A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

Rostam, Nur Aqilah Paskhal; Malim, Nurul Hashimah Ahamed Hassain; Abdullah, Rosni; Ahmad, Abdul Latif; Ooi, Boon Seng; Chan, Derek Juinn Chieh

doi:10.1109/access.2021.3102044

Cited by 27 publications

(16 citation statements)

References 77 publications

(121 reference statements)

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“…As well as a series of experiments with LSTM on currents, sea level, and even ENSO phenomenon (Broni-Bedaiko et al, 2019;Ishida et al, 2020;Zulfa et al, 2021). LSTM models were also shown to be effective in predicting chlorophyll-a concentrations (Cho and Park, 2019;Rostam et al, 2021;Cen et al, 2022). These experiments have demonstrated the robust performance of the LSTM model.…”

Section: Rnnmentioning

confidence: 82%

A review of artificial intelligence in marine science

Song

Pang²,

Hou³

et al. 2023

Front. Earth Sci.

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Utilization and exploitation of marine resources by humans have contributed to the growth of marine research. As technology progresses, artificial intelligence (AI) approaches are progressively being applied to maritime research, complementing traditional marine forecasting models and observation techniques to some degree. This article takes the artificial intelligence algorithmic model as its starting point, references several application trials, and methodically elaborates on the emerging research trend of mixing machine learning and physical modeling concepts. This article discusses the evolution of methodologies for the building of ocean observations, the application of artificial intelligence to remote sensing satellites, smart sensors, and intelligent underwater robots, and the construction of ocean big data. We also cover the method of identifying internal waves (IW), heatwaves, El Niño-Southern Oscillation (ENSO), and sea ice using artificial intelligence algorithms. In addition, we analyze the applications of artificial intelligence models in the prediction of ocean components, including physics-driven numerical models, model-driven statistical models, traditional machine learning models, data-driven deep learning models, and physical models combined with artificial intelligence models. This review shows the growth routes of the application of artificial intelligence in ocean observation, ocean phenomena identification, and ocean elements forecasting, with examples and forecasts of their future development trends from several angles and points of view, by categorizing the various uses of artificial intelligence in the ocean sector.

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Section: Rnnmentioning

confidence: 82%

A review of artificial intelligence in marine science

Song

Pang²,

Hou³

et al. 2023

Front. Earth Sci.

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“…Here, ( x

) represents the offset of x relative to

and ((

−

)/(

−

)) represents the slope from

. By multiplying the offset by the slope and adding it to

, the missing value y can be estimated [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods

Wang

Qiao

et al. 2023

Entropy

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In the context of escalating global environmental concerns, the importance of preserving water resources and upholding ecological equilibrium has become increasingly apparent. As a result, the monitoring and prediction of water quality have emerged as vital tasks in achieving these objectives. However, ensuring the accuracy and dependability of water quality prediction has proven to be a challenging endeavor. To address this issue, this study proposes a comprehensive weight-based approach that combines entropy weighting with the Pearson correlation coefficient to select crucial features in water quality prediction. This approach effectively considers both feature correlation and information content, avoiding excessive reliance on a single criterion for feature selection. Through the utilization of this comprehensive approach, a comprehensive evaluation of the contribution and importance of the features was achieved, thereby minimizing subjective bias and uncertainty. By striking a balance among various factors, features with stronger correlation and greater information content can be selected, leading to improved accuracy and robustness in the feature-selection process. Furthermore, this study explored several machine learning models for water quality prediction, including Support Vector Machines (SVMs), Multilayer Perceptron (MLP), Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM). SVM exhibited commendable performance in predicting Dissolved Oxygen (DO), showcasing excellent generalization capabilities and high prediction accuracy. MLP demonstrated its strength in nonlinear modeling and performed well in predicting multiple water quality parameters. Conversely, the RF and XGBoost models exhibited relatively inferior performance in water quality prediction. In contrast, the LSTM model, a recurrent neural network specialized in processing time series data, demonstrated exceptional abilities in water quality prediction. It effectively captured the dynamic patterns present in time series data, offering stable and accurate predictions for various water quality parameters.

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“…Kesimpulan pada bagian ini akan memberikan gambaran awal tentang arah dan implikasi pemanfaatan sistem cerdas dalam konteks pabrik AMDK yang dapat menjadi dasar untuk pengembangan penelitian lebih lanjut. [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38]…”

Section: Instrumen Dan Proses Analisisunclassified

Kajian Awal Pemanfaatan Sistem Cerdas untuk Pemantauan Kualitas Air dalam Konteks Pembangunan Pabrik AMDK

Irfan Wahyu Ramadhan,

Firdaus

2024

ZTR

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Penelitian ini merupakan kajian awal yang mendalam terhadap literatur ilmiah yang membahas pemanfaatan sistem cerdas dalam pemantauan kualitas air, dengan fokus pada konteks pembangunan pabrik Air Minum Dalam Kemasan (AMDK). Melalui tinjauan berbagai jurnal, penelitian ini menyelidiki berbagai metode dan teknologi yang digunakan dalam pemantauan serta pengendalian parameter kritis air. Beberapa penelitian menawarkan solusi berbasis Internet of Things (IoT) dan kecerdasan buatan untuk pengolahan data, dengan parameter utama seperti pH, kekeruhan, suhu, dan Total Dissolved Solids (TDS). Hasil kajian menunjukkan bahwa implementasi sistem cerdas memberikan solusi yang efektif dalam mengatasi tantangan kritis terkait kualitas air, seperti pencemaran, pemantauan real-time, dan pengelolaan sumber daya air yang efisien. Analisis dalam kajian ini terfokus pada parameter kualitas air, sistem cerdas yang diadopsi, metode penetapan standar kualitas air, serta standar kualitas air yang diterapkan dalam literatur yang ditinjau. Dengan mengumpulkan dan menganalisis data dari sumber terpercaya seperti ScienceDirect, IEEE Xplore, dan Google Scholar, penelitian ini membuka perspektif baru terkait pemanfaatan sistem cerdas dalam merancang sistem monitoring kualitas air yang optimal pada tahap awal pembangunan pabrik AMDK.

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A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

Cited by 27 publications

References 77 publications

A review of artificial intelligence in marine science

A review of artificial intelligence in marine science

Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods

Kajian Awal Pemanfaatan Sistem Cerdas untuk Pemantauan Kualitas Air dalam Konteks Pembangunan Pabrik AMDK

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