Review of: "Determination of Evapotranspiration and Crop Coefficients of Irrigated Legumes on Different Soil Textures Using the FAO56 Approach" DOI Creative Commons
Aman Srivastava

Published: April 20, 2024

Language: Английский

Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models DOI Creative Commons
Khabat Khosravi, Aitazaz A. Farooque, Seyed Amir Naghibi

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 85, P. 102933 - 102933

Published: Dec. 7, 2024

Language: Английский

Citations

11
Estimation of crop evapotranspiration using statistical and machine learning techniques with limited meteorological data: a case study in Udham Singh Nagar, India DOI
Anurag Satpathi, Abhishek Danodia, Ajeet Singh Nain

et al.

Theoretical and Applied Climatology, Journal Year: 2024, Volume and Issue: 155(6), P. 5279 - 5296

Published: April 3, 2024

Language: Английский

Citations

10
Hybrid catboost models optimized with metaheuristics for predicting shear strength in rock joints DOI
Xiaohua Ding, Mahdi Hasanipanah, Mohammad Matin Rouhani

et al.

Bulletin of Engineering Geology and the Environment, Journal Year: 2025, Volume and Issue: 84(3)

Published: Feb. 25, 2025

Language: Английский

Citations

1
Principal Component Analysis (PCA) and feature importance-based dimension reduction for Reference Evapotranspiration (ET0) predictions of Taif, Saudi Arabia DOI
Rab Nawaz Bashir, Olfa Mzoughi, Muhammad Ali Shahid

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 222, P. 109036 - 109036

Published: May 21, 2024

Language: Английский

Citations

5
Revolutionizing the Future of Hydrological Science: Impact of Machine Learning and Deep Learning amidst Emerging Explainable AI and Transfer Learning DOI Creative Commons
Rajib Maity, Aman Srivastava,

Subharthi Sarkar

et al.

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

Published: Nov. 9, 2024

Language: Английский

Citations

5
Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India DOI Creative Commons
Jitendra Rajput, Nand Lal Kushwaha, Aman Srivastava

et al.

Water Practice & Technology, Journal Year: 2024, Volume and Issue: 19(7), P. 2655 - 2672

Published: June 4, 2024

ABSTRACT Accurate prediction of pan evaporation and mean temperature is crucial for effective water resources management, influencing the hydrological cycle impacting availability. This study focused on New Delhi's semi-arid climate, data spanning 31 years (1990–2020) were used to predict these variables using advanced algorithms such as Bagging, Random Subspace (RSS), M5P, REPTree. The models rigorously evaluated 10 performance metrics, including correlation coefficient, absolute error (MAE), Nash–Sutcliffe Efficiency (NSE) model coefficient. Bagging emerged best with indices values r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, MAPE 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90, 22.0, respectively, during testing phase prediction. In predicting temperature, reported results 0.90 phase. These findings offer valuable insights enhancing relative humidity in diverse climatic conditions. model's robust underscores its potential application resource management.

Language: Английский

Citations

4
Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar DOI Creative Commons
Raouf Hassan, Mohammad Reza Kazemi

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 30, 2025

Language: Английский

Citations

0
Evaporation forecasting using different machine learning models in Beni Haroun Dam, Algeria DOI

Zohra Baba Amer,

Boutouatou Farah

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(2)

Published: Jan. 18, 2025

Language: Английский

Citations

0
Optimizing water management and climate-resilient agriculture in rice-fallow regions of the Dwarakeswar river basin using ML models DOI Creative Commons

Chiranjit Singha,

Satiprasad Sahoo, Ajit Govind

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 7(4)

Published: April 11, 2025

Language: Английский

Citations

0
Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study DOI Creative Commons
Izhar Hussain, Kok Boon Ching,

Chessda Uttraphan

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 8, 2025

An accurate energy consumption prediction becomes crucial with increasing electric vehicle usage for effective power grid management. This research examined the performance of eleven machine learning models this purpose: Ridge Regression, Lasso K-Nearest Neighbors, Gradient Boosting, Support Vector Multi-Layer Perceptron, XGBoost, CatBoost, LightGBM, Gaussian Processes Regression(GPR) and Extra Trees Regressor, considering real historical data from Colorado. The were evaluated using different metrics: Mean Absolute Error (MAE), Squared (MSE), R², Root Error(RMSE) Normalized Error(NRMSE), visual analyses through scatter plots time series plots. best model observed was which had an MAE 0.5888, MSE 3.2683, R² value 0.9592, RMSE 1.8078 NRMSE 0.020. Boosting KNN also returned good results, although they slightly more dispersed. Nevertheless, while non-linear like MLP, LightGBM linear such as Regression offer valuable insights, exhibit shortcomings in estimating energy, especially at extreme levels, highlighting limitations capturing complex interactions. study focuses on their applicability to projections demonstrate how well ensemble may capture intricate patterns series. These cutting-edge techniques might greatly enhance demand predictions.

Language: Английский

Citations

0