Groundwater Quality Analysis and Drinkability Prediction using Artificial Intelligence

Panigrahi, Niranjan; Patro, S Gopal Krishna; Kumar, Raghvendra; Omar, Michael; Ngân, Trần Thị; Giang, Nguyễn Long; Thu, Bui Thi; Thắng, Nguyễn Tất

doi:10.1007/s12145-023-00977-x

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…ML has been utilized in the water distribution and quality areas in a variety of methods for improvement, pattern discovery, demand forecast, and leak detection (Ayati et al, 2022;García et al, 2023;Panigrahi et al, 2023;Xu et al, 2022;. Large volumes of data from databases such as the United States Geologic Survey (USGS) and National Water Information System (NWIS); experimental data, and other reputable sources have been analyzed for this purpose (Hu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Application Of Machine Learning To Understand Pfas Occurrence, Distribution, Transport And Removal In Water

Ajao

2024

Preprint

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Per- and polyfluoroalkyl substances (PFAS) are arguably the most common water contaminants in the world today. While several research experiments have been done to understand and remove PFAS from the environment, there is still a lot of unknown. Little has been known about the use of Machine learning (ML) to understand PFAS. This work hence reviews some leading ML approaches and applications in PFAS studies in the distribution, transport, removal, and occurrence predictions of PFAS. Several evaluation matrices were examined and used to perform this function. There are still a lot of areas whereby ML can be used to improve our PFAS knowledge base, some of these were briefly stated in this review.

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Section: Introductionmentioning

confidence: 99%

Application Of Machine Learning To Understand Pfas Occurrence, Distribution, Transport And Removal In Water

Ajao

2024

Preprint

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“…The operation is based on building a hyperplane to separate the data into different classes [35], while minimizing the classification error, through geometric margin maximization between classes [33]. SVM can be applied in different research areas, such as in chemistry to predict the toxicity of different compounds [37,38], in hydrology to determine water quality for drinking purposes [39], and in food technology to study plasticizers in extra virgin olive oil [40] or to classify Greek olive oils [41], among others.…”

Section: Introductionmentioning

confidence: 99%

Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in Olea europaea Leaves

Rodríguez-Fernández,

Fernández-Gómez,

Mejuto

et al. 2023

Foods

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The study of the phenolic compounds present in olive leaves (Olea europaea) is of great interest due to their health benefits. In this research, different machine learning algorithms such as RF, SVM, and ANN, with temperature, time, and volume as input variables, were developed to model the extract yield and the total phenolic content (TPC) from experimental data reported in the literature. In terms of extract yield, the neural network-based ANNZ-L model presents the lowest root mean square error (RMSE) value in the validation phase (9.44 mg/g DL), which corresponds with a mean absolute percentage error (MAPE) of 3.7%. On the other hand, the best model to determine the TPC value was the neural network-based model ANNR, with an RMSE of 0.89 mg GAE/g DL in the validation phase (MAPE of 2.9%). Both models obtain, for the test phase, MAPE values of 4.9 and 3.5%, respectively. This affirms that ANN models would be good modelling tools to determine the extract yield and TPC value of the ultrasound-assisted extraction (UAE) process of olive leaves under different temperatures, times, and solvents.

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An optimized explainable artificial intelligence approach for sustainable clean water

Ezzat

Soliman

Ahmed

et al. 2023

Environ Dev Sustain

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Water, sanitation, and hygiene are essential components of the 2030 agenda for sustainable development. Goal 6 is dedicated to guarantee all societies have access to water and sanitation. Water quality (WQ) assessment is crucial to ensure the availability of clean water. This paper presents an approach called AHA–XDNN for predicting WQ. The proposed approach is based on three pillars to predict WQ with high accuracy and confidence, namely, deep neural networks (DNN), artificial hummingbird algorithm (AHA), and explainable artificial intelligence. The proposed approach involves five phases: data preprocessing, optimization, training, and evaluation. In the first phase, problems such as unwanted noise and imbalance are addressed. In the second phase, AHA is applied to optimize the DNN model’s hyper-parameters. In the third phase, the DNN model is trained on the dataset processed in the first phase. The performance of the optimized DNN model is evaluated using four measurements, and the results are explained and interpreted using SHapley additive exPlanations. The proposed approach achieved an accuracy, average precision, average recall, average F1-score of 91%, 91%, 91.5%, and 91% on the test set, respectively. By comparing the proposed approach with existing models based on artificial neural network (ANN), the proposed approach was able to outperform its counterparts in terms of average recall and average F1-score.

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Groundwater Quality Analysis and Drinkability Prediction using Artificial Intelligence

Cited by 6 publications

References 30 publications

Application Of Machine Learning To Understand Pfas Occurrence, Distribution, Transport And Removal In Water

Application Of Machine Learning To Understand Pfas Occurrence, Distribution, Transport And Removal In Water

Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in Olea europaea Leaves

An optimized explainable artificial intelligence approach for sustainable clean water

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