Deep learning for water quality prediction: the application of LSTM model to predict water quality in catchment scale

2021 ◽  
Author(s):  
Amir Sahraei ◽  
Lutz Breuer ◽  
Philipp Kraft ◽  
Tobias Houska
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Mean Squared Error ◽  
Model Performance ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Quality Prediction ◽  
Water Quality Prediction

<p>The prediction of water quality is an efficient way for managing water resources and protecting ecosystems by providing an early warning against water quality deterioration. So far, the classical approach is to predict water quality by the utilization of complex process-based water quality models. However, these models are not easy to set up and require comprehensive input data. The local characteristics, detailed process understandings and eventually data from land users such as farmers are needed, to build up a valid model structure. Such constraints can end up in wrong scientific conclusions ranging from false alarms to unpredicted environmental pollution in practical water monitoring application. Long short-term memory (LSTM) algorithms are known to be able to overcome some of the typical constraints in hydrological model applications. However, their performance in water quality prediction has rarely been explored. In this study, we investigate the ability of a LSTM model to predict the complex, nonlinear behavior of water quality parameters in the Schwingbach Environmental Observatory (SEO), Germany.  We predict weekly nitrogen-nitrate concentrations, weekly stable isotopes of water concentrations (δ<sup>18</sup>O) and daily water temperature in six stream‑ and six groundwater sources with different landuse and hillslope conditions. We use meteorological forcing data and catchment attributes as input variables. To ensure an efficient model performance, we employ a Bayesian optimization approach to optimize the hyperparameters of the LSTM. The model performance is evaluated by the Root Mean Squared Error (RMSE). Our LSTM is robust in capturing the dynamics of the water quality parameters over time. The RMSE for the LSTM performance ranges from 0.27 to 3.38 mg/l, from 0.069 to 0.27 ‰ and from 1.3 to 2.1 °C for nitrogen‑nitrate, δ<sup>18</sup>O and water temperature, respectively. We compare the RMSE with statistical parameters of data. Results confirm that the LSTM is a promising tool for early risk assessment of water quality, particularly in view that only a minimal set of catchment information is needed to gain robust results.</p>

scholarly journals A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1420 ◽  
Author(s):  
Zhuhua Hu ◽  
Yiran Zhang ◽  
Yaochi Zhao ◽  
Mingshan Xie ◽  
Jiezhuo Zhong ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Prediction ◽  
Short Term ◽  
Water Quality Prediction ◽  
Term Prediction

An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson’s correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively.

scholarly journals Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1273
Author(s):  
Jianzhuo Yan ◽  
Jiaxue Liu ◽  
Yongchuan Yu ◽  
Hongxia Xu
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Prediction Algorithm ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Quality Prediction ◽  
Water Quality Data ◽  
Water Quality Prediction

The current global water environment has been seriously damaged. The prediction of water quality parameters can provide effective reference materials for future water conditions and water quality improvement. In order to further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new comprehensive deep learning water quality prediction algorithm. Firstly, the water quality data are cleaned and pretreated by isolation forest, the Lagrange interpolation method, sliding window average, and principal component analysis (PCA). Then, one-dimensional residual convolutional neural networks (1-DRCNN) and bi-directional gated recurrent units (BiGRU) are used to extract the potential local features among water quality parameters and integrate information before and after time series. Finally, a full connection layer is used to obtain the final prediction results of total nitrogen (TN), total phosphorus (TP), and potassium permanganate index (COD-Mn). Our prediction experiment was carried out according to the actual water quality data of Daheiting Reservoir, Luanxian Bridge, and Jianggezhuang at the three control sections of the Luan River in Tangshan City, Hebei Province, from 5 July 2018 to 26 March 2019. The minimum mean absolute percentage error (MAPE) of this method was 2.4866, and the coefficient of determination (R2) was able to reach 0.9431. The experimental results showed that the model proposed in this paper has higher prediction accuracy and generalization than the existing LSTM, GRU, and BiGRU models.

A Hierchcial Bayesian Model for Spatio-Temporal Water Quality Modeling in a Chainging Climate in South Korea

2020 ◽  
Author(s):  
Minkyu Jung ◽  
Hong-Geun Choi ◽  
Dinh Huy Nguyen ◽  
Hyun-Han Kwon
Keyword(s):  
Water Quality ◽  
South Korea ◽  
Bayesian Model ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Prediction ◽  
Hierarchical Bayesian ◽  
Modeling Framework ◽  
Water Quality Prediction ◽  
Spatio Temporal

<p>Contaminants that cause water pollution are generated from large areas and flow into rivers. It becomes difficult to obtain an accurate prediction of water quality due to the large spatio-temporal variability in a changing climate which in turn leads to considerable uncertainty in the estimation of water quality. Water quality over South Korea highly depends on hydrometeorological conditions due to distinct seasonality. In this context, we explored the use of hydrometeorological variables (i.e., precipitation and temperature) and the autocorrelation structure of water quality parameters in the water quality prediction model within a Bayesian modeling framework. More specifically, we analyzed explored the interdepedencies and correlations between hydrometeorological factors and the water quality parameters for the Mangyeong River basin, and built a hierarchical Bayesian regression model for the TN and TP which are main water quality paramters in South Korea. The result shows that the proposed modeling framework can capture the key aspects of the water quality paramters in terms of seasonality and their uncertainty.</p><p> </p><p>KEYWORDS: Hierarchical Bayesian Model, Meteorological factors, Water Quality prediction</p><p> </p><p>Acknowledgement</p><p>This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI(KMI2018-01215)</p>

scholarly journals A Bayesian Optimization Approach for Multi-Function Estimation for Environmental Monitoring Using an Autonomous Surface Vehicle: Ypacarai Lake Case Study

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 963
Author(s):  
Federico Peralta ◽  
Daniel Gutierrez Reina ◽  
Sergio Toral ◽  
Mario Arzamendia ◽  
Derlis Gregor
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Bayesian Optimization ◽  
Computational Time ◽  
Optimization Approach ◽  
Test Scenario ◽  
Sequential Method ◽  
Autonomous Surface Vehicle ◽  
Better Than

Bayesian optimization is a sequential method that can optimize a single and costly objective function based on a surrogate model. In this work, we propose a Bayesian optimization system dedicated to monitoring and estimating multiple water quality parameters simultaneously using a single autonomous surface vehicle. The proposed work combines different strategies and methods for this monitoring task, evaluating two approaches for acquisition function fusion: the coupled and the decoupled techniques. We also consider dynamic parametrization of the maximum measurement distance traveled by the ASV so that the monitoring system balances the total number of measurements and the total distance, which is related to the energy required. To evaluate the proposed approach, the Ypacarai Lake (Paraguay) serves as the test scenario, where multiple maps of water quality parameters, such as pH and dissolved oxygen, need to be obtained efficiently. The proposed system is compared with the predictive entropy search for multi-objective optimization with constraints (PESMOC) algorithm and the genetic algorithm (GA) path planning for the Ypacarai Lake scenario. The obtained results show that the proposed approach is 10.82% better than other optimization methods in terms of R2 score with noiseless measurements and up to 17.23% better when the data are noisy. Additionally, the proposed approach achieves a good average computational time for the whole mission when compared with other methods, 3% better than the GA technique and 46.5% better than the PESMOC approach.

scholarly journals Research and Design of Distributed IoT Water Environment Monitoring System Based on LoRa

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wei Chen ◽  
Xiao Hao ◽  
JianRong Lu ◽  
Kui Yan ◽  
Jin Liu ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Real Time ◽  
Monitoring System ◽  
Water Environment ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Environment Monitoring ◽  
Long Distance ◽  
Internet Of Things Technology

In order to solve the problems of high labor cost, long detection period, and low degree of information in current water environment monitoring, this paper proposes a lake water environment monitoring system based on LoRa and Internet of Things technology. The system realizes remote collection, data storage, dynamic monitoring, and pollution alarm for the distributed deployment of multisensor node information (water temperature, pH, turbidity, conductivity, and other water quality parameters). Moreover, the system uses STM32L151C8T6 microprocessor and multiple types of water quality sensors to collect water quality parameters in real time, and the data is packaged and sent to the LoRa gateway remotely by LoRa technology. Then, the gateway completes the bridging of LoRa link to IP link and forwards the water quality information to the Alibaba Cloud server. Finally, end users can realize the water quality control of monitored water area by monitoring management platform. The experimental results show that the system has a good performance in terms of real-time data acquisition accuracy, data transmission reliability, and pollution alarm success rate. The average relative errors of water temperature, pH, turbidity, and conductivity are 0.31%, 0.28%, 3.96%, and 0.71%, respectively. In addition, the signal reception strength of the system within 2 km is better than -81 dBm, and the average packet loss rate is only 94%. In short, the system’s high accuracy, high reliability, and long distance characteristics meet the needs of large area water quality monitoring.

Monitoring Water Resources through a Bayesian Optimization-based Approach using Multiple Surface Vehicles: The Ypacarai Lake Case Study

2021 ◽  
Author(s):  
Federico Peralta Samaniego ◽  
Sergio Toral Marín ◽  
Daniel Gutierrez Reina
Keyword(s):  
Water Quality ◽  
Mean Squared Error ◽  
Bayesian Optimization ◽  
Water Quality Parameter ◽  
Optimization Approach ◽  
Decision Strategy ◽  
Sequential Decision ◽  
Partition System ◽  
Autonomous Surface Vehicle ◽  
Voronoi Partition

<div>Bayesian optimization is a popular sequential decision strategy that can be used for environmental monitoring. In this work, we propose an efficient multi-Autonomous Surface Vehicle system capable of monitoring the Ypacarai Lake (San Bernardino, Paraguay) (60 km<sup>2</sup>) using the Bayesian optimization approach with a Voronoi Partition system. The system manages to quickly approximate the real unknown distribution map of a water quality parameter using Gaussian Processes as surrogate models. Furthermore, to select new water quality measurement locations, an acquisition function adapted to vehicle energy constraints is used. Moreover, a Voronoi Partition system helps to distributing the workload with all the available vehicles, so that robustness and scalability is assured. For evaluation purposes, we use both the mean squared error and computational efficiency. The results showed that our method manages to efficiently monitor the Ypacarai Lake, and also provides confident approximate models of water quality parameters. It has been observed that, for every vehicle, the resulting surrogate model improves by 38%.</div>

Evaluating statistical model performance in water quality prediction

2018 ◽  
Vol 206 ◽  
pp. 910-919 ◽  
Author(s):  
Rodelyn Avila ◽  
Beverley Horn ◽  
Elaine Moriarty ◽  
Roger Hodson ◽  
Elena Moltchanova
Keyword(s):  
Water Quality ◽  
Statistical Model ◽  
Model Performance ◽  
Quality Prediction ◽  
Water Quality Prediction

scholarly journals Assessment of Quality Parameters of a Pond Water: A Limnological Case Study in Bareilly District

2021 ◽  
Vol 07 (08) ◽  
Author(s):  
Vikas Jain ◽  
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Pond Water ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Hardness ◽  
Standard Procedures ◽  
Calcium And Magnesium

The manuscript herewith presents the assessment of water quality parameters in the samples drawn in year 2014-15 from Akshar Vihar pond, located centrally in district Bareilly (U.P.), India. Analysis of check parameters chosen, was performed by employing standard procedures laid down in APHA. The minimum to maximum values recorded in each month of the experimental year for pH, total hardness, DO, BOD, COD, calcium and magnesium were 7.2-8.8, 380 - 486mg/L, 4.2-10.6 mg/L, 1.0-1.6 mg/L, 3.8-8.4 mg/L, 52.97-74.84 mg/L and 56.74-72.98 mg/L respectively. Significant correlation was observed for COD with pH (0.816), carbonate (0.875) and bicarbonate (0.927); that of total hardness with magnesium (0.954) as well as of DO inversely with water temperature (-0.821).

Investigation into the Trichoptera fauna of the Opa Stream in Ile-Ife, southwestern Nigeria

Zoosymposia ◽  
2016 ◽  
Vol 10 (1) ◽  
pp. 85-90
Author(s):  
BABATUNDE AMUSAN ◽  
SYLVESTER OGBOGU
Keyword(s):  
Water Quality ◽  
Species Composition ◽  
Water Temperature ◽  
Negative Relationship ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Southwestern Nigeria ◽  
Dry Seasons ◽  
Wet And Dry Seasons

The species composition and abundance of caddisflies in association with some water quality parameters (pH, water temperature and conductivity) in Opa Stream in Ile-Ife, Nigeria were investigated during October 2009–August 2010. One hundred and ninety adult caddisflies collected from the stream represent six species in six genera and three families. Hydropsychidae had three species, which is more than were found in other families. The caddisflies showed a relative mean abundance of 62% and 38.9% in the wet and dry seasons, respectively. Caddisfly abundance was positively correlated with pH and conductivity but there was a negative relationship between water temperature and the abundance of caddisflies in the stream.

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