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Zamani MG, Nikoo MR, Al-Rawas G, Nazari R, Rastad D, Gandomi AH. Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 358:120756. [PMID: 38599080 DOI: 10.1016/j.jenvman.2024.120756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 03/09/2024] [Accepted: 03/22/2024] [Indexed: 04/12/2024]
Abstract
Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms-namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)-alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir. Moreover, to optimize the outcomes of the developed hybrid model, we considered the impact of a decomposition technique, specifically the wavelet transform (WT). In addition to these efforts, we created two distinct machine learning (ML) algorithms-namely, random forest (RF) and support vector regression (SVR)-to demonstrate the superior performance of deep learning algorithms over individual ML ones. We initially gathered WQIs from diverse locations and varying depths within the reservoir using an AAQ-RINKO device in the study area to achieve this. It is important to highlight that, despite utilizing diverse data-driven models in water quality estimation, a significant gap persists in the existing literature regarding implementing a comprehensive hybrid algorithm. This algorithm integrates the wavelet transform, convolutional neural network (CNN), and gated recurrent unit (GRU) methodologies to estimate WQIs accurately within a spatiotemporal framework. Subsequently, the effectiveness of the models that were developed was assessed utilizing various statistical metrics, encompassing the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) throughout both the training and testing phases. The findings demonstrated that the WT-CNN-GRU model exhibited better performance in comparison with the other algorithms by 13% (SVR), 13% (RF), 9% (CNN), and 8% (GRU) when R-squared and DO were considered as evaluation indices and WQIs, respectively.
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Affiliation(s)
- Mohammad G Zamani
- Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.
| | - Mohammad Reza Nikoo
- Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.
| | - Ghazi Al-Rawas
- Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.
| | - Rouzbeh Nazari
- Department of Civil, Construction, and Environmental Engineering, The University of Alabama, Alabama, USA.
| | - Dana Rastad
- Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran.
| | - Amir H Gandomi
- Department of Engineering and I.T., University of Technology Sydney, Ultimo, NSW, 2007, Australia; University Research and Innovation Center (EKIK), Óbuda University, 1034, Budapest, Hungary.
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Ketabchy M, Buell EN, Yazdi MN, Sample DJ, Behrouz MS. The effect of piping stream channels on dissolved oxygen concentration and ecological health. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:460. [PMID: 36899153 DOI: 10.1007/s10661-023-11070-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
Sunlight plays a key role in the nutrient cycle within streams. Streams are often piped to accommodate urban residential or commercial development for buildings, roads, and parking. This results in altered exposure to sunlight, air, and soil, subsequently affecting the growth of aquatic vegetation, reducing reaeration, and thus impairing the water quality and ecological health of streams. While the effects of urbanization on urban streams, including changing flow regimes, stream bank and bed erosion, and degraded water quality, are well understood, the effects of piping streams on dissolved oxygen (DO) concentrations, fish habitats, reaeration, photosynthesis, and respiration rates are not. We addressed this research gap by assessing the effects of stream piping on DO concentrations before and after a 565-m piped section of Stroubles Creek in Blacksburg, VA, for several days during the summer of 2021. Results indicate that the DO level decreased by approximately 18.5% during daylight hours as water flowed through the piped section of the creek. Given the optimum DO level (9.0 mg·L-1) for brook trout (Salvelinus sp.), which are native and present in a portion of Stroubles Creek, the resulting DO deficits were - 0.49 and - 1.24 mg·L-1, for the inlet and outlet, respectively, indicating a possible adverse impact from piping the stream on trout habitat. Photosynthesis and respiration rates were reduced through the piped section, primarily due to the reduced solar radiation and the resultant reduction in oxygen production from aquatic vegetation; however, the reaeration rate increased. This study can inform watershed restoration efforts, particularly decisions regarding stream daylighting with respect to potential water quality and aquatic habitat benefits.
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Affiliation(s)
- Mehdi Ketabchy
- Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, USA
- Roadway Business Line, Gannett Fleming, Inc., Baltimore, MD, USA
| | - Elyce N Buell
- Department of Biological System Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Mohammad Nayeb Yazdi
- Department of Biological System Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
- School of Environment and Natural Resources, Ohio State University, Wooster, OH, USA
| | - David J Sample
- Department of Biological System Engineering, Hampton Roads Agricultural Research and Extension Center, Virginia Polytechnic Institute and State University, 1444 Diamond Springs Rd, VA, 23455, VA Beach, USA.
| | - Mina Shahed Behrouz
- Department of Biological System Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
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Strokal M, Strokal V, Kroeze C. The future of the Black Sea: More pollution in over half of the rivers. AMBIO 2023; 52:339-356. [PMID: 36074247 PMCID: PMC9453707 DOI: 10.1007/s13280-022-01780-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 06/24/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
The population in the Black Sea region is expected to decline in the future. However, a better understanding of how river pollution is affected by declining trends in population and increasing trends in economic developments and urbanization is needed. This study aims to quantify future trends in point-source emissions of nutrients, microplastics, Cryptosporidium, and triclosan to 107 rivers draining into the Black Sea. We apply a multi-pollutant model for 2010, 2050, and 2100. In the future, over half of the rivers will be more polluted than in 2010. The population in 74 sub-basins may drop by over 25% in our economic scenario with poor wastewater treatment. Over two-thirds of the people will live in cities and the economy may grow 9-fold in the region. Advanced wastewater treatment could minimize trade-offs between economy and pollution: our Sustainability scenario projects a 68-98% decline in point-source pollution by 2100. Making this future reality will require coordinated international efforts.
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Affiliation(s)
- Maryna Strokal
- Water Systems and Global Change, Wageningen University & Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, The Netherlands
| | - Vita Strokal
- National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony 15, Kiev, 03041 Ukraine
| | - Carolien Kroeze
- Water Systems and Global Change, Wageningen University & Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, The Netherlands
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Chi D, Huang Q, Liu L. Dissolved Oxygen Concentration Prediction Model Based on WT-MIC-GRU—A Case Study in Dish-Shaped Lakes of Poyang Lake. ENTROPY 2022; 24:e24040457. [PMID: 35455119 PMCID: PMC9032188 DOI: 10.3390/e24040457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/21/2022] [Accepted: 03/22/2022] [Indexed: 01/27/2023]
Abstract
Dissolved oxygen concentration has the characteristics of nonlinearity, time series and instability, which increase the difficulty of accurate prediction. In order to accurately predict the dissolved oxygen concentration in the dish-shaped lakes in Poyang Lake of Jiangxi Province, China, a dissolved oxygen concentration prediction model, based on wavelet transform (WT)-based denoising, maximal information coefficient (MIC)-based feature selection, and the gated recurrent unit (GRU), was proposed for this study. In experiments, the proposed model showed good prediction performance, achieving a root-mean-square error (RMSE) of 0.087 mg/L, a mean absolute percentage error (MAPE) of 0.723%, and a coefficient of determination (R2) as high as 0.998. It shows that the prediction model based on the combination of the wavelet transform and the GRU has a relatively high prediction accuracy and a better fitting effect. The model proposed in this study can provide a reference for protecting this type of lake-water body and the restoration of missing values in lake water quality monitoring data.
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Affiliation(s)
- Dianwei Chi
- School of Artificial Intelligence, Yantai Institute of Technology, Yantai 264003, China;
| | - Qi Huang
- Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
- Key Laboratory of Watershed Eco-Geological Processes, Ministry of Natural Resources, Nanjing 210016, China
- Correspondence:
| | - Lizhen Liu
- Institute of Microbiology, Jiangxi Academy of Sciences, Nanchang 330096, China;
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Ziyad Sami BF, Latif SD, Ahmed AN, Chow MF, Murti MA, Suhendi A, Ziyad Sami BH, Wong JK, Birima AH, El-Shafie A. Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan. Sci Rep 2022; 12:3649. [PMID: 35256619 PMCID: PMC8901922 DOI: 10.1038/s41598-022-06969-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 01/24/2022] [Indexed: 11/09/2022] Open
Abstract
Water quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an important concern in the Fei-Tsui reservoir for decades since it's the primary water source for Taipei City. Therefore, this study aims to develop a reliable prediction model to predict D.O. in the Fei-Tsui reservoir for better water quality monitoring. The proposed model is an artificial neural network (ANN) with one hidden layer. Twenty-nine years of water quality data have been used to validate the accuracy of the proposed model. A different number of neurons have been investigated to optimize the model's accuracy. Statistical indices have been used to examine the reliability of the model. In addition to that, sensitivity analysis has been carried out to investigate the model's sensitivity to the input parameters. The results revealed the proposed model capable of capturing the dissolved oxygen's nonlinearity with an acceptable level of accuracy where the R-squared value was equal to 0.98. The optimum number of neurons was found to be equal to 15-neuron. Sensitivity analysis shows that the model can predict D.O. where four input parameters have been included as input where the d-factor value was equal to 0.010. This main achievement and finding will significantly impact the water quality status in reservoirs. Having such a simple and accurate model embedded in IoT devices to monitor and predict water quality parameters in real-time would ease the decision-makers and managers to control the pollution risk and support their decisions to improve water quality in reservoirs.
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Affiliation(s)
- Balahaha Fadi Ziyad Sami
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia
| | - Sarmad Dashti Latif
- Civil Engineering Department, College of Engineering, Komar University of Science and Technology, Sulaimany, Kurdistan Region, 46001, Iraq
| | - Ali Najah Ahmed
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia
| | - Ming Fai Chow
- Discipline of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.
| | | | - Asep Suhendi
- School of Electrical Engineering, Telkom University, Bandung, Indonesia
| | - Balahaha Hadi Ziyad Sami
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia
| | - Jee Khai Wong
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia
| | - Ahmed H Birima
- Department of Civil Engineering, College of Engineering, Qassim University, Unaizah, Saudi Arabia
| | - Ahmed El-Shafie
- Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.,National Water and Energy Center, United Arab Emirates University, Al Ain, United Arab Emirates
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Anmala J, Turuganti V. Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed. WATER ENVIRONMENT RESEARCH : A RESEARCH PUBLICATION OF THE WATER ENVIRONMENT FEDERATION 2021; 93:2360-2373. [PMID: 34528328 DOI: 10.1002/wer.1642] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 09/09/2021] [Accepted: 09/13/2021] [Indexed: 06/13/2023]
Abstract
Stream waters play a crucial role in catering to the world's needs with the required quality of water. Due to the discharges of wastewater from the various point and nonpoint sources, most of the watersheds are contaminated easily. The Upper Green River watershed in Kentucky, USA, is one such watershed that is contaminated over the years due to the runoff from rural areas and agricultural lands and combined sewer overflows (CSOs) from urban areas. Monitoring and characterizing the water quality status of streams in such watersheds has become of great importance, with multivariate statistical techniques such as regression, factor analysis, cluster analysis, and artificial intelligence methods such as artificial neural networks (ANNs). The water quality parameters, namely, fecal coliform (FC), turbidity, pH, and conductivity have been predicted quantitatively using ANNs to understand the water quality status of streams in the Upper Green River watershed elsewhere. In this study, a novel attempt has been made to predict the status of the quality of the Green River water with the predictive capabilities of a few decision tree (DT) algorithms such as classification and regression tree (CART) model, multivariate adaptive regression splines (MARS) model, random forest (RF) model, and extreme learning machine (ELM) model. The RF model's performance is better in predicting FC, turbidity, and pH than CART models in training and testing phases. Relatively, MARS and ELM models did better in testing though the performance is poorer in training. For example, we obtain the RMSE values of 2206, 2532, 1533, and 1969 using RF, CART, MARS, and ELM for FC in testing. A good correlation has been observed between conductivity and temperature, precipitation, and land-use factors for the MARS model. Overall, DT models are helpful in understanding, interpreting the outcomes, and visualizing the results compared with the other models. PRACTITIONER POINTS: The prediction of stream water quality parameters using decision trees is explored. The climate and land use parameters are used as input parameters to the modeling. The DT models of CART, MARS, RF, and ANNs such as ELM are explored to predict stream water quality. The RF model shows stable results compared with CART, MARS, and ELM for the data explored. Apart from the R2 value, RMSE and MAE indicate the effectiveness of DTs in prediction.
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Affiliation(s)
- Jagadeesh Anmala
- Department of Civil Engineering, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad, Telangana, India
| | - Venkateswarlu Turuganti
- Department of Civil Engineering, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad, Telangana, India
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Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks. SUSTAINABILITY 2021. [DOI: 10.3390/su13179898] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Predicting the level of dissolved oxygen (DO) is an important issue ensuring the sustainability of the inhabitants of a river. A prediction model can predict the DO level using a historical dataset with regard to water temperature, pH, and specific conductance for a given river. The model can be built using sophisticated computational procedures such as multi-layer perceptron-based artificial neural networks. Different types of networks can be constructed for this purpose. In this study, the authors constructed three networks, namely, multi-verse optimizer (MVO), black hole algorithm (BHA), and shuffled complex evolution (SCE). The networks were trained using the datasets collected from the Klamath River Station, Oregon, USA, for the period 2015–2018. We found that the trained networks could predict the DO level of 2019. We also found that both BHA- and SCE-based networks could predict the level of DO using a relatively simple configuration compared to that of MVO. From the viewpoints of absolute errors and Pearson’s correlation coefficient, MVO- and SCE-based networks performed better than BHA-based networks. In synopsis, the authors recommend MVO- and MLP-based artificial neural networks for predicting the DO level of a river.
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Nacar S, Mete B, Bayram A. Estimation of daily dissolved oxygen concentration for river water quality using conventional regression analysis, multivariate adaptive regression splines, and TreeNet techniques. ENVIRONMENTAL MONITORING AND ASSESSMENT 2020; 192:752. [PMID: 33159587 DOI: 10.1007/s10661-020-08649-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 09/29/2020] [Indexed: 06/11/2023]
Abstract
The aim of this study was to model the surface water quality of the Broad River Basin, South Carolina. The most suitable two monitoring stations numbered as USGS 02156500 (Near Carlisle) and USGS 02160991 (Near Jenkinsville) were selected for the reason that the river water temperature (WT), pH, and specific conductance (SC), as well as dissolved oxygen (DO) concentration, were simultaneously monitored and recorded at these sites. The monitoring period from September 2016 to August 2017 was taken into account for the modeling studies. The electrical conductivity (EC) values corresponding to the river SC values were calculated. First, the conventional regression analysis (CRA) was applied to three regression forms, i.e., linear, power, and exponential functions, to estimate the river DO concentration. Then, the multivariate adaptive regression splines (MARS) and TreeNet gradient boosting machine (TreeNet) techniques were employed. Three performance statistics, i.e., root means square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe coefficient of efficiency (NS), were used to compare the estimation capabilities of these techniques. The TreeNet technique, which was used for the first time in the modeling of DO concentration, had higher estimation success with the RMSE, MAE, and NS values of 0.182 mg/L, 0.123 mg/L, and 0.990, respectively, for the Carlisle station and 0.313 mg/L, 0.233 mg/L, and 0.965, respectively, for the Jenkinsville station in the training phase. The MARS technique, which had limited availability of its application in the modeling of DO concentration, had higher estimation success with the RMSE, MAE, and NS values of 0.240 mg/L, 0.195 mg/L, and 0.981, respectively, for the Carlisle station and 0.527 mg/L, 0.432 mg/L, and 0.980, respectively, for the Jenkinsville station in the testing phase. Considering the RMSE and MAE values being lower, as well as NS values being higher for the model having an input combination of WT, pH, and EC, the Carlisle station came into prominence. It was concluded that international researchers, who have engaged in the river water quality modeling studies, can favor the MARS and TreeNET techniques without any hesitation and estimate the river DO concentration successfully. The models developed for the Carlisle station were tested with the data sets for the monitoring period from September 2017 to August 2018 at the same station. Similarly, the models developed for the Jenkinsville station were tested with the data sets for the monitoring period from September 2017 to August 2018 at the same station. It was concluded that the models could estimate the river DO concentrations very close to in situ measurements at the same site but for the different monitoring periods, too. Furthermore, the models developed for the Carlisle station were tested with the data sets from the Jenkinsville station for the same monitoring period. Similarly, the models developed for the Jenkinsville station were tested with the data sets from the Carlisle station for the same monitoring period. It was also concluded that the developed models could estimate the river DO concentrations very close to in situ measurements at different monitoring sites but for the same monitoring period on the same river, too. It can be asserted that the models developed for any monitoring site on a river can be employed for another monitoring site on the same river, too, as in the case of the Broad River, South Carolina.
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Affiliation(s)
- Sinan Nacar
- Faculty of Engineering, Department of Civil Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey.
- Faculty of Engineering and Architecture, Department of Civil Engineering, Tokat Gaziosmanpaşa University, 60150, Tokat, Turkey.
| | - Betul Mete
- Faculty of Engineering, Department of Civil Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey
| | - Adem Bayram
- Faculty of Engineering, Department of Civil Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey
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