scholarly journals A Machine‐Learning‐Based Model for Water Quality in Coastal Waters, Taking Dissolved Oxygen and Hypoxia in Chesapeake Bay as an Example

2020 ◽  
Vol 56 (9) ◽  
Author(s):  
Xin Yu ◽  
Jian Shen ◽  
Jiabi Du
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Chesapeake Bay ◽  
Coastal Waters

scholarly journals Machine learning for manually-measured water quality prediction in fish farming

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256380
Author(s):  
Andres Felipe Zambrano ◽  
Luis Felipe Giraldo ◽  
Julian Quimbayo ◽  
Brayan Medina ◽  
Eduardo Castillo
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Random Forests ◽  
Prediction Models ◽  
Fish Farming ◽  
Time Data ◽  
Water Quality Prediction ◽  
Water Pond ◽  
Measured Water

Monitoring variables such as dissolved oxygen, pH, and pond temperature is a key aspect of high-quality fish farming. Machine learning (ML) techniques have been proposed to model the dynamics of such variables to improve the fish farmer’s decision-making. Most of the research on ML in aquaculture has focused on scenarios where devices for real-time data acquisition, storage, and remote monitoring are available, making it easy to develop accurate ML techniques. However, fish farmers do not necessarily have access to such devices. Many of them prefer to use equipment to manually measure these variables limiting the amount of available data to process. In this work, we study the use of random forests, multivariate linear regression, and artificial neural networks in scenarios with limited amount of measurements to analyze data from water-quality variables that are commonly measured in fish farming. We propose a methodology to build models in two scenarios: i) estimation of unobserved variables based on the observed ones, and ii) forecasting when a low amount of data is available for training. We show that random forests can be used to forecast dissolved oxygen, pond temperature, pH, ammonia, and ammonium when the water pond variables are measured only twice per day. Moreover, we showed that these prediction models can be implemented on a mobile-based information system and run in an average smartphone that fish farmers can afford.

scholarly journals Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

2019 ◽  
Vol 11 (6) ◽  
pp. 617 ◽  
Author(s):  
Sidrah Hafeez ◽  
Man Wong ◽  
Hung Ho ◽  
Majid Nazeer ◽  
Janet Nichol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Satellite Data ◽  
Coastal Waters ◽  
Machine Learning Techniques ◽  
Blue Band ◽  
Chl A ◽  
Learning Techniques ◽  
Reflectance Data

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters.

scholarly journals Prediction of the Concentration of Dissolved Oxygen in Running Water by Employing A Random Forest Machine Learning Technique

2020 ◽  
Author(s):  
Mohammad Hafez Ahmed
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Random Forest ◽  
Dissolved Oxygen ◽  
Water Temperature ◽  
Quality Data ◽  
Linear Regression Method ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Input Variables

Dissolved oxygen (DO) is a key indicator in the study of the ecological health of rivers. Modeling DO is a major challenge due to complex interactions among various process components of it. Considering the vital importance of it in water bodies, the accurate prediction of DO is a critical issue in ecosystem management. Given the intricacy of the current process-based water quality models, a data-driven model could be an effective alternative tool. In this study, a random forest machine learning technique is employed to predict the DO level by identifying its major drivers. Time-series of half-hourly water quality data, spanning from 2007 to 2019, for the South Branch Potomac River near Springfield, WV, are obtained from the United States Geological Survey database. Key drivers are identified, and models are formulated for different scenarios of input variables. The model is calibrated for each input scenario using 80% of the data. Water temperature and pH are found to be the most influential predictors of DO. However, satisfactory model performance is achieved by considering water temperature, pH, and specific conductance as input variables. The model validation is made by predicting DO concentrations for the remaining 20% of the data. The comparison with the traditional multiple linear regression method shows that the random forest model performs significantly better. The study insights are, therefore, expected to be useful to estimate stream/river DO levels at various sites with a minimum number of predictors and help build a sturdy framework for ecosystem health management across an environmental gradient.

Development of Dissolved Oxygen Forecast Model Using Hybrid Machine Learning Algorithm with Hydro-Meteorological Variables

2021 ◽  
Author(s):  
Abul Abrar Masrur Ahmed ◽  
M A I Chowdhury ◽  
Oli Ahmed ◽  
Ambica Sutradhar
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Learning Algorithm ◽  
Data Extraction ◽  
Forecast Model ◽  
Multivariate Adaptive Regression Splines ◽  
Meteorological Variables ◽  
Discrete Wavelet ◽  
Input Selection

Abstract The ability to predict dissolved oxygen, which is a critical water quality (WQ) parameter, is critical for aquatic managers responsible for maintaining ecosystem health and the management of reservoirs affected by WQ. This paper reports forecasting dissolved oxygen (DO) concentration using multivariate adaptive regression splines (MARS) of running river water using a set of water quality and hydro-meteorological variables. This study’s key objectives were to assess input selection methods and five multi-resolution analyses as a data extraction approach. Moreover, the hybrid model is prepared by maximum overlap discrete wavelet transformation (MODWT) with the MARS model (i.e., MODWT-MARS). The proposed model is further compared with numerous machine learning methods. The result shows that the hybrid algorithms (i.e., MODWT-MARS) outperformed the other models (r = 0.981, WI = 0.990, RMAE = 2.47% and MAE = 0.089). This hybrid method may serve as the foundation for forecasting water quality variables with fewer predictor variables.

scholarly journals The competing impacts of climate change and nutrient reductions on dissolved oxygen in Chesapeake Bay

2017 ◽  
Author(s):  
Isaac D. Irby ◽  
Marjorie A. M. Friedrichs ◽  
Fei Da ◽  
Kyle E. Hinson
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Sea Level Rise ◽  
Chesapeake Bay ◽  
Sea Level ◽  
Climate Impacts ◽  
Freshwater Flow ◽  
The Impact ◽  
Impacts Of Climate Change

Abstract. The Chesapeake Bay region is projected to experience changes in temperature, sea level, and precipitation as a result of climate change. This research uses an estuarine-watershed hydrodynamic- biogeochemical modeling system along with projected changes in temperature, freshwater flow, and sea level rise for a 2050 scenario to explore the impact climate change may have on future Chesapeake Bay dissolved oxygen (DO) concentrations and the potential success of nutrient reductions in attaining mandated estuarine water quality improvements. Results indicate that warming Bay waters will decrease oxygen solubility year-round, while also increasing oxygen utilization via respiration and remineralization, primarily impacting bottom oxygen in the spring. Rising sea level will increase the volume of the Bay, pushing coastal saline water further into the Bay. Changes in precipitation are projected to deliver higher winter and spring freshwater flow and nutrient loads, fueling increased primary production. Together, these multiple climate impacts will lower DO throughout the Chesapeake Bay and negatively impact progress towards meeting water quality standards associated with the Chesapeake Bay Total Maximum Daily Load. However, this research also shows that the potential impacts of climate change will be significantly smaller than improvements in DO expected in response to the required nutrient reductions, especially at the anoxic and hypoxic levels. Overall, increased temperature exhibits the strongest control on the change in future DO concentrations, primarily due to decreased solubility, while sea level rise is expected to exert a small positive impact and increased winter river flow is anticipated to exert a small negative impact.

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