Machine Learning Based Predictions of Dissolved Oxygen in a Small Coastal Embayment

Journal of Marine Science and Engineering, Год журнала: 2020, Номер 8(12), С. 1007 - 1007

Опубликована: Дек. 9, 2020

Coastal dissolved oxygen (DO) concentrations have a profound impact on nearshore ecosystems and, in recent years, there has been an increased prevalance of low DO hypoxic events that negatively organisms. Even with advanced numerical models, accurate prediction coastal variability is challenging and computationally expensive. Here, we apply machine learning techniques order to reconstruct predict small embayment while using comprehensive set offshore measurements easily measured input (training) parameters. We show both random forest regression (RFR) support vector (SVR) models accurately reproduce the extremely high accuracy. In general, RFR consistently peformed slightly better than SVR, latter which was more difficult tune took longer train. Although each datasets were able values training data from same site, model only had moderate success when one site at another likely due complexities underlying dynamics across sites. also accuracy can be achieved relatively little data, highlighting potential application for correcting time series missing quality control or sensor issues. This work establishes ability waters, important implications detect indirectly measure near real-time. Future should explore forecast events.

Язык: Английский

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Assessment of Algorithm Performance on Predicting Total Dissolved Solids Using Artificial Neural Network and Multiple Linear Regression for the Groundwater Data

Water, Год журнала: 2022, Номер 14(13), С. 2002 - 2002

Опубликована: Июнь 23, 2022

Estimating groundwater quality parameters through conventional methods is time-consuming laboratory measurements for megacities. There a need to develop models that can help decision-makers make policies sustainable reserves. The current study compared the efficiency of multivariate linear regressions (MLR) and artificial neural network (ANN) in prediction total dissolved solids (TDS) three sub-divisions Lahore, Pakistan. data this were collected every quarter year six years. ANN was applied investigate feasibility feedforward, backpropagation networks with training functions T-BR (Bayesian regularization backpropagation), T-LM (Levenberg–Marquardt T-SCG (scaled conjugate backpropagation). Two activation used analyze performance algorithmic functions, i.e., Logsig Tanh. Input pH, electrical conductivity (EC), calcium (Ca2+), magnesium (Mg2+), chloride (Cl−), sulfate (SO42−) predict TDS as an output parameter. computed values by MLR close agreement their respective measured values. Comparative analysis showed root means square error (RMSE) city sub-division Pearson’s coefficient correlation (r) 2.9% 0.981 4.5% 0.978, respectively. Similarly, Farrukhabad sub-division, RMSE r 4.9% 0.952, while 5.5% 0.941, For Shahadra 10.8%, 0.869 ANN, 11.3%, 0.860 MLR. results exhibited model less than Therefore, be employed successfully tool assessment.

Язык: Английский

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Enhancing shallow water quality monitoring efficiency with deep learning and remote sensing: A case study in Mar Menor

Journal of Ambient Intelligence and Smart Environments, Год журнала: 2024, Номер unknown, С. 1 - 18

Опубликована: Март 1, 2024

Satellite remote sensing technology has proven effective in monitoring various environmental parameters, but its efficiency assessing shallow lakes been limited. This study applies state-of-the-art machine and deep learning algorithms supported by classical statistic methods to analyze data measure chlorophyll-a (Chl-a) concentration levels. Focused on a coastal lagoon, Mar Menor, this work analyzes statistically daily Sentinel 3 information behaviour compares Machine Learning Deep techniques enhance accuracy of satellite. Convolutional Neural Networks (CNNs) stand out as robust choice, capable delivering excellent results even the presence anomalous events. Our findings demonstrate that CNN-based approach directly utilizing satellite yields promising lakes, offering enhanced robustness. research contributes optimizing produce continuous flow addressed aquatic ecosystems with potential management conservation applications.

Язык: Английский

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A modular and mesh-capable LoRa based Content Transfer Protocol for Environmental Sensing

Опубликована: Янв. 8, 2023

Sensors are increasingly collecting data everywhere, changing how we relate to and manage the environment. These answer scientific questions of researchers, but can also generate social, cultural, political effects, reinforcing need for in near real time. In this work, a low-power, scalable, sustainable communication solution based on LoRa is proposed develop an oceanographic monitoring system composed water quality buoys that measure, among others, parameters such as temperature, chlorophyll-a, turbidity, oxygen concentration, etc. An exhaustive evaluation carried out with test bed 135 Km ² coastal lagoon, demonstrating our proposal offers good performance terms transfer time latency, energy consumption, range, showing scalability number added.

Язык: Английский

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Ammonium nitrogen and phosphorus removal by bacterial-algal symbiotic dynamic sponge bioremediation system in micropolluted water: Operational mechanism and transformation pathways

The Science of The Total Environment, Год журнала: 2024, Номер 947, С. 174636 - 174636

Опубликована: Июль 9, 2024

Язык: Английский

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