基于计算智能的水产养殖水质预测预警方法研究
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摘要
养殖水质恶化是诱导水产品疾病爆发甚至大批量死亡的首要因素,而养殖水质受多种因素影响,参数间作用机理复杂,导致水质精准预测预警一直是水产养殖业亟需解决的棘手难题。本文以水产养殖中河蟹养殖水质关键参数溶解氧和pH值为研究对象,采用信号处理技术、群集智能计算和机器学习技术,研究了基于计算智能的水产养殖水质预测预警方法,具体如下：
(1)水产养殖水质因子及其影响分析。针对水产养殖水体水质参数多、互相作用机理复杂、水质参数间的作用关系及参数自身的变化规律难以分析等问题,提出了基于系统动力学和能量守恒的水质参数互相作用关系方法,建立了溶解氧、pH值、水温等水质参数系统动力学模型,阐明了水产养殖水质关键参数互相作用的关系。研究表明,该方法是适用于水产养殖水质参数定性的多因素分析方法。
(2)水产养殖水质数据预处理方法研究。针对监测的水质数据中存在数据缺失和噪声影响预测预警方法性能的问题,提出了简单实用的养殖水质数据修复、降噪与特征提取方法。通过线性插值法,相似数据的水平和垂直处理均值法对数据进行修复；采用改进小波分析方法对水质数据进行降噪和特征提取处理。在相同条件下,与其他方法相比,改进小波分析的降噪方法,其评价指标SNR提高了18.93%,BIAS和RMS分别下降了96.15%和33.76%。结果表明,该方法能够满足养殖水质数据净化的要求,为养殖水质信号降噪和特征提取提供一条新手段。
(3)基于改进蚁群算法优化最小二乘支持向量回归机(ACO-LSSVR)的水产养殖溶解氧非线性预测方法研究。针对传统预测方法不适于小样本、高维数、参数优化受人为主观因素影响大等问题,提出了基于ACO-LSSVR的水产养殖溶解氧非线性预测方法。该方法通过基于“探测”思想的局部精细搜索和信息素动态更新思想,改进了蚁群优化算法,实现了LSSVR模型最佳参数自动获取,构建了ACO融合LSSVR的溶解氧非线性预测模型。与BPNN相比,该方法的RMSE.运行时间t分别降低了67.9%和2.3464s。结果表明,该方法不仅克服了传统方法的缺陷,而且能够基本满足水产养殖溶解氧预测的需要。
(4)基于改进粒子群算法优化最小二乘支持向量回归机(IPSO-LSSVR)的水产养殖溶解氧非线性预测方法研究。针对传统预测方法收敛速度慢、预测精度低的问题,提出了IPSO-LSSVR的水产养殖溶解氧非线性预测模型。该方法通过惯性权重自适应动态更新策略,改进了粒子群算法(IPSO),实现了LSSVR模型组合参数优化过程中的精细搜索,构建了IPSO融合LSSVR的溶解氧非线性预测模型。与传统的LSSVR相比,该方法的RMSE、MAE分别下降了29.36%和67.46%,结果表明,该方法收敛速度快,预测效果好,实现了水产养殖溶解氧高精度预测。
(5)基于小波分析、柯西粒子群算法优化最小二乘支持向量回归机(WA-CPSO-LSSVR)的水产养殖溶解氧非线性预测方法研究。针对传统方法受噪音干扰大、预测精度低、易陷入局部极值的缺陷,提出了基于WA-CPSO-LSSVR的水产养殖溶解氧非线性预测方法。通过多分辨率的小波分析,实现了养殖水质数据降噪和多尺度特征分量提取；柯西变异和权重自适应更新算子相结合,改进了粒子群优化算法,实现了LSSVR模型全局最佳组合参数的自适应获取,构建了多尺度分析的水产养殖溶解氧非线性组合预测模型。实验结果表明,该方法不仅有效解决了传统方法的问题,而且能够多尺度分析水质特征,预测效果更好,更适合高密度水产养殖溶解氧的非线性预测,为水质科学化调控提供决策依据。
(6)基于主成分分析法、文化鱼群算法优化最小二乘支持向量回归机(PCA-MCAFA-LSSVR的水产养殖pH值非线性预测方法研究。为减少pH值对水产品新陈代谢及生理功能的胁迫影响,提出了基于PCA-MCAFA-LSSVR的养殖水质pH值非线性预测方法。通过主成分分析法实现养殖水质数据降维和pH值关键影响因子的筛选,优化模型结构；采用文化鱼群算法对最小二乘支持向量回归机参数组合优化,避免搜索过程的盲目性,构建了pH非线性预测模型。该方法将养殖水质指标由10个压缩到4个主成分,其绝对误差小于8%的样本达到93.05%。研究表明,该方法不仅消除了水质信息冗余、降低了计算复杂度,且具有较高的预测精度,为水产养殖pH值精准预测提供一条新途径。
(7)基于粗糙集融合支持向量L(RS-SVM)的水产养殖水质预警方法研究。为解决因水质预警耦合因素多,预警模式复杂以及信息不完整所引起的水质预警精度低的问题,提出了基于RS-SVM的水质预警模型。通过粗糙集对水质数据进行属性约简,精简了基于支持向量机的分类器网络结构,缩减了SVM的训练时间,提高了计算效率。该方法将养殖水质预警指标由14个约简到5个核心预警指标,在不同精度级别上,预警精度均在91%以上。结果表明,该方法不仅消除了冗余属性干扰、优化了模型结构,提高了计算效率,还取得了较好的水质预警效果,能够满足水产养殖水质预警的实际需要。
(8)水产养殖水质预测预警系统的设计与实现。为验证上述方法的有效性,设计实现了水产养殖水质预测预警系统。该系统的硬件部分主要包括水质传感器、水质数据采集器、无线传输设备、现场监控中心、远程监控中心等5部分；软件系统主要包括数据获取、水质预测预警管理、数据检索、信息发布与水质调控管理、系统维护等5个功能模块。通过在该系统上的多次实验表明,所提出的基于计算智能的水产养殖水质预测预警方法是可靠有效的。
Water quality deterioration is the primary factor that induces the outbreak of aquatic disease and even large quantities of death. The quality of cultured water is affected by many factors, and mechanisms of action between the parameters are complex, all of this lead to the fact that accurate prediction and forecasting and early warning of water quality has been a tough problem that needs to be solved. In this paper, based on computational intelligence, we take the key parameters of crabs in the cultured water, dissolved oxygen and PH to study the aquaculture water quality prediction and warning method by using signal processing technology, swarm intelligence computation and machine learning technology. The detailed explanations are as follows:
(1) The analysis of factors and its influence of Aquaculture water quality. In view of problems of too many parameters of aquacultured water quality, complex interaction mechanisms, and also the difficulty in analyzing the relationship between water quality parameters and changing rules of the parameter itself, the water quality parameters interacts upon system dynamics and energy conservation. Moreover, a systematic dynamics model for dissolved oxygen, PH value, temperature and other water quality parameters is also established, the interactive relationship among the key parameters of crabs in cultured water is clarified. The study shows that, this method is the suitable and multi-factored method for determining the nature of the aquatic parameters of aquaculture.
(2) The study of water quality data pre-processing method of aquaculture. In view of the data deficiency of the water quality monitored and the performance of method which forecasts and alerts the noise effect, this paper provides a simple and useful mean to repair the cultured water quality data, coming up with the noise reduction method and the feature extraction method. By using linear interpolation and averaging method of horizontal and vertical processing of similar data, which will be repaired. This paper also applies the modified wavelet analysis method to denoise the water quality data and extract the features of water quality data. Compared with other methods under the same condition, the evaluation index SNR of this modified wavelet analysis method has improved18.93%, and BIAS and RMS decreased96.15%and33.76%respectively. The result shows that this method is possibly to meet the requirement of cleansing the data of cultured water quality, and provide a new path for denoise and feature extraction of culture water quality signal.
(3) Dissolved oxygen content nonlinear prediction model based on the least squares support vector regression (LSSVR) model with optimal parameters selected by ant colony optimization (ACO) algorithm. In view of the traditional prediction method, which is not suitable for small scale of sampling, high dimension and parameter optimization by artificial subjective factors influence puts forward the prediction model of dissolved oxygen in aquaculture nonlinear basing on ACO-LSSVR algorithm. The method updates the strategy through the local fine "detection" search and dynamic pheromone updating ideas, improved ant colony optimization algorithm (ACO), we construct the nonlinear prediction model of dissolved oxygen based on ACO fusion LSSVR. Compared with BPNN, the RMSE. running time t decreased67.9%and2.3464s respectively. The results show that the model prediction accuracy has been obviously improved, and also has good robustness and generalization ability, which meets the actual needs of the intensive aquaculture water quality management.
(4) Dissolved oxygen content nonlinear prediction model based on the least squares support vector regression (LSSVR) model with optimal parameters selected by improved particle swarm optimization (1PSO) algorithm. In view of the traditional prediction method of slow convergence, low accuracy of prediction problems has been put forward the prediction model of dissolved oxygen in aquaculture nonlinear IPSO-LSSVR. The method updates the strategy through the adaptive inertia weight dynamically, improved particle swarm optimization algorithm (IPSO), the fine search LSSVR model parameter optimization process, we construct the prediction model of dissolved oxygen in nonlinear IPSO fusion LSSVR. Compared with the traditional LSSVR, the relative RMSE and MAE are29.36%and67.46%, the results show that, the method has fast convergence speed, good prediction effect, and high precision prediction of dissolved oxygen in aquaculture.
(5) Dissolved oxygen content nonlinear forecasting method in aquaculture is based on wavelet analysis (WA) and least squares support vector regression (LSSVR) with an optimal improved Cauchy particle swarm optimization (CPSO) algorithm. In order to solve large noise disturbance, the low prediction accuracy and inefficiency to trap in local extreme of the traditional forecasting methods in water quality, we proposes a hybrid dissolved oxygen content forecasting method based on WA-CPSO-LSSVR algorithm. In the modeling process, the original dissolved oxygen sequences were de-noised and decomposed into several different resolution frequency signal subsets by using the wavelet analysis method. Cauchy mutation and the adaptive weight update operator combination, improved particle swarm optimization algorithm, the combined LSSVR model with adaptive parameter optimal acquisition, construction of the multi-scale analysis of crab aquaculture dissolved oxygen nonlinear combination forecasting model. The experimental results show that, the method can effectively solve problems of the conventional method, multi-scale analysis of the characteristics of water quality, betters the prediction effect, suits for high density aquaculture dissolved oxygen prediction and offers the decision based on quality scientific regulation.
(6) Forecasting for pH value of aquaculture water quality based on principal component analysis (PCA) and least squares support vector machine (LSSVR) optimized by modified cultural artificial fish-swarm algorithm (MCAFA). In order to reduce the pH value of the crabs metabolism and physiological function of stress, we puts forward the prediction model of water quality based on PCA-MCAFA-LSSVR, which the hyper-parameters is optimized by MCAFA algorithm. The dimension of aquiculture ecologic environmental data has been reduced by PCA method, a core prediction set based on4factors obtained by using PCA to deduct the redundant and disturbed properties from the initial set based on10factors. Using the double evolutionary mechanism of cultural algorithm for reference, the model takes LSSVR as an artificial fish, using the belief space to guide the shoal evolution step size and global search and the Cauchy mutation to improve the diversity of the artificial fish swarm, which obtains the optimal hyper-parameters nonlinear water quality prediction model automatically. Experimental results show that the PCA-MCAFA-LSSVR prediction model has better prediction effect than the other methods, for example, the absolute error of the93.05%test samples are less than8%. It is obvious that PCA-MCAFA-LSSVR prediction model can eliminate superfluous data, low computational complexity and high forecast accuracy, and open up new approaches for aquaculture pH accurate forecasting.
(7) Water quality early-warning model of aquaculture based on support vector machine optimized by rough set algorithm. A new early warning model of water quality, combining rough set (RS) and support vector machine (SVM), is presented to improve the prediction precision affected by mass coupling factors, complex mode and information loss. Firstly, a core warning set based on5factors is obtained by using RS to deduct the redundancy and disturb properties from the initial set based on14factors. Consequently, the early warning model of water quality based on RS-SVM is built up by the core warning set. The experimental results show that our method improves the precision to more than91%in any warning level by using the water quality data. Compared with the other methods, the new model not only has effectiveness of calculation and prediction, but also provides warning results with practicality. This model demonstrates a new thought of early warning on intensive aquaculture water quality.
(8) Design and implementation of the prototype system of water quality forecasting warning system for river crab aquaculture. The objective of the prototype system is to test the abovementioned methods. The hardware system of the prototype includes5parts, i.e., water quality sensors, wireless transmitting equipment, the on-site monitoring center and the remote monitoring center. The software system includes5modules, i.e., data acquisition, data preprocessing, water quality forecasting early warning management, data retrieval, Information release, water quality control management and system maintenance. Great deals of experiments indicate that the researched method works effectively and efficiently in aquaculture forecasting early warning based on computational intelligence.
(1)水产养殖水质因子及其影响分析。针对水产养殖水体水质参数多、互相作用机理复杂、水质参数间的作用关系及参数自身的变化规律难以分析等问题,提出了基于系统动力学和能量守恒的水质参数互相作用关系方法,建立了溶解氧、pH值、水温等水质参数系统动力学模型,阐明了水产养殖水质关键参数互相作用的关系。研究表明,该方法是适用于水产养殖水质参数定性的多因素分析方法。
(2)水产养殖水质数据预处理方法研究。针对监测的水质数据中存在数据缺失和噪声影响预测预警方法性能的问题,提出了简单实用的养殖水质数据修复、降噪与特征提取方法。通过线性插值法,相似数据的水平和垂直处理均值法对数据进行修复；采用改进小波分析方法对水质数据进行降噪和特征提取处理。在相同条件下,与其他方法相比,改进小波分析的降噪方法,其评价指标SNR提高了18.93%,BIAS和RMS分别下降了96.15%和33.76%。结果表明,该方法能够满足养殖水质数据净化的要求,为养殖水质信号降噪和特征提取提供一条新手段。
(3)基于改进蚁群算法优化最小二乘支持向量回归机(ACO-LSSVR)的水产养殖溶解氧非线性预测方法研究。针对传统预测方法不适于小样本、高维数、参数优化受人为主观因素影响大等问题,提出了基于ACO-LSSVR的水产养殖溶解氧非线性预测方法。该方法通过基于“探测”思想的局部精细搜索和信息素动态更新思想,改进了蚁群优化算法,实现了LSSVR模型最佳参数自动获取,构建了ACO融合LSSVR的溶解氧非线性预测模型。与BPNN相比,该方法的RMSE.运行时间t分别降低了67.9%和2.3464s。结果表明,该方法不仅克服了传统方法的缺陷,而且能够基本满足水产养殖溶解氧预测的需要。
(4)基于改进粒子群算法优化最小二乘支持向量回归机(IPSO-LSSVR)的水产养殖溶解氧非线性预测方法研究。针对传统预测方法收敛速度慢、预测精度低的问题,提出了IPSO-LSSVR的水产养殖溶解氧非线性预测模型。该方法通过惯性权重自适应动态更新策略,改进了粒子群算法(IPSO),实现了LSSVR模型组合参数优化过程中的精细搜索,构建了IPSO融合LSSVR的溶解氧非线性预测模型。与传统的LSSVR相比,该方法的RMSE、MAE分别下降了29.36%和67.46%,结果表明,该方法收敛速度快,预测效果好,实现了水产养殖溶解氧高精度预测。
(5)基于小波分析、柯西粒子群算法优化最小二乘支持向量回归机(WA-CPSO-LSSVR)的水产养殖溶解氧非线性预测方法研究。针对传统方法受噪音干扰大、预测精度低、易陷入局部极值的缺陷,提出了基于WA-CPSO-LSSVR的水产养殖溶解氧非线性预测方法。通过多分辨率的小波分析,实现了养殖水质数据降噪和多尺度特征分量提取；柯西变异和权重自适应更新算子相结合,改进了粒子群优化算法,实现了LSSVR模型全局最佳组合参数的自适应获取,构建了多尺度分析的水产养殖溶解氧非线性组合预测模型。实验结果表明,该方法不仅有效解决了传统方法的问题,而且能够多尺度分析水质特征,预测效果更好,更适合高密度水产养殖溶解氧的非线性预测,为水质科学化调控提供决策依据。
(6)基于主成分分析法、文化鱼群算法优化最小二乘支持向量回归机(PCA-MCAFA-LSSVR的水产养殖pH值非线性预测方法研究。为减少pH值对水产品新陈代谢及生理功能的胁迫影响,提出了基于PCA-MCAFA-LSSVR的养殖水质pH值非线性预测方法。通过主成分分析法实现养殖水质数据降维和pH值关键影响因子的筛选,优化模型结构；采用文化鱼群算法对最小二乘支持向量回归机参数组合优化,避免搜索过程的盲目性,构建了pH非线性预测模型。该方法将养殖水质指标由10个压缩到4个主成分,其绝对误差小于8%的样本达到93.05%。研究表明,该方法不仅消除了水质信息冗余、降低了计算复杂度,且具有较高的预测精度,为水产养殖pH值精准预测提供一条新途径。
(7)基于粗糙集融合支持向量L(RS-SVM)的水产养殖水质预警方法研究。为解决因水质预警耦合因素多,预警模式复杂以及信息不完整所引起的水质预警精度低的问题,提出了基于RS-SVM的水质预警模型。通过粗糙集对水质数据进行属性约简,精简了基于支持向量机的分类器网络结构,缩减了SVM的训练时间,提高了计算效率。该方法将养殖水质预警指标由14个约简到5个核心预警指标,在不同精度级别上,预警精度均在91%以上。结果表明,该方法不仅消除了冗余属性干扰、优化了模型结构,提高了计算效率,还取得了较好的水质预警效果,能够满足水产养殖水质预警的实际需要。
(8)水产养殖水质预测预警系统的设计与实现。为验证上述方法的有效性,设计实现了水产养殖水质预测预警系统。该系统的硬件部分主要包括水质传感器、水质数据采集器、无线传输设备、现场监控中心、远程监控中心等5部分；软件系统主要包括数据获取、水质预测预警管理、数据检索、信息发布与水质调控管理、系统维护等5个功能模块。通过在该系统上的多次实验表明,所提出的基于计算智能的水产养殖水质预测预警方法是可靠有效的。
Water quality deterioration is the primary factor that induces the outbreak of aquatic disease and even large quantities of death. The quality of cultured water is affected by many factors, and mechanisms of action between the parameters are complex, all of this lead to the fact that accurate prediction and forecasting and early warning of water quality has been a tough problem that needs to be solved. In this paper, based on computational intelligence, we take the key parameters of crabs in the cultured water, dissolved oxygen and PH to study the aquaculture water quality prediction and warning method by using signal processing technology, swarm intelligence computation and machine learning technology. The detailed explanations are as follows:
(1) The analysis of factors and its influence of Aquaculture water quality. In view of problems of too many parameters of aquacultured water quality, complex interaction mechanisms, and also the difficulty in analyzing the relationship between water quality parameters and changing rules of the parameter itself, the water quality parameters interacts upon system dynamics and energy conservation. Moreover, a systematic dynamics model for dissolved oxygen, PH value, temperature and other water quality parameters is also established, the interactive relationship among the key parameters of crabs in cultured water is clarified. The study shows that, this method is the suitable and multi-factored method for determining the nature of the aquatic parameters of aquaculture.
(2) The study of water quality data pre-processing method of aquaculture. In view of the data deficiency of the water quality monitored and the performance of method which forecasts and alerts the noise effect, this paper provides a simple and useful mean to repair the cultured water quality data, coming up with the noise reduction method and the feature extraction method. By using linear interpolation and averaging method of horizontal and vertical processing of similar data, which will be repaired. This paper also applies the modified wavelet analysis method to denoise the water quality data and extract the features of water quality data. Compared with other methods under the same condition, the evaluation index SNR of this modified wavelet analysis method has improved18.93%, and BIAS and RMS decreased96.15%and33.76%respectively. The result shows that this method is possibly to meet the requirement of cleansing the data of cultured water quality, and provide a new path for denoise and feature extraction of culture water quality signal.
(3) Dissolved oxygen content nonlinear prediction model based on the least squares support vector regression (LSSVR) model with optimal parameters selected by ant colony optimization (ACO) algorithm. In view of the traditional prediction method, which is not suitable for small scale of sampling, high dimension and parameter optimization by artificial subjective factors influence puts forward the prediction model of dissolved oxygen in aquaculture nonlinear basing on ACO-LSSVR algorithm. The method updates the strategy through the local fine "detection" search and dynamic pheromone updating ideas, improved ant colony optimization algorithm (ACO), we construct the nonlinear prediction model of dissolved oxygen based on ACO fusion LSSVR. Compared with BPNN, the RMSE. running time t decreased67.9%and2.3464s respectively. The results show that the model prediction accuracy has been obviously improved, and also has good robustness and generalization ability, which meets the actual needs of the intensive aquaculture water quality management.
(4) Dissolved oxygen content nonlinear prediction model based on the least squares support vector regression (LSSVR) model with optimal parameters selected by improved particle swarm optimization (1PSO) algorithm. In view of the traditional prediction method of slow convergence, low accuracy of prediction problems has been put forward the prediction model of dissolved oxygen in aquaculture nonlinear IPSO-LSSVR. The method updates the strategy through the adaptive inertia weight dynamically, improved particle swarm optimization algorithm (IPSO), the fine search LSSVR model parameter optimization process, we construct the prediction model of dissolved oxygen in nonlinear IPSO fusion LSSVR. Compared with the traditional LSSVR, the relative RMSE and MAE are29.36%and67.46%, the results show that, the method has fast convergence speed, good prediction effect, and high precision prediction of dissolved oxygen in aquaculture.
(5) Dissolved oxygen content nonlinear forecasting method in aquaculture is based on wavelet analysis (WA) and least squares support vector regression (LSSVR) with an optimal improved Cauchy particle swarm optimization (CPSO) algorithm. In order to solve large noise disturbance, the low prediction accuracy and inefficiency to trap in local extreme of the traditional forecasting methods in water quality, we proposes a hybrid dissolved oxygen content forecasting method based on WA-CPSO-LSSVR algorithm. In the modeling process, the original dissolved oxygen sequences were de-noised and decomposed into several different resolution frequency signal subsets by using the wavelet analysis method. Cauchy mutation and the adaptive weight update operator combination, improved particle swarm optimization algorithm, the combined LSSVR model with adaptive parameter optimal acquisition, construction of the multi-scale analysis of crab aquaculture dissolved oxygen nonlinear combination forecasting model. The experimental results show that, the method can effectively solve problems of the conventional method, multi-scale analysis of the characteristics of water quality, betters the prediction effect, suits for high density aquaculture dissolved oxygen prediction and offers the decision based on quality scientific regulation.
(6) Forecasting for pH value of aquaculture water quality based on principal component analysis (PCA) and least squares support vector machine (LSSVR) optimized by modified cultural artificial fish-swarm algorithm (MCAFA). In order to reduce the pH value of the crabs metabolism and physiological function of stress, we puts forward the prediction model of water quality based on PCA-MCAFA-LSSVR, which the hyper-parameters is optimized by MCAFA algorithm. The dimension of aquiculture ecologic environmental data has been reduced by PCA method, a core prediction set based on4factors obtained by using PCA to deduct the redundant and disturbed properties from the initial set based on10factors. Using the double evolutionary mechanism of cultural algorithm for reference, the model takes LSSVR as an artificial fish, using the belief space to guide the shoal evolution step size and global search and the Cauchy mutation to improve the diversity of the artificial fish swarm, which obtains the optimal hyper-parameters nonlinear water quality prediction model automatically. Experimental results show that the PCA-MCAFA-LSSVR prediction model has better prediction effect than the other methods, for example, the absolute error of the93.05%test samples are less than8%. It is obvious that PCA-MCAFA-LSSVR prediction model can eliminate superfluous data, low computational complexity and high forecast accuracy, and open up new approaches for aquaculture pH accurate forecasting.
(7) Water quality early-warning model of aquaculture based on support vector machine optimized by rough set algorithm. A new early warning model of water quality, combining rough set (RS) and support vector machine (SVM), is presented to improve the prediction precision affected by mass coupling factors, complex mode and information loss. Firstly, a core warning set based on5factors is obtained by using RS to deduct the redundancy and disturb properties from the initial set based on14factors. Consequently, the early warning model of water quality based on RS-SVM is built up by the core warning set. The experimental results show that our method improves the precision to more than91%in any warning level by using the water quality data. Compared with the other methods, the new model not only has effectiveness of calculation and prediction, but also provides warning results with practicality. This model demonstrates a new thought of early warning on intensive aquaculture water quality.
(8) Design and implementation of the prototype system of water quality forecasting warning system for river crab aquaculture. The objective of the prototype system is to test the abovementioned methods. The hardware system of the prototype includes5parts, i.e., water quality sensors, wireless transmitting equipment, the on-site monitoring center and the remote monitoring center. The software system includes5modules, i.e., data acquisition, data preprocessing, water quality forecasting early warning management, data retrieval, Information release, water quality control management and system maintenance. Great deals of experiments indicate that the researched method works effectively and efficiently in aquaculture forecasting early warning based on computational intelligence.
引文
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