共查询到20条相似文献，搜索用时 15 毫秒
1.
Dissolved oxygen in water is an important ecological factor in ensuring the healthy growth of aquatic products, as hypoxic stress is known to restrict the growth of aquatic products. The accurate monitoring and prediction of dissolved oxygen is the key to precise regulation and control of pond aquaculture water quality. The current dissolved oxygen prediction model has some limitations, such as a short prediction period and inadequate prediction accuracy for actual production demands. Therefore, a prediction model of dissolved oxygen in pond culture was proposed based on K-means clustering and Gated Recurrent Unit (GRU) neural network. Firstly, the key factors affecting the changes in dissolved oxygen were selected by principal component analysis (PCA). The dissolved oxygen time series was then subjected to K-means clustering, and the dissolved oxygen prediction model was constructed using GRU. To improve the clustering effect, we enhanced the similarity calculation for the time series based on the variation of dissolved oxygen. This process combined the Euclidean distance with the dynamic time-warping distance. The proposed method can predict the dissolved oxygen content of aquaculture water over different time intervals according to the demands of real-world scenarios. The average absolute error of the 30-min interval model was 0.264, and the mean absolute percentage error was 3.5 %. Experimental results indicated that the proposed method achieves higher prediction accuracy and flexibility than the conventional approach. 相似文献
2.
As dissolved oxygen (DO) is an important indicator of water quality in aquaculture, an accurate prediction for DO can effectively improve quantity and quality of product. Accordingly, a novel hybrid dissolved oxygen prediction model, which combines the multiple-factor analysis and the multi-scale feature extraction, is proposed. Firstly, considering that dissolved oxygen is affected by complex factors, water temperature and pH are chosen as the most relevant environmental factors for dissolved oxygen, using grey relational degree method. Secondly, the ensemble empirical mode decomposition (EEMD) is adopted to decompose the dissolved oxygen, water temperature and pH data into several sub-sequences, respectively. Then, the sample entropy (SE) algorithm reconstructs the sub-sequences to obtain the trend component, random component and detail component. Lastly, regularized extreme learning machine (RELM), a currently effective and stable artificial intelligent (AI) tool, is applied to predict three components independently. The prediction models of random component, detail component and trend component are RELM1, RELM2 and RELM3 respectively. The dissolved oxygen, water temperature and pH of the random component forms the input layer of RELM1, and predicted value of dissolved oxygen in the random component is the output layer of RELM1. The input and output of RELM2 and RELM3 are similar to that of RELM1. Final prediction results are obtained by superimposing three components predicted values. One of the main features of the proposed approach is that it integrates the multiple-factor analysis and the multi-scale feature extraction using grey correlation analysis and EEMD. Its performance is compared with several outstanding algorithms. Results for experiment show that the proposed model has satisfactory performance and high precision. 相似文献
3.
目前神经网络研究文献成果较多，虽然在水质精准预测方面起到了一定的参考，但由于文献缺少科学分类，使用率不高，导致学者难以找到研究切入点。针对这一问题，本文将神经网络方法在养殖区水质精准预测方面的文献按照海水和淡水两大领域进行分类，主要对每个领域所应用的预测模型从正反馈架构、循环架构和混合架构三个方向对海水时空序列文献进行分类研究和综述，发现混合架构模型的预测性能优于正反馈模型和循环架构模型，有利于提升不同深度水质预测模型的精度。另外，本文对基于神经网络方法的三维水质预测模型进行了初步探讨，发现学者的研究成果更多地集中在水表层和水中层的不同位置水质参数的变化方面，而神经网络方法对水表层水质预测精度比水中层和水深层水质预测精度高。 相似文献
4.
基于模糊神经网络的池塘溶解氧预测模型 总被引：5，自引：0，他引：5
在分析了池塘溶解氧影响因素的基础上，利用模糊神经网络良好的非线性逼近能力建立了池塘溶解氧的模糊神经网络预测模型。神经网络模型如采用常规的BP或其它梯度算法，常导致训练时间较长且易陷入局部极小点，本实验采用快速的粒子群优化算法对模糊神经网络进行训练，收敛速度明显加快。实验结果表明采用该方法预报溶解氧的预测精度较常规BP递推算法的预测精度明显提高，所采用的模型能对溶解氧进行可靠的预测，该方法为研制开发智能水质检测仪以及工厂化养殖工作奠定了基础，对实际生产具有一定的指导意义。 相似文献
5.
针对养殖水质、水温及p H预测准确性低的问题,提出了一种基于粒子群优化BP神经网络的养殖水质参数预测方法。首先应用粒子群算法优化得出BP神经网络的初始权值和阈值,然后对得到的数据进行预处理,修复异常数据信息,再以当前时间的多个水质参数作为输入,下个时间点的水温、p H作为输出,建立养殖水质预测模型,最后利用采集的水质数据在BP神经网络中进行训练,并通过实验检验水质预测模型的可行性和预测性能。与支持向量回归(SVR)和传统BP神经网络相比,基于粒子群优化的BP神经网络在预测水温方面,均方根误差(RMSE)下降幅度分别为64.4%和86.7%;在预测p H方面,RMSE下降幅度分别为11.1%和78.9%。研究表明,基于粒子群优化的BP神经网络养殖水质预测模型具有灵活简便、预测精度高、易于实现的特点,同时具有很好的预测能力。 相似文献
6.
Dissolved oxygen (DO) is a key ecological factor to measure the quality of water in the aquaculture. As the pond water body is affected by the breeding environment, the spatial distribution of DO shows a certain law in the entire pond. Therefore, to simulate the distribution of DO in aquaculture waters and grasp the temporal and spatial variation of DO is the key to achieving precise regulation of DO. For this purpose, this paper proposed a method for simulating the temporal and spatial distribution of DO in pond culture based on a sliding window-temporal convolutional network together with trend surface analysis (SW-TCN-TSA). This paper first utilized SW to construct DO data sets with different prediction durations, and then used the improved TCN model to realize one-dimensional time series prediction for DO at single monitoring point. Based on the prediction results of DO, a TSA method was performed on the predicted values of DO at the extreme moments of all discrete monitoring points, so as to realize the simulation of the temporal and spatial distribution of DO in the pond. Experimental results show that the SW-TCN model has better prediction performance for one-dimensional time series prediction of DO. Compared with traditional deep networks, such as CNN, GRU, LSTM, CNN-GRU and CNN-LSTM, the values of evaluation indicators (MSE, MAE and RMSE) have been greatly improved. In the process of trend surface fitting, all fitting R2 of DO at different water depths are higher than 0.9, indicating that the TSA can accurately reflect the temporal and spatial distribution of DO. This method can provide a basis for the prediction and early warning of DO in the three-dimensional space of the pond and has high practicability in aquaculture. 相似文献
7.
家鱼池塘底泥耗氧率与理化因子的相关性分析 总被引：1，自引：0，他引：1
采用原位底泥耗氧测定法,研究了10口家鱼鱼池底泥耗氧率与底部水体理化因子(溶氧、温度、pH值、氧化还原电位)和底泥有机质含量及深度的相关关系。结果显示:池塘平均底泥耗氧率(SOD)为0.91 g/(m2.d),变动范围为0.76～1.09 g/(m2.d)。双变量相关性分析表明,底泥耗氧率与池塘底部水体理化指标的相关性均达到极显著水平(P<0.01),与溶氧相关性最高(Pearson相关系数为0.779),其次是温度、pH值和氧化还原电位,相关系数分别为0.587、0.557和-0.421;底泥耗氧率与底泥深度相关性达到显著水平(P<0.05)。偏相关分析结果表明,底泥耗氧率与溶氧和温度呈极显著相关(P<0.01),与其它因素均未达到显著水平。影响底泥耗氧率最重要的环境因子是溶氧,其次是温度。利用BP神经网络分析影响SOD的理化因子,以溶氧、温度和底泥深度为BP神经网络模型的输入变量建立BP神经网络模型对SOD进行预测分析,BP神经网络模型训练和测试相关系数分别为0.911和0.879,平均相对误差分别为11.6%和10.4%,预测值与真实值偏差较小,拟合度较高,可有效预测池塘底泥耗氧率。 相似文献
8.
针对无线化的水产养殖水质监测系统耗能大、电池寿命短的问题,设计了基于Zigbee和GPRS的节能型水质监测系统。通过采用低功耗器件,在电源与传感器、信号调理电路之间添加选通芯片ADG1414控制各模块分时分区工作,减少各模块的供电时间来降低硬件能耗;通过设置阈值对采集的数据进行判断,对阈值范围内的数据不发送,减少数据发送量,从而减少系统数据发送能耗。以CC2530为核心构建无线传感网络,将传感器采集到的温度、p H、溶氧等水质参数传输至监测中心,构建实时监测平台,并在此基础上建立数据管理系统,实现对水产养殖水质环境的实时监测。系统测试与实验结果表明,该系统节能效果显著,能有效延长无线水质监测系统电池的工作时间。 相似文献
9.
针对水产养殖产量预测难的现状,提出一种基于启发式Johnson算法优化的反向传播神经网络(BPNN)的产量预测模型。该模型在传统BP神经网络的基础上,针对网络训练时间长、易陷入局部最优的问题,通过启发式Johnson算法降低输入神经元维度,再结合试凑法确定神经网络隐层个数,构建启发式Johnson反向传播神经网络(HJA-BPNN)学习预测模型。实验结果表明,该模型在山东省对虾海水养殖产量预测中,预测的均方根误差小于传统BP神经网络和GM(1,1),且学习效率相比传统BP神经网络有所提升。研究表明,该学习预测模型在大量历史数据的模型构造上有更大的优势,能够缩短建模时间,同时获得良好的预测效果,为水产养殖产量预测提供了一种可行的新方法。 相似文献
10.
A dynamic model of dissolved oxygen behavior in a stillwater aquaculture pond is presented. Using theories and principles that have been established for aerobic waste-water treatment ponds and shallow lakes and reservoirs, equations were developed to describe the short-term dissolved oxygen fluctuations of an aquaculture pond. Components considered in this model include the consumption and production of oxygen by phytoplankton, fish, detritus, and the process of reaeration. Factors affecting these components can be specified by the user; these include meteorologic and geographic data, and phytoplankton, fish, and detrital characteristics. After comparing the model to field data and analyzing the sensitivity of its components, areas of research which may have an impact on pond management are identified. 相似文献
11.
Fish species identification is vital for aquaculture and fishery industries, stock management of water bodies and environmental monitoring of aquatics. Traditional fish species identification approaches are costly, time consuming, expert-based and unsuitable for large-scale applications. Hence, in this study, a deep learning neural network as a smart, real-time and non-destructive method was developed and applied to automate the identification of four economically important carp species namely common carp (Cyprinus carpio), grass carp (Ctenopharingodon idella), bighead carp (Hypophtalmichthys nobilis) and silver carp (Hypophthalmichthys molitrix). The obtained results proved that our approach, evaluated through 5-fold cross-validation, achieved the highest possible accuracy of 100 %. The achieved high level of classification accuracy was due to the ability of the suggested deep model to build a hierarchy of self-learned features, which was in accordance with the hierarchy of these fish’s identification keys. In conclusion, the proposed convolutional neural network (CNN)-based method has a single and generic trained architecture with promising performance for fish species identification. 相似文献
12.
This paper presents a novel method to evaluate fish feeding intensity for aquaculture fish farming. Determining the level of fish appetite helps optimize fish production and design more efficient aquaculture smart feeding systems. Given an aquaculture surveillance video, our goal is to improve fish feeding intensity evaluation by proposing a two-stage approach: an optical flow neural network is first applied to generate optical flow frames, which are then inputted to a 3D convolution neural network (3D CNN) for fish feeding intensity evaluation. Using an aerial drone, we capture RGB water surface images with significant optical flows from an aquaculture site during the fish feeding activity. The captured images are inputs to our deep optical flow neural network, consisting of the leading neural network layers for video interpolation and the last layer for optical flow regression. Our optical flow detection model calculates the displacement vector of each pixel across two consecutive frames. To construct the training dataset of our CNNs and verify the effectiveness of our proposed approach, we manually annotated the level of fish feeding intensity for each training image frame. In this paper, the fish feeding intensity is categorized into four, i.e., ‘none,’ ‘weak,’ ‘medium’ and ‘strong.’ We compared our method with other state-of-the-art fish feeding intensity evaluations. Our proposed method reached up to 95 % accuracy, which outperforms the existing systems that use CNNs to evaluate the fish feeding intensity. 相似文献
13.
14.
亚硝态氮对于水产养殖动物具有毒性,对于其含量的及时监控非常重要。基于光谱法和电极法设计的亚硝态氮传感器价格昂贵,难以大面积推广,因此急需研发一种能快速预测养殖水体亚硝态氮的模型。实验通过实验室构建的水质在线检测系统测定水体中温度、pH、溶解氧、氧化还原电位4个参数,同时用α-萘胺比色法测定水体中亚硝态氮的浓度,从4种参数中选取与亚硝态氮浓度相关的参数作为预测模型的关联变量。水质参数数据及亚硝态氮浓度数据分别经预处理后作为原始数据用于SAE神经网络的训练,训练方法采用无监督逐层贪婪训练法,用学习到的特征监督训练SAE-BP神经网络,利用反向传播算法(BP)优化模型。训练得到结构为4-5-4-3-1的SAE-BP神经网络模型,建立的神经网络模型对实验数据预测的拟合优度R2为0.95,预测结果的均方根误差RMSEP为0.099 71。研究表明,亚硝态氮预测模型可以较为精准地预测水体中亚硝态氮的浓度。本模型将为开发在线快速监测养殖水体亚硝态氮浓度提供新的思路。 相似文献
15.
16.
基于BP神经网络模型的福建海域赤潮预报方法研究 总被引：4，自引：0，他引：4
赤潮往往给渔业生产和人类的生命安全造成极大的危害,但由于赤潮的成因十分复杂,对其进行预报非常困难。本研究收集了福建海区2000年至2016年发生的219个赤潮案例有效数据,应用BP神经网络人工智能模型建立了其与气温、降水、风速、气压和日照5个气象因子的非线性关系,并将这些赤潮案例数据与相应的气象指标按闽东、闽中和闽南3个海区,分别输入模型进行学习、训练与预测。结果显示:1)闽东海区53个训练样本45个预测正确,正确率达84.91%,3个模拟预测样本全部正确;2)闽中海区69个训练样本58个预测正确,正确率达84.06%,4个模拟预测样本全部正确;3)闽南海区85个训练样本的运算预测结果63个正确,正确率74.12%,5个模拟预测样本全部正确,达到预期的结果。研究表明,以气象因子为自变量采用BP神经网络模型对赤潮的发生进行预测是可行的,该方法可为赤潮的预测提供新的途径。 相似文献
17.
该研究分别采用残差修正的GM(1,1)模型和灰色拓扑预测方法对2007—2017年春(5月)、秋(10月)海州湾人工鱼礁区的溶解氧(DO)、化学需氧量(COD)、生化需氧量(BOD5)及溶解无机氮(DIN)4个指标的监测数据建立水质预测模型并选择精度较高的模型预测2018—2022年的水质变化趋势,最终利用2018年的调查数据对预测结果进行检验。结果表明,灰色拓扑模型相较于残差修正的GM(1,1)模型针对水质数据具有更好的预测精度,预测结果与2018年的调查结果较吻合,可信度较高;预测结果显示,DO和COD在2018—2022年能够保持良好的水质状态,可见人工鱼礁建设对海域水环境状况具有一定的修复作用,但BOD5和DIN存在一定的超标风险;针对灰色拓扑模型的改进仍具有很大的研究空间,有待进一步挖掘。 相似文献
18.
《Journal Of Applied Aquaculture》2013,25(2):63-78
Oxygen transfer rates for mechanical aerators are usually determined by standard aeration tests conducted in concrete tanks. Results from standard tests are then extrapolated to field application of the aerators in aquaculture ponds, but these extrapolations have not been verified. In this study, oxygen transfer rates were estimated for propeller-aspirator-pump aerators deployed in brackish water aquaculture ponds under normal pond conditions. Estimation was accomplished by fitting the Whole Pond Respiration Diffusion (WPRD) model to nighttime observations of dissolved oxygen concentration for ponds in which artificial aeration had been initiated during the night. Application of this new technique revealed that oxygen transfer rate determined from a standard aeration test was similar to that estimated from trials in aquaculture ponds. Preliminary results suggest that oxygen transfer rates were higher when the aerator was placed in the deep end of the ponds than when the aerator was situated in the shallow end. Aeration efficiencies ranged from 1.45 to 1.8 kg O 2 per kW-hour. 相似文献
19.
水产养殖过程中,池塘生态系统可分为自成熟期和人工维持期。在养殖容量提高的情况下,养殖生物呼吸需氧量在不断增加,缺氧条件下有机物分解成有害物质,影响养殖生产。维持池塘生态系统稳定的主要工程机制为:通过上下水层交换、平衡营养元素等方法,强化光合作用,提高营养物质转化规模,提升初级生产力;形成生态增氧为主、机械增氧为辅的高效增氧机制。以中国养殖池塘生态系统为研究对象,分析探讨养殖池塘生态机制、水体溶氧理论、增氧机作用机理、不同类型增氧机的机械性能等,提出了大宗淡水鱼混养池塘及几种典型单养池塘增氧机配置方式,从而为池塘养殖系统增氧机的配置提供技术参考。 相似文献
20.
为实时掌握水产养殖水质和气象环境信息,针对溶氧控制过程中非线性、惯性大和时滞的问题,以循环水流水槽养殖模式为基础,设计了水产养殖环境监测和控制系统。通过PLC对养殖环境中溶氧、pH、温度、湿度、风速、风向、大气压等参数进行信息采集与传输,上位机实时显示环境信息,用模糊算法处理信息,处理后的结果作为PLC的输出传送到变频器中,变频器控制增氧机调节水中溶氧量。结果显示:该系统可实时传输与显示上述参数信息,提供历史数据和环境异常报警功能。模糊控制在调节溶氧过程中超调小、精度高,溶氧偏差±0.4 mg/L,可减少增氧机启停次数、延长设备寿命。监控系统进行实地应用测试,达到预期效果,可在水产养殖中进行推广和应用。 相似文献