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

Опубликована: Апрель 20, 2024

Язык: Английский

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

и другие.

Bulletin of Engineering Geology and the Environment, Год журнала: 2025, Номер 84(3)

Опубликована: Фев. 25, 2025

Язык: Английский

Процитировано

2
Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models DOI Creative Commons
Khabat Khosravi, Aitazaz A. Farooque, Seyed Amir Naghibi

и другие.

Ecological Informatics, Год журнала: 2024, Номер 85, С. 102933 - 102933

Опубликована: Дек. 7, 2024

Язык: Английский

Процитировано

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

и другие.

Theoretical and Applied Climatology, Год журнала: 2024, Номер 155(6), С. 5279 - 5296

Опубликована: Апрель 3, 2024

Язык: Английский

Процитировано

10
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

и другие.

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 222, С. 109036 - 109036

Опубликована: Май 21, 2024

Язык: Английский

Процитировано

6
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

и другие.

Applied Computing and Geosciences, Год журнала: 2024, Номер 24, С. 100206 - 100206

Опубликована: Ноя. 9, 2024

Язык: Английский

Процитировано

6
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

и другие.

Water Practice & Technology, Год журнала: 2024, Номер 19(7), С. 2655 - 2672

Опубликована: Июнь 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.

Язык: Английский

Процитировано

4
Evaporation forecasting using different machine learning models in Beni Haroun Dam, Algeria DOI

Zohra Baba Amer,

Boutouatou Farah

Theoretical and Applied Climatology, Год журнала: 2025, Номер 156(2)

Опубликована: Янв. 18, 2025

Язык: Английский

Процитировано

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

и другие.

Deleted Journal, Год журнала: 2025, Номер 7(4)

Опубликована: Апрель 11, 2025

Язык: Английский

Процитировано

0
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, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 30, 2025

Язык: Английский

Процитировано

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

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 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.

Язык: Английский

Процитировано

0