Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

A Review of the Artificial Neural Network Models for Water Quality Prediction

With the rapid emergence of deep learning (DL) technology, it has been successfully used in various fields including aquaculture. This change can create new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on the applications of DL in aquaculture, including live fish identification, species classification, behavioral analysis, feeding decision-making, size or biomass estimation, water quality prediction. In addition, the technical details of DL methods applied to smart fish farming are also analyzed, including data, algorithms, computing power, and performance. The results of this review show that the most significant contribution of DL is the ability to automatically extract features. However, challenges still exist; DL is still in an era of weak artificial intelligence. A large number of labeled data are needed for training, which has become a bottleneck restricting further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs in the handling of complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for the implementation of smart fish farming.

Deep learning for smart fish farming: applications, opportunities and challenges

Evaluation of fish feeding intensity in aquaculture using a convolutional neural network and machine vision.

Control framework for cooperative robots in smart home using bio-inspired neural network

Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach

A review on the research status and development trend of equipment in water treatment processes of recirculating aquaculture systems

Survey of underwater robot positioning navigation

Research on a dissolved oxygen prediction method for recirculating aquaculture systems based on a convolution neural network

Traditional planting and aquaculture has the problem of large consumption of water resources and land resources, and the water environmental pollution is also a difficult problem facing human beings. Population growth and food safety issues have promoted the concept of aquaponics——a recycling ecological planting and breeding mode. It combines hydroponics and recirculating aquaculture technology to realize water resources and nutrient recycling, low pollution and high productivity and efficiency. In this paper, hydroponics as the main vegetable cultivation method in aquaponics and the main equipment of water treatment in recirculating aquaculture are introduced, and the traditional equipment and its development prospects are analyzed. The greenhouse environments, water quality and nutrient circulation involved in intelligent monitoring and control of aquaponic systems are systematically analyzed and summarized. This paper summarizes the current development of technology and methods in aquaponics and provides prospects for future development trends. With the development of technology, in the future, the aquaponics system will become more intelligent, intensive, accurate and efficient.

/pdf/equipment-and-intelligent-control-system-in-aquaponics-a-4t6pjkdlxo.pdf

Yaoguang Wei

Papers

A review on the research status and development trend of equipment in water treatment processes of recirculating aquaculture systems

Survey of underwater robot positioning navigation

Research on a dissolved oxygen prediction method for recirculating aquaculture systems based on a convolution neural network

Equipment and Intelligent Control System in Aquaponics: A Review

A method for predicting dissolved oxygen in aquaculture water in an aquaponics system