scholarly journals Machine Learning modeling techniques for forecasting the trophic level in a restored South Mediterranean lagoon using Chlorophyll-α

2021 ◽  
Author(s):  
Nadia Ben Hadid ◽  
Catherine Goyet ◽  
Hatem Chaar ◽  
Naceur Ben Maiz ◽  
Veronique Guglielmi ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Secchi Depth ◽  
Quality Data ◽  
Predictor Variables ◽  
Water Quality Data ◽  
Chl A ◽  
The North ◽  
Artificial Neural Network Ann ◽  
Narx Model

Abstract An Artificial Neural Network (ANN), a Machine Learning (ML) modeling approach is proposed to predict the ecological state of the North Lagoon of Tunis, a shallow restored Mediterranean coastal ecosystem. A Nonlinear Auto Regressive with exogenous input (NARX) neural network model was fitted to predict Chlorophyll- a (Chl- a ) concentrations in the North Lagoon of Tunis as an eutrophication indicator. The modeling is based on approximately three decades of monitoring water quality data (from January 1989 to April 2018) to train, validate and test the NARX model. The most relevant predictor variables used in this model were those having a high permutation importance ranking with Random Forest (RF) model, which simplified the structure of the network, resulting in a more accurate and efficient procedure. Those predictor variables are secchi depth, and dissolved oxygen. Various model scenarios with different NARX architectures were tested for Chl- a prediction. To verify the model performances, the trained models were applied to field monitoring data. Results indicated that the developed NARX model can predict Chl- a concentrations in the North Lagoon of Tunis with high accuracy (R= 0.79; MSE= 0.31). In addition, results showed that the NARX model generally performed better than multivariate linear regression (MVLR). This approach could provide a quick assessment of Chl- a variations for lagoon management and eco-restoration.

scholarly journals Phytoplankton in relation to water quality of Tanguar Haor ecosystem, Bangladesh: I. Rauar station

2019 ◽  
Vol 28 (2) ◽  
pp. 131-138
Author(s):  
Mohammad Azmal Hossain Bhuiyan ◽  
SAM Shariar Islam ◽  
Abu Kowser ◽  
Md Rasikul Islam ◽  
Shahina Akter Kakoly ◽  
...  
Keyword(s):  
Water Quality ◽  
Air Temperature ◽  
Environmental Variables ◽  
Secchi Depth ◽  
Soluble Reactive Phosphorus ◽  
Organic Pollution ◽  
Quality Data ◽  
Water Quality Data ◽  
Chl A ◽  
Reactive Phosphorus

The water quality at Rauar station of Tanguar Haor, Sunamganj was assessed studying phytoplankton and associated environmental variables. The environmental variables were monitored over a period of one year, collecting samples at two months interval between March, 2017 and March, 2018. Air temperature, rainfall, and humidity ranged from 22.6 - 32.1°C, 48 - 76% and 8 - 930 mm, respectively. Air temperature showed a direct relationship with water temperature which varied from 22.4 - 31.0°C during the study period. The water transparency remained relatively constant throughout the year having a mean Secchi depth (Zs) value of 2.48 m. Total dissolved solids (TDS), conductivity, and pH of the water ranged from 51 - 85 mg/l, 60 - 110 μS/cm, and 7.2 - 9.7, respectively. In December, because of a temperature fall, the dissolved oxygen (DO) concentration of the water reached its maximum value of 6.09 mg/l. In the rest of the period, the concentration remained between 2.44 and 4.80 mg/l. The value of alkalinity ranged from 0.43 - 1.35 meq/l. Among the nutrients, soluble reactive phosphorus (SRP), soluble reactive silicate (SRS), and NO3-N ranged from 5.43 - 36.43 μg/l, 4 - 14.58 mg/l, and 0.06 - 0.31 mg/l, respectively. The concentration of NH4+ ranged from 238 - 1230 μg/l. The highest concentrations (905 and 1230 μg/l) occurred between September and December, 2017. This might be attributed to the higher density of migratory birds during that period. The phytoplanktonic biomass expressed as chlorophyll-a (Chl-a) ranged from 1.35 - 8.45 μg/l while its degraded product phaeophytin concentration ranged from 0.08 - 3.5 μg/l. The standing crop of phytoplankton ranged from 397 - 2480 × 103 individuals/l of haor water exhibiting its maximum abundance in September. This parameter showed a highly significant positive correlation with SRP. From the correlation analysis, the degradation of chl-a to phaeophytin was found to be temperature dependent. Considering the different physicochemical and biological water quality data, it could be said that the Tanguar Haor is still free from organic pollution. However, the range of soluble reactive phosphorus data (5.43 - 36.43 μg/l) show that the Haor has been passing a meso-eutrophic state. Dhaka Univ. J. Biol. Sci. 28(2): 131-138, 2019 (July)

scholarly journals Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)

Hydrology ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 80
Author(s):  
Khurshid Jahan ◽  
Soni M. Pradhanang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Sodium Chloride ◽  
Stormwater Runoff ◽  
Quality Data ◽  
Ann Model ◽  
Water Quality Data ◽  
Artificial Neural Network Ann ◽  
Chloride Concentrations

Road salts in stormwater runoff, from both urban and suburban areas, are of concern to many. Chloride-based deicers [i.e., sodium chloride (NaCl), magnesium chloride (MgCl2), and calcium chloride (CaCl2)], dissolve in runoff, travel downstream in the aqueous phase, percolate into soils, and leach into groundwater. In this study, data obtained from stormwater runoff events were used to predict chloride concentrations and seasonal impacts at different sites within a suburban watershed. Water quality data for 42 rainfall events (2016–2019) greater than 12.7 mm (0.5 inches) were used. An artificial neural network (ANN) model was developed, using measured rainfall volume, turbidity, total suspended solids (TSS), dissolved organic carbon (DOC), sodium, chloride, and total nitrate concentrations. Water quality data were trained using the Levenberg-Marquardt back-propagation algorithm. The model was then applied to six different sites. The new ANN model proved accurate in predicting values. This study illustrates that road salt and deicers are the prime cause of high chloride concentrations in runoff during winter and spring, threatening the aquatic environment.

scholarly journals Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhonghua Xu ◽  
Changguo Dai ◽  
Jing Wang ◽  
Lejun Liu ◽  
Lei Jiang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Resource Management ◽  
Water Environment ◽  
Water Bodies ◽  
Quality Data ◽  
Water Quality Data ◽  
Recognition Model ◽  
Digital Twins ◽  
Artificial Neural

In the water environment, construction, and civil engineering industries, digital twins have gradually become a popular solution in recent years, and in digital twins, accurate data prediction and category recognition are important parts of it. Artificial neural network (ANN), a widely used data-driven model, can accurately identify nonlinear relationships in the water environment. In this paper, a recognition model for black-odorous water bodies based on ANN was established to directly identify the sensory description of water bodies. This study used water quality data and sensory description (color and odor) as samples to train backpropagation (BP) neural networks. The training results show that the accuracy of the color and odor models reaches 86.7% and 85.8%, respectively. It can thus be suggested that the sensory description can be accurately recognized by BP neural network. The application results indicate that all seven rivers had black-odorous phenomenon within a year. The recognition models have been instrumental in water resource management. Meanwhile, the models provide a reference for the evaluation and early warning of black-odorous water bodies in other regions.

Fundamental Categories of Artificial Neural Networks

2021 ◽  
pp. 30-64
Author(s):  
Arunaben Prahladbhai Gurjar ◽  
Shitalben Bhagubhai Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Operation ◽  
Artificial Neural ◽  
Classification Prediction ◽  
Artificial Neural Network Ann ◽  
Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

Fundamental Categories of Artificial Neural Networks

2022 ◽  
pp. 1-30
Author(s):  
Arunaben Prahladbhai Gurjar ◽  
Shitalben Bhagubhai Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Operation ◽  
Artificial Neural ◽  
Classification Prediction ◽  
Artificial Neural Network Ann ◽  
Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

scholarly journals Multi-Step-Ahead Forecasting of Wave Conditions Based on a Physics-Based Machine Learning (PBML) Model for Marine Operations

2020 ◽  
Vol 8 (12) ◽  
pp. 992
Author(s):  
Mengning Wu ◽  
Christos Stefanakos ◽  
Zhen Gao
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
High Reliability ◽  
Computational Cost ◽  
Model Performance ◽  
The North ◽  
The North Sea ◽  
Wave Conditions ◽  
Wave Models ◽  
Artificial Neural Network Ann

Short-term wave forecasts are essential for the execution of marine operations. In this paper, an efficient and reliable physics-based machine learning (PBML) model is proposed to realize the multi-step-ahead forecasting of wave conditions (e.g., significant wave height Hs and peak wave period Tp). In the model, the primary variables in physics-based wave models (i.e., the wind forcing and initial wave boundary condition) are considered as inputs. Meanwhile, a machine learning algorithm (artificial neural network, ANN) is adopted to build an implicit relation between inputs and forecasted outputs of wave conditions. The computational cost of this data-driven model is obviously much lower than that of the differential-equation based physical model. A ten-year (from 2001 to 2010) dataset of every three hours at the North Sea center was used to assess the model performance in a small domain. The result reveals high reliability for one-day-ahead Hs forecasts, while that of Tp is slightly lower due to the weaker implicit relationships between the data. Overall, the PBML model can be conceived as an efficient tool for the multi-step-ahead forecasting of wave conditions, and thus has great potential for furthering assist decision-making during the execution of marine operations.

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