Daily and hourly prediction of DO concentration using machine learning algorithm

2020 ◽  
Author(s):  
heesung lim ◽  
hyunuk an
Keyword(s):  
Water Quality ◽  
Target Location ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Water Quality Management ◽  
Practical Aspect ◽  
Weather Data ◽  
Sequence Length ◽  
Water Quality Prediction ◽  
Do Concentration

<p>In order to perform adequate water quality management, it is important to predict the water quality through measurement and data accumulation of the concentration of contaminants. However, daily measurement of water quality pollutant is unrealistic in practical aspect. In this study, the possibility of daily- or hourly-based water quality prediction through dissolved oxygen (DO) using RNN-LSTM (Recurrent Neural Network-Long Short-term Memory) algorithm, which is well-known for time-series learning, was performed. The research selected Bugok Bridge in Oncheon-stream, Busan, South Korea. Hourly-based DO, temperature, wind speed, relative humidity, rainfall data was collected at the target location and was converted to daily data. To forecast the DO concentration, TensorFlow, a deep learning open source library developed by Google, was utilized. Data of four years (2014-2017) was used for daily learning data and 2018 data was used for verification of the trained model. The performance with the adjusted number of hidden layers, number of repetitions, and the sequence length, as well as the accuracy of the model was analyzed. As a result of this research, it is proven that the performance of the prediction can be improved when weather data and large amount of data is available.</p>

scholarly journals Water Quality Prediction Using Artificial Intelligence Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Theyazn H. H Aldhyani ◽  
Mohammed Al-Yaari ◽  
Hasan Alkahtani ◽  
Mashael Maashi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Water Quality ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Neural Network Models ◽  
Statistical Parameters ◽  
Water Quality Prediction

During the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI) and water quality classification (WQC). For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM), K -nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. The used dataset has 7 significant parameters, and the developed models were evaluated based on some statistical parameters. The results revealed that the proposed models can accurately predict WQI and classify the water quality according to superior robustness. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values and the SVM algorithm has achieved the highest accuracy (97.01%) for the WQC prediction. Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient ( RNARNET = 96.17 % and RLSTM = 94.21 % ). This kind of promising research can contribute significantly to water management.

scholarly journals A prediction model of aquaculture water quality based on multiscale decomposition

2021 ◽  
Vol 18 (6) ◽  
pp. 7561-7579
Author(s):  
Huanhai Yang ◽  
◽  
Shue Liu ◽  
◽  
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Prediction Model ◽  
Short Term Memory ◽  
Normal Growth ◽  
Original Data ◽  
Quality Data ◽  
Water Quality Data ◽  
Water Quality Prediction ◽  
Aquaculture Water

<abstract><p>In the field of intensive aquaculture, the deterioration of water quality is one of the main factors restricting the normal growth of aquatic products. Predicting water quality in real time constitutes the theoretical basis for the evaluation, planning and intelligent regulation of the aquaculture environment. Based on the design principles of decomposition, recombination and integration, this paper constructs a multiscale aquaculture water quality prediction model. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the different water quality variables at different time scales step by step to generate a series of intrinsic mode function (IMF) components with the same characteristic scale. Then, the sample entropy of each IMF component is calculated, the components with similar sample entropies are combined, and the original data are recombined into several subsequences through the above operations. In this paper, a prediction model based on a long short-term memory (LSTM) neural network is constructed to predict each recombination subsequence, and the Adam optimization algorithm is used to continuously update the weight of neural network to train and optimize the prediction performance. Finally, the predicted value of each subsequence is superimposed to predict the original water quality data. The dissolved oxygen and pH data of an aquaculture base were collected for prediction experiments, the results of which show that the proposed model has a high prediction accuracy and strong generalization performance.</p></abstract>

scholarly journals Water Quality Prediction Method Based on IGRA and LSTM

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1148 ◽  
Author(s):  
Jian Zhou ◽  
Yuanyuan Wang ◽  
Fu Xiao ◽  
Yunyun Wang ◽  
Lijuan Sun
Keyword(s):  
Water Quality ◽  
Short Term Memory ◽  
Water Environment ◽  
Prediction Method ◽  
Time Sequence ◽  
Quality Information ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Single Feature ◽  
Grey Relational

Water quality prediction has great significance for water environment protection. A water quality prediction method based on the Improved Grey Relational Analysis (IGRA) algorithm and a Long-Short Term Memory (LSTM) neural network is proposed in this paper. Firstly, considering the multivariate correlation of water quality information, IGRA, in terms of similarity and proximity, is proposed to make feature selection for water quality information. Secondly, considering the time sequence of water quality information, the water quality prediction model based on LSTM, whose inputs are the features obtained by IGRA, is established. Finally, the proposed method is applied in two actual water quality datasets: Tai Lake and Victoria Bay. Experimental results demonstrate that the proposed method can take full advantage of the multivariate correlations and time sequence of water quality information to achieve better performance on water quality prediction compared with the single feature or non-sequential prediction methods.

scholarly journals Comparison of Long Short-Term Memory and Weighted Regressions on Time, Discharge, and Season Models for Nitrate-N Load Estimation

2020 ◽  
Vol 12 (15) ◽  
pp. 5942
Author(s):  
Kichul Jung ◽  
Myoung-Jin Um ◽  
Momcilo Markus ◽  
Daeryong Park
Keyword(s):  
Water Quality ◽  
Short Term Memory ◽  
Water Quality Management ◽  
Model Performance ◽  
The United States ◽  
Percentage Error ◽  
Short Term ◽  
Term Memory ◽  
Nitrate N ◽  
Long Short Term Memory

The long short-term memory (LSTM) model has been widely used for a broad range of applications entailing the estimation of variables in different fields to improve water quality management in rivers. The main objectives of this study are (1) to develop a novel LSTM-based model for the estimation of nitrate-N loads, which adversely affect water resources, and (2) to evaluate the performance of the model by comparing it with that of Monte Carlo sub-sampling and the weighted regressions on time discharge and season (WRTDS) model. We evaluated the model performance using various numbers of hidden layers, ranging from one to four, in the LSTM model to determine the appropriate number of hidden layers; furthermore, we applied the sampling frequencies of 6, 12, and 24 to assess their impact. Seven polluted river basins in the United States were used for analysis, and the relative root mean squared error (rRMSE) and the mean percentage error (MPE) metrics were applied for the validation of the model estimates. The proposed model achieved accurate nitrate-N load estimates using three to four hidden layers, and improved model performance was observed when the sampling frequency was increased. The differences among the results obtained using the LSTM model were examined based on a binning technique via a log-log plot of nitrate-N concentration against discharge. The binning analysis showed that the slope obtained from the average rates of discharge and low discharge values apparently influenced the estimates. Furthermore, box plot analyses of the statistical indices such as rRMSE and MPE demonstrate that the LSTM model seems to exhibit better performance than the WRTDS model. The results of the examination demonstrate that the LSTM model may be a good alternative with regard to estimating nitrate-N loads for the control of water quality constituents.

scholarly journals Prediction of DO Concentration in Nakdong River Estuary through Case Study Based on Long Short Term Memory Model

2021 ◽  
Vol 33 (6) ◽  
pp. 238-245
Author(s):  
Seongsik Park ◽  
Kyunghoi Kim
Keyword(s):  
Dissolved Oxygen ◽  
Short Term Memory ◽  
River Estuary ◽  
Sequence Length ◽  
Nakdong River ◽  
Model Parameter ◽  
Study Results ◽  
Model Condition ◽  
Do Concentration

In this study, we carried out case study to predict dissolved oxygen (DO) concentration of Nakdong river estuary with LSTM model. we aimed to figure out a optimal model condition and appropriate predictor for prediction in dissolved oxygen concentration with model parameter and predictor as cases. Model parameter case study results showed that Epoch = 300 and Sequence length = 1 showed higher accuracy than other conditions. In predictor case study, it was highest accuracy where DO and Temperature were used as a predictor, it was caused by high correlation between DO concentration and Temperature. From above results, we figured out an appropriate model condition and predictor for prediction in DO concentration of Nakdong river estuary.

Machine learning-based tools for water digitalisation

2021 ◽  
Author(s):  
Asma Slaimi ◽  
Susan Hegarty ◽  
Fiona Regan ◽  
Michael Scriney ◽  
Noel O’Connor
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Data Warehouse ◽  
New Technologies ◽  
Short Term Memory ◽  
Water Quality Management ◽  
Water Levels ◽  
Quality Data ◽  
Quality Information ◽  
Water Quality Data

&lt;p&gt;Advanced technologies have proven to deliver significant outcomes in the water management sector. New technologies provide the capability to collect and correlate the information from remote devices, introducing smart tools that can leverage augmented intelligence for interpreting structured and unstructured, text-based or sensory data. However, most of the single feature or non-sequential prediction machine learning methods for understanding water quality achieve poor results due to the fact that water quality information exists in the form of multivariate time-series datasets.&lt;/p&gt;&lt;p&gt;At the catchment scale, there are many layers where relevant data needs to be measured and captured. For that, data warehouses play an essential role in decision support systems as they provide adequate information.&amp;#160;&lt;/p&gt;&lt;p&gt;In this paper, we started by extracting, transforming, cleaning and consolidating data from several data sources into a data warehouse. Then, the data in the warehouse was used to develop a computer tool to predict river water level using Artificial Neural Networks (ANNs), in particular, Long Short-Term Memory networks (LSTM). As the prediction performance is significantly affected by the model inputs, the feature selection step, which considers the multivariate correlation of water quality information in terms of similarity and proximity, is particularly important. The features obtained from the previous steps are the inputs to the prediction model based on LSTM, which naturally takes the time sequence of water quality information into account.&lt;/p&gt;&lt;p&gt;The proposed method is applied to two different catchments in the island of Ireland. Experimental results indicate that our model provides accurate predictions for water levels and is a useful supportive tool for water quality management.&amp;#160;&lt;/p&gt;&lt;p&gt;Ultimately, digitised representations of water environments will guarantee situational awareness of water flow and quality monitoring. The digitalisation of water is no longer optional but a necessity to solve many of the challenges faced by the water industry.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; Water digitalisation, water quality, data warehouse, machine learning, predictive model, LSTM.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;

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