Ensemble machine learning paradigms in hydrology: A review

Journal of Hydrology, Journal Year: 2021, Volume and Issue: 598, P. 126266 - 126266

Published: April 1, 2021

Language: Английский

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Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Bioresource Technology, Journal Year: 2022, Volume and Issue: 369, P. 128486 - 128486

Published: Dec. 14, 2022

Language: Английский

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Water quality prediction using machine learning models based on grid search method

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(12), P. 35307 - 35334

Published: Sept. 29, 2023

Abstract Water quality is very dominant for humans, animals, plants, industries, and the environment. In last decades, of water has been impacted by contamination pollution. this paper, challenge to anticipate Quality Index (WQI) Classification (WQC), such that WQI a vital indicator validity. study, parameters optimization tuning are utilized improve accuracy several machine learning models, where techniques process predicting WQC. Grid search method used optimizing four classification models also, regression models. Random forest (RF) model, Extreme Gradient Boosting (Xgboost) (GB) Adaptive (AdaBoost) model as K-nearest neighbor (KNN) regressor decision tree (DT) support vector (SVR) multi-layer perceptron (MLP) WQI. addition, preprocessing step including, data imputation (mean imputation) normalization were performed fit make it convenient any further processing. The dataset in study includes 7 features 1991 instances. To examine efficacy approaches, five assessment metrics computed: accuracy, recall, precision, Matthews's Correlation Coefficient (MCC), F1 score. assess effectiveness Mean Absolute Error (MAE), Median (MedAE), Square (MSE), coefficient determination (R 2 ). terms classification, testing findings showed GB produced best results, with an 99.50% when WQC values. According experimental MLP outperformed other achieved R value 99.8% while

Language: Английский

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Predicting Water Quality Index (WQI) by feature selection and machine learning: A case study of An Kim Hai irrigation system

Ecological Informatics, Journal Year: 2023, Volume and Issue: 74, P. 101991 - 101991

Published: Jan. 18, 2023

Language: Английский

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Predicting lake water quality index with sensitivity-uncertainty analysis using deep learning algorithms

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 406, P. 136885 - 136885

Published: April 3, 2023

Language: Английский

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Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Heliyon, Journal Year: 2024, Volume and Issue: 10(6), P. e27920 - e27920

Published: March 1, 2024

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality ensure usability. The advent of the. Internet Things (IoT) has brought about revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring water (WQ). By employing Machine learning (ML) techniques, this gathered can be analyzed make accurate predictions regarding quality. These predictive insights play crucial role decision-making processes aimed at safeguarding quality, such identifying areas need immediate attention and implementing preventive measures avert contamination. This paper aims provide comprehensive review current state art monitoring, with specific focus on employment IoT wireless technologies ML techniques. study examines utilization range technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, Bluetooth, context Furthermore, it explores application both supervised unsupervised algorithms for analyzing interpreting collected data. In addition discussing art, survey also addresses challenges open research questions involved integrating (WQM).

Language: Английский

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Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 5, 2024

Abstract Prediction of suspended sediment load (SSL) in streams is significant hydrological modeling and water resources engineering. Development a consistent accurate prediction model highly necessary due to its difficulty complexity practice because transportation vastly non-linear governed by several variables like rainfall, strength flow, supply. Artificial intelligence (AI) approaches have become prevalent resource engineering solve multifaceted problems modelling. The present work proposes robust incorporating support vector machine with novel sparrow search algorithm (SVM-SSA) compute SSL Tilga, Jenapur, Jaraikela Gomlai stations Brahmani river basin, Odisha State, India. Five different scenarios are considered for development. Performance assessment developed analyzed on basis mean absolute error (MAE), root squared (RMSE), determination coefficient (R 2 ), Nash–Sutcliffe efficiency (E NS ). outcomes SVM-SSA compared three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper SVM-BA (Bat benchmark SVM model. findings revealed that successfully estimates high accuracy scenario V (3-month lag) discharge (current time-step 3-month as input than other alternatives RMSE = 15.5287, MAE 15.3926, E 0.96481. conventional performed the worst prediction. Findings this investigation tend claim suitability employed approach rivers precisely reliably. guarantees precision forecasted while significantly decreasing computing time expenditure, satisfies demands realistic applications.

Language: Английский

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A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(21), P. 12655 - 12699

Published: May 13, 2024

Abstract Artificial neural networks (ANN), machine learning (ML), deep (DL), and ensemble (EL) are four outstanding approaches that enable algorithms to extract information from data make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, EL models have found extensive application in predicting geotechnical geoenvironmental parameters. This research aims provide a comprehensive assessment of applications addressing forecasting within field related engineering, including soil mechanics, foundation rock environmental geotechnics, transportation geotechnics. Previous studies not collectively examined all algorithms—ANN, EL—and explored their advantages disadvantages engineering. categorize address this gap existing literature systematically. An dataset relevant was gathered Web Science subjected an analysis based on approach, primary focus objectives, year publication, geographical distribution, results. Additionally, study included co-occurrence keyword covered techniques, systematic reviews, review articles data, sourced Scopus database through Elsevier Journal, were then visualized using VOS Viewer further examination. The results demonstrated ANN is widely utilized despite proven potential methods engineering due real-world laboratory civil engineers often encounter. However, when it comes behavior scenarios, techniques outperform three other methods. discussed here assist understanding benefits geo area. enables practitioners select most suitable creating certainty resilient ecosystem.

Language: Английский

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An advanced deep learning model for predicting water quality index

Ecological Indicators, Journal Year: 2024, Volume and Issue: 160, P. 111806 - 111806

Published: Feb. 29, 2024

Predicting a water quality index (WQI) is important because it serves as an metric for assessing the overall health and safety of bodies. Our paper develops new hybrid model predicting WQI. The study uses combination convolutional neural network (CNN), clockwork recurrent (Clockwork RNN), M5 Tree (CNN-CRNN-M5T) to predict M5T lacks advanced operators extracting meaningful data from parameters, so enhances its ability analyze intricate patterns. general linear analysis variance (GLM-ANOVA) improved version ANOVA. GLM-ANOVA determine significant inputs. As all input variables had p < 0.050, they were defined variables. Results showed that NH-NL PH highest lowest impact, respectively. used CNN-CRNN-M5T, CNN-CRNN, CRNN-M5T, CNN-M5T, CRNN, CNN, models WQI large basin in Malaysia. CNN-CRNN decreased testing mean absolute error (MAE) by 2.1 %, 12 15 CNN-CRNN-M5T increased Nash–Sutcliffe efficiency coefficient other 4–20 % 2.1–19 was reliable tool spatial temporal predictions

Language: Английский

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Projection of future drought and its impact on simulated crop yield over South Asia using ensemble machine learning approach

The Science of The Total Environment, Journal Year: 2021, Volume and Issue: 807, P. 151029 - 151029

Published: Oct. 19, 2021

Language: Английский

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