Application of Time Serial Model in Water Quality Predicting

Wu, Jiang; Zhang, Jianjun; Tan, Wenwu; Lan, Hao; Zhang, Sirao; Xiao, Ke; Wang, Li; Lin, Haijun; Sun, Guang; Guo, Peng

doi:10.32604/cmc.2023.030703

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…Absolute error was used in the perception of models by comparing the real data with the prediction model's result for 7 days. Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE) are commonly used to assess the predictive accuracy of a model [24]. Among them, MAE better reflects the actual situation of the prediction error value.…”

Section: Methodology 21 Prediction Of Water Quality Parametersmentioning

confidence: 99%

IoT-based Machine Learning Comparative Models of Stream Water Parameters Forecasting for Freshwater Lobster

Abdelmoneim A. Bakhit,

Nur Syahirah Mohd Sabli,

Mohd Faizal Jamlos

et al. 2024

ARAM

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Water quality parameters such as dissolved oxygen, pH, and mineral content are important factors for aquaculture. Predictive analytics can predict water conditions in aquaculture and significantly reduce the mortality probability of aquaculture products. This paper applied stream predictive analytics to the freshwater lobster farming dataset where its real-time data supplied by End Node Unit (ENU) which integrated with dissolved oxygen (DO), potential hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS). The real-time data of ENU in Structured Query Language (SQL) is normally displayed for remote monitoring and the analytics will only be done after in different processing platform called batch analytics. Instead of batch, this paper demonstrates capability of stream analytics where the real-time data query from ENU streaming through Structured Query Language (SQL) right into R Studio and Autoregressive Integrated Moving Average (ARIMA) predictions executed on the query table simultaneously on the same processing platform. Previously, ARIMA, Neural Network Autoregressive (NNETAR), and Naïve Bayes, were run and evaluated in R Studio to identify the best algorithm for stream analytics. Prediction procedure in R studio start with importing real-time data stored in SQL database and stream into R Studio using command of “dbGetQuery(con,sql)”. These three models evaluated the performance of freshwater lobster water conditions, dissolved oxygen (DO), potential hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS). The data was collected for six months, and 70% was used as training data and 30% as test data. Compared to NNETAR and Naïve Bayes, ARIMA fits the entire data set well for 7 days; the ARIMA model exhibited lower absolute errors for pH and electrical conductivity, with errors ranging from 0.04 to 1.7 across days, while the NNETAR model had generally lower errors for TDS, with errors ranging from 0.3 to 0.7; however, the Naïve Bayes model's performance varied, with the lowest error for DO on day (5) 0.15 but higher errors for other parameters and days, including the highest error for electrical conductivity on day (6) 6.2. In conclusion, the average absolute errors for DO, pH, EC, and TDS are 0.163, 0.064, 0.705, and 0.498, respectively. Our findings underscore the efficacy of ARIMA for comprehensive water quality via stream prediction while highlighting the nuanced strengths and weaknesses of each model in forecasting specific parameters. This study contributes to the aquaculture literature by providing a nuanced comparative analysis of predictive models tailored to freshwater lobster farming, emphasizing the imperative role of stream predictive modelling. It enables real-time monitoring of water quality parameters, ensuring prompt interventions to maintain optimal conditions, thereby minimizing risks, enhancing aquaculture productivity, and ultimately contributing to sustainable and efficient freshwater lobster farming practices.

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Section: Methodology 21 Prediction Of Water Quality Parametersmentioning

confidence: 99%

IoT-based Machine Learning Comparative Models of Stream Water Parameters Forecasting for Freshwater Lobster

Abdelmoneim A. Bakhit,

Nur Syahirah Mohd Sabli,

Mohd Faizal Jamlos

et al. 2024

ARAM

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“…These parameters strike a balance between prediction accuracy and the stability of the original sequence. In addition, previous studies have also suggested that a prediction horizon of 10 s is beneficial for PEMS [25]. Therefore, it is reasonable to limit the prediction horizon to 10 s.…”

Section: Determination Of Structural Parametersmentioning

confidence: 99%

“…From the results depicted in Figure 9, it can be observed that, regardless of the prediction method used, the prediction accuracy decreases significantly as the prediction horizon increases. The accuracy drops sharply when the prediction horizon exceeds 10 s. In addition, previous studies have also suggested that a prediction horizon of 10 s is beneficial for PEMS [25]. Therefore, it is reasonable to limit the prediction horizon to 10 s. Taking into account considerations such as code execution efficiency and calculation time, the recommended structure parameters are as follows: sample size of 500, d ranging from 2 to 4, and p ranging from 2 to 4.…”

Section: Determination Of Structural Parametersmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Online Prediction Method for Vehicle Velocity and Road Gradient Based on a Flexible-Structure Auto-Regressive Integrated Moving Average Model

Ma,

Li,

Guo

et al. 2023

Sustainability

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The auto-regressive integrated moving average (ARIMA) model has shown promise in predicting vehicle velocity and road gradient (V–G) for the purpose of constructing power demands in predictive energy management strategies (PEMS) for electric vehicles (EVs). It offers flexibility, accuracy, and computational efficiency. However, the performance of a conventional ARIMA model with fixed structure parameters can be disappointing when the data fluctuate. To overcome this limitation, a novel and flexible-structure-based ARIMA (FS–ARIMA) is proposed in this paper to improve online prediction performance. First, the sliding window method was developed to produce fitting data in real time based on real local historical data, reducing the online computation time. Secondly, the influence of the sliding window sample size, differencing order, and lag in the model on the prediction accuracy was investigated. Based on this, an FS–ARIMA was proposed to improve the prediction accuracy, where an augmented Dickey–Fuller (ADF) test was developed to select the differencing order in real time and the Bayesian information criterion (BIC) was applied to update the model and determine its lag under an optimal sample size. Lastly, to validate the proposed FS–ARIMA, simulations were conducted using two typical driving cycles collected via experiments, as well as the following three typical driving cycles: the New European Driving Cycle (NEDC), the Urban Dynamometer Driving Schedule (UDDS), and the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The results demonstrated that FS–ARIMA improved prediction accuracy by approximately 41.63% and 42.19% for the velocity and gradient, respectively. The proposed FS–ARIMA prediction model has potential applications in predictive energy management strategies for EVs.

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“… 5 , 6 . Jiang Wu et al 7 proposed a water quality prediction method combining ARIMA and clustering model, and taking the water quality monitoring data of a basin as a sample, the total phosphorus (TP) index of water quality was selected as the prediction object, and the water quality change in the basin was successfully predicted. Mohamed Elhag et al 8 used the adjusted ARIMA and SARIMA models to predict water quality parameters, and verified that the SARIMA model could effectively predict water quality parameters with seasonal characteristics.…”

Section: Introductionmentioning

confidence: 99%

Application of hybrid improved temporal convolution network model in time series prediction of river water quality

Wang

et al. 2023

Sci Rep

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Time series prediction of river water quality is an important method to grasp the changes of river water quality and protect the river water environment. However, due to the time series data of river water quality have strong periodicity, seasonality and nonlinearity, which seriously affects the accuracy of river water quality prediction. In this paper, a new hybrid deep neural network model is proposed for river water quality prediction, which is integrated with Savitaky-Golay (SG) filter, STL time series decomposition method, Self-attention mechanism, and Temporal Convolutional Network (TCN). The SG filter can effectively remove the noise in the time series data of river water quality, and the STL technology can decompose the time series data into trend, seasonal and residual series. The decomposed trend series and residual series are input into the model combining the Self-attention mechanism and TCN respectively for training and prediction. In order to verify the proposed model, this study uses opensource water quality data and private water quality data to conduct experiments, and compares with other water quality prediction models. The experimental results show that our method achieves the best prediction results in the water quality data of two different rivers.

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Application of Time Serial Model in Water Quality Predicting

Cited by 11 publications

References 9 publications

IoT-based Machine Learning Comparative Models of Stream Water Parameters Forecasting for Freshwater Lobster

IoT-based Machine Learning Comparative Models of Stream Water Parameters Forecasting for Freshwater Lobster

A Novel Online Prediction Method for Vehicle Velocity and Road Gradient Based on a Flexible-Structure Auto-Regressive Integrated Moving Average Model

Application of hybrid improved temporal convolution network model in time series prediction of river water quality

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