Evaluating empirical and machine learning approaches for reference evapotranspiration estimation using limited climatic variables in Nepal

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104254 - 104254

Published: Feb. 1, 2025

Language: Английский

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Development of wavelet-based Kalman Online Sequential Extreme Learning Machine optimized with Boruta-Random Forest for drought index forecasting

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 117, P. 105545 - 105545

Published: Nov. 10, 2022

Language: Английский

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Simulation of daily maize evapotranspiration at different growth stages using four machine learning models in semi-humid regions of northwest China

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 617, P. 128947 - 128947

Published: Dec. 14, 2022

Language: Английский

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Monthly sodium adsorption ratio forecasting in rivers using a dual interpretable glass-box complementary intelligent system: Hybridization of ensemble TVF-EMD-VMD, Boruta-SHAP, and eXplainable GPR

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 237, P. 121512 - 121512

Published: Sept. 13, 2023

Language: Английский

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Performance of machine learning algorithms for multi-step ahead prediction of reference evapotranspiration across various agro-climatic zones and cropping seasons

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 620, P. 129418 - 129418

Published: March 22, 2023

Language: Английский

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Multivariate data decomposition based deep learning approach to forecast one-day ahead significant wave height for ocean energy generation

Renewable and Sustainable Energy Reviews, Journal Year: 2023, Volume and Issue: 185, P. 113645 - 113645

Published: Aug. 15, 2023

Language: Английский

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Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

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

Published: July 1, 2024

Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In current research, EC two Australian rivers (Albert River Barratta Creek) was forecasted up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method used determine significant inputs (time series lagged data) model. To compare performance Boruta-XGB-CNN-LSTM models, three machine approaches-multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), extreme gradient boosting (XGBoost) were used. Different statistical metrics, such correlation coefficient (R), root mean square error (RMSE), absolute percentage error, assess models' performance. From years data in both rivers, 7 (2012-2018) training set, 3 (2019-2021) testing models. Application model forecasting day ahead showed that stations, can forecast parameter better than other models test dataset (R = 0.9429, RMSE 45.6896, MAPE 5.9749 Albert River, R 0.9215, 43.8315, 7.6029 Creek). Considering this 3-10 EC. results very capable next days. by increasing horizon from days, slightly decreased. study show be good soft computing accurately how will change rivers.

Language: Английский

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Application of hybrid machine learning-based ensemble techniques for rainfall-runoff modeling

Earth Science Informatics, Journal Year: 2023, Volume and Issue: 16(3), P. 2475 - 2495

Published: July 19, 2023

Language: Английский

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Multi-step ahead hourly forecasting of air quality indices in Australia: Application of an optimal time-varying decomposition-based ensemble deep learning algorithm

Atmospheric Pollution Research, Journal Year: 2023, Volume and Issue: 14(6), P. 101752 - 101752

Published: April 20, 2023

Language: Английский

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Revolutionizing the Future of Hydrological Science: Impact of Machine Learning and Deep Learning amidst Emerging Explainable AI and Transfer Learning

Applied Computing and Geosciences, Journal Year: 2024, Volume and Issue: 24, P. 100206 - 100206

Published: Nov. 9, 2024

Language: Английский

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