Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
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DOI：
10.3390/app10207079
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发表时间：
2020-10-01
影响因子：
2.7
通讯作者：
Ajmal, Tahmina
中科院分区：
文献类型：
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作者：
Eze, Elias;Ajmal, Tahmina
溶解氧(DO)浓度是表征水质的重要参数。本文利用时间序列分析方法对一个养殖池塘采集的数据进行短期预测。这可以为预警系统提供数据支持，为改善水产养殖场的管理提供依据。传统的预测方法普遍存在预测精度低、泛化能力差的问题。提出了一种基于集成经验模式分解(EEMD)的LSTM(Long Short-Term Memory)神经网络混合DO浓度预测方法。该方法首先通过数据预处理的线性插值法和滑动平均滤波法提高传感器数据的完整性。然后，应用EEMD算法将原始传感器数据分解为多个固有模式函数(IMF)。最后，特征选择被用来仔细地选择与原始传感器数据强烈相关的IMF，并将其整合到神经网络的两个输入中。然后构建了基于EEMD的混合LSTM预测模型。将该模型在训练集和验证集上的性能与实际传感器数据进行了比较。为了对基于EEMD的混合LSTM预测模型的预测结果进行准确的评估，采用了4个统计性能指标：平均绝对误差(MAE)、均方误差(MSE)、均方根误差(RMSE)和平均绝对百分比误差(MAPE)。短期(12小时)和长期(1个月)的结果令人鼓舞，表明该技术适用于预测DO值。
Dissolved oxygen (DO) concentration is a vital parameter that indicates water quality. We present here DO short term forecasting using time series analysis on data collected from an aquaculture pond. This can provide the basis of data support for an early warning system, for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD)-based LSTM (long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that strongly correlate with the original sensor data, and integrate into both inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. The performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, four statistical performance indices were adopted: mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results are presented for the short term (12-h) and the long term (1-month) that are encouraging, indicating suitability of this technique for forecasting DO values.