Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm
Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm
复制标题
DOI：
10.3390/w13131782
复制
发表时间：
2021-07-01
期刊：
影响因子：
3.4
通讯作者：
Ajmal, Tahmina
中科院分区：
文献类型：
--
作者：
Eze, Elias;Halse, Sarah;Ajmal, Tahmina
提供水质参数的准确预测以改善水质管理是水产养殖业的一个热点问题。传统的预测方法表现出不同的挑战，如泛化能力差，预测精度差，时间复杂度高。针对这些挑战，提出了一种基于集成经验模式分解（EEMD）和深度学习（DL）长短期记忆（LSTM）神经网络的混合预测模型。在这种创新的混合EEMD-DL-LSTM模型中，首先，通过应用水质参数数据集预处理的移动平均滤波和线性插值技术来增强数据集的完整性。其次，测量的真实的传感器水质参数数据集的EEMD算法的帮助下分解成不同的IMF和相应的残差项。第三，应用多特征选择过程来仔细选择与测量的真实的水质参数数据集强相关的IMF组，并将它们作为输入集成到DL-LSTM神经网络。所提出的模型是建立在从南非鲍鱼养殖场收集的水质传感器数据。通过与真实的数据集的比较，验证了新的混合预测模型的性能。为了衡量新的混合预测模型的整体精度，不同的统计指标，即平均绝对误差（MAE），均方误差（MSE），均方根误差（RMSE），平均绝对百分比误差（MAPE），被使用。
Providing an accurate prediction of water quality parameters for improved water quality management is a topical issue in the aquaculture industry. Conventional prediction methods have shown different challenges like a poor generalization, poor prediction accuracy, and high time complexity. Aiming at these challenges, a novel hybrid prediction model with ensemble empirical mode decomposition (EEMD) and deep learning (DL) long-short term memory (LSTM) neural network is proposed in this paper. In this innovative hybrid EEMD-DL-LSTM model, firstly, the integrity of the datasets is enhanced by applying moving average filtering and linear interpolation techniques of water quality parameter datasets pre-treatment. Secondly, the measured real sensor water quality parameters dataset is decomposed with the aid of the EEMD algorithm into disparate IMFs and a corresponding residual item. Thirdly, a multi-feature selection process is applied to make a careful selection of a strongly correlated group of IMFs with the measured real water quality parameter datasets and integrate them as inputs to the DL-LSTM neural network. The presented model is built on water quality sensor data collected from an Abalone farm in South Africa. The performance of the novel hybrid prediction model is validated by comparing the results against the real datasets. To measure the overall accuracy of the novel hybrid prediction model, different statistical indices, namely the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), are used.