An On-Line Oxygen Forecasting System for Waterless Live Transportation of Flatfish Based on Feature Clustering

Abstract

Featured Application: The proposed method could be adapted into other sea fish on-line ambient monitoring and optimal regulation applications of waterless live transportation.Abstract: Accurate prediction of forthcoming oxygen concentration during waterless live fish transportation plays a key role in reducing the abnormal occurrence, increasing the survival rate in delivery operations, and optimizing manufacturing costs. The most effective ambient monitoring techniques that are based on the analysis of historical process data when performing forecasting operations do not fully consider current ambient influence. This is likely lead to a greater deviation in on-line oxygen level forecasting in real situations. Therefore, it is not advisable for the system to perform early warning and on-line air adjustment in delivery. In this paper, we propose a hybrid method and its implementation system that combines a gray model (GM (1, 1)) with least squares support vector machines (LSSVM) that can be used effectively as a forecasting model to perform early warning effectively according to the dynamic changes of oxygen in a closed system. For accurately forecasting of the oxygen level, the fuzzy C-means clustering (FCM) algorithm was utilized for classification according to the flatfish's physical features-i.e., length and weight-for more pertinent training. The performance of the gray model-particle swarm optimization-least squares support vector machines (GM-PSO-LSSVM) model was compared with the traditional modeling approaches of GM (1, 1) and LSSVM by applying it to predict on-line oxygen level, and the results showed that its predictions were more accurate than those of the LSSVM and grey model. Therefore, it is a suitable and effective method for abnormal condition forecasting and timely control in the waterless live transportation of flatfish.

Effects of ammonia, temperature and their interaction on oxygen consumption rate of Asian seabass (Lates calcarifer) juveniles

“…During transport, water quality deteriorates, increasing the TAN and CO 2 levels, decreasing the dissolved oxygen and pH and leading to stress on fish (Gomes et al, 1999;Grottum et al, 2008;Hong et al, 2019). Live fish transportation has involved pre-transportation starving (Shrivastava et al, 2017), adding sodium chloride or using an anesthetic (Zhang et al, 2018;Hong et al, 2019), controlling the temperature (Hong et al, 2019), optimizing the stocking density (Gomes et al, 2003, Hong et al, 2019 and oxygenation (Hahn and Pérez, 2015;Zhang et al, 2017;Hong et al, 2019).…”

Intelligent feeding technique based on predicting shrimp growth in recirculating aquaculture system

“…The technique will help control and prevent excessive waste of feed and promote fish growth in aquaculture. In recent years, the combination of sensors with the Internet of Things and artificial intelligence technologies has been widely used in aquaculture, making water quality prediction and early warning technologies more accurate and smarter (Chen et al, 2018; Li et al, 2018; Liu et al, 2013; Zhang et al, 2017).…”