Shuangyin Liu

A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction

Abstract Water quality prediction plays an important role in modern intensive river crab aquaculture management. Due to the nonlinearity and non-stationarity of water quality indicator series, the accuracy of the commonly used conventional methods, including regression analyses and neural networks, has been limited. A prediction model based on support vector regression (SVR) is proposed in this paper to solve the aquaculture water quality prediction problem. To build an effective SVR model, the SVR parameters must be set carefully. This study presents a hybrid approach, known as real-value genetic algorithm support vector regression (RGA–SVR), which searches for the optimal SVR parameters using real-value genetic algorithms, and then adopts the optimal parameters to construct the SVR models. The approach is applied to predict the aquaculture water quality data collected from the aquatic factories of YiXing, in China. The experimental results demonstrate that RGA–SVR outperforms the traditional SVR and back-propagation (BP) neural network models based on the root mean square error (RMSE) and mean absolute percentage error (MAPE). This RGA–SVR model is proven to be an effective approach to predict aquaculture water quality.

A hybrid WA-CPSO-LSSVR model for dissolved oxygen content prediction in crab culture

To increase prediction accuracy, reduce aquaculture risks and optimize water quality management in intensive aquaculture ponds, this paper proposes a hybrid dissolved oxygen content forecasting model based on wavelet analysis (WA) and least squares support vector regression (LSSVR) with an optimal improved Cauchy particle swarm optimization (CPSO) algorithm. In the modeling process, the original dissolved oxygen sequences were de-noised and decomposed into several resolution frequency signal subsets using the wavelet analysis method. Independent prediction models were developed using decomposed signals with wavelet analysis and least squares support vector regression. The independent prediction values were reconstructed to obtain the ultimate prediction results. In addition, because the kernel parameter @d and the regularization parameter @c in the LSSVR training procedure significantly influence forecasting accuracy, the Cauchy particle swarm optimization (CPSO) algorithm was used to select optimum parameter combinations for LSSVR. The proposed hybrid model was applied to predict dissolved oxygen in river crab culture ponds. Compared with traditional models, the test results of the hybrid WA-CPSO-LSSVR model demonstrate that de-noising and capturing non-stationary characteristics of dissolved oxygen signals after WA comprise a very powerful and reliable method for predicting dissolved oxygen content in intensive aquaculture accurately and quickly.

Prediction of Dissolved Oxygen Content in Aquaculture of Hyriopsis Cumingii Using Elman Neural Network

Hyriopsis Cumingii is Chinese major fresh water pearl mussel, widely distributed in the southern provinces of China’s large and medium-sized freshwater lakes. In the management of Hyriopsis Cumingii ponds, dissolved oxygen (DO) is the key point to measure, predict and control. In this study, we analyzes the important factors for predicting dissolved oxygen of Hyriopsis Cumingii ponds, and finally chooses solar radiation(SR), water temperature(WT), wind speed(WS), PH and oxygen(DO) as six input parameters. In this paper, Elman neural networks were used to predict and forecast quantitative characteristics of water. As the dissolved oxygen in the outdoor pond is low controllability and scalability, this paper proposes a predicting model for dissolved oxygen. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The Elman NN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Experimental results show that: Elman neural network predicting model with good fitting ability, generalization ability, and high prediction accuracy, can better predict the changes of dissolved oxygen.

Key Technology of South Sea Pearl Industry Management Information Service Platform Based on the Internet of Things

This research constructs the south sea pearl industry management information service platform based on the Internet of Things, analyzing the features and technical advantages of Internet of Things, as well as the current existing problems of the pearl Industrial management. We have investigated the application management model of the Internet of Things in the product flow of pearl industry from production to processing management and explored the application of the key technologies, like radio frequency identification (RFID), wireless sensor network (WSN), physical markup language (PML) and the Electronic Product Code (EPC), to the Internet of Things in the pearl industrial management information services application platform. With the hope to bridge and eliminate the gap caused by differences in technology and standards among various sectors in the South Sea pearl production, processing, marketing and distribution, thus integration and optimization of pearl industrial management will be promoted and developed healthily.

A Review of Measurement Methods of Dissolved Oxygen in Water

Some kinds of checking methods and principle of dissolved oxygen in water were summarized. Such as: iodometric method, current determination method (Clark dissolved oxygen electrode), conductance measurement and fluorescence quenching. The advantages and disadvantages of each method were compared, and fluorescence quenching was discussed. The method uses Ruthenium complex as fluorescence sensitive reagent, which emits fluorescence under the excited light. The quenching accords with Stem-Volmer formula, and the density of oxygen could be deduced by checking fluorescence spectrum.

Prediction of water temperature in prawn cultures based on a mechanism model optimized by an improved artificial bee colony

Abstract To reduce aquaculture risk and optimize water quality management in prawn culture ponds, this paper uses mechanistic and statistical analytic methods to propose a hybrid water temperature forecasting model based on the water temperature mechanism model (WTMM) with optimal parameters selected by an improved artificial bee colony (IABC) algorithm. Because of existing problems with using an artificial bee colony algorithm in modeling, an improved ABC with a dynamically adjusted inertia weight based on the fitness function value was implemented to improve local and global search abilities. Then, IABC was employed to adaptively search for the optimal combinatorial parameters needed in the WTMM model, which overcomes the blindness of and limits to parameter selection for the traditional WTMM model. We adopted an IABC-WTMM algorithm to construct a non-linear mechanical prediction model. The IABC-WTMM was tested and compared to other algorithms by applying it to the prediction of water temperature in prawn culture ponds. Experimental results show that the proposed IABC-WTMM could increase prediction accuracy and execute generalization performance better than the original water temperature mechanism model (O-WTMM) and back-propagation neural network (BP-NN), but was inferior to the standard LSSVR model. Overall, it is a suitable and effective method for predicting water temperature in intensive aquacultures.

Water Temperature Forecasting in Sea Cucumber Aquaculture Ponds by RBF Neural Network Model

Water temperature is considered to be the most important parameter which can largely determine the aquaculture production of sea cucumbers, so it is extremely important to monitor and forecast the water temperature at different water depths. As the change of water temperature is a complex process which can not be exactly described with a certain formula, the artificial neural network characterized by non-linearity, adaptivity, generalization, and model independence is a proper choice. This paper presents a RBF neural network model based on nearest neighbor clustering algorithm and puts forward four improved methods, then integrates them into an optimization model and verifies it on matlab platform. Finally, a comparison between the optimized RBF model and the original RBF model is made to confirm the excellent forecasting performance of the optimized RBF neural network model. This paper provides a relatively impeccable learning algorithm to complete the choice of radial basis clustering center in the process of RBF network design, and obtains a high forecasting precision so that the demand of water temperature forecasting in sea cucumber aquaculture ponds can be satisfied.