishing application platform. The invention realizes the quantified web publishing of the remote sensing data and improves the data displaying and processing efficiency.

Web publishing method of quantified remote sensing data

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Deep-Learning-Based Approach for Prediction of Algal Blooms

Tile-type map service caching real-time dynamic update method applicable to on-line edit of user

Web publishing method of quantified remote sensing data