
Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown
Published: June 3, 2024
Language: Английский
Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown
Published: June 3, 2024
Language: Английский
Agricultural Water Management, Journal Year: 2024, Volume and Issue: 296, P. 108807 - 108807
Published: April 2, 2024
The reference evapotranspiration (ETo) is a key parameter in achieving sustainable use of agricultural water resources. To accurately acquire ETo under limited conditions, this study combined the northern goshawk optimization algorithm (NGO) with extreme gradient boosting (XGBoost) model to propose novel NGO-XGBoost model. performance was evaluated using meteorological data from 30 stations North China Plain and compared XGBoost, random forest (RF), k nearest neighbor (KNN) models. An ensemble embedded feature selection (EEFS) method results RF, adaptive (AdaBoost), categorical (CatBoost) models used obtain importance factors estimating ETo, thereby determine optimal combination inputs indicated that by top 3, 4, 5 important as input combinations, all achieved high estimation accuracy. It worth noting there were significant spatial differences precisions four models, but exhibited consistently precisions, global indicator (GPI) rankings 1st, range coefficient determination (R2), nash efficiency (NSE), root mean square error (RMSE), absolute (MAE) bias (MBE) 0.920–0.998, 0.902–0.998, 0.078–0.623 mm d−1, 0.058–0.430 −0.254–0.062 respectively. Furthermore, accuracy varied across different seasons, which more significantly affected humidity wind speed winter. When target station insufficient, trained historical neighboring still maintained precision. Overall, recommends reliable for provides calculating absence data.
Language: Английский
Citations
11The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 927, P. 171842 - 171842
Published: March 20, 2024
Language: Английский
Citations
4Agronomy, Journal Year: 2025, Volume and Issue: 15(3), P. 599 - 599
Published: Feb. 27, 2025
Accurately estimating reference crop evapotranspiration (ETo) improves agricultural water use efficiency. However, the accuracy of ETo estimation needs to be further improved in Northeast region China, country’s main grain production area. In this research, meteorological data from 30 sites China over past 59 years (1961–2019) were selected evaluate simulation 11 models. By using least square method (LSM) and three population heuristic intelligent algorithms—a genetic algorithm (GA), a particle swarm optimization (PSO), differential evolution (DE)—the parameters eleven kinds models optimized, respectively, model suitable for northeast was selected. The results showed that radiation-based Jensen Haise (JH) had best among empirical models, with R2 0.92. Hamon an acceptable accuracy, while combination low ranges 0.74–0.88. After LSM optimization, all been significantly by 0.58–12.1%. algorithms Door optimized GA DE higher Although JH requires more factors than model, it shows better stability. Regardless original formula or various algorithms, has is greater 0.91. Therefore, when only temperature radiation available, recommended estimate ETo, respectively; both underestimated absolute error range 0.01–0.02 mm d−1 compared Penman–Monteith (P–M) equation. When could used less 0.01 d−1. This study provided accurate within regional scope incomplete data.
Language: Английский
Citations
0Hydrological Sciences Journal, Journal Year: 2025, Volume and Issue: unknown
Published: April 9, 2025
Language: Английский
Citations
0Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127829 - 127829
Published: April 1, 2025
Language: Английский
Citations
0Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133430 - 133430
Published: May 1, 2025
Language: Английский
Citations
0Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: 15(7), P. 3377 - 3394
Published: June 25, 2024
ABSTRACT The empirical models commonly employed as alternatives for estimating evapotranspiration provide constraints and yield inaccurate results when applied to Nigeria. This study aims develop novel enhance (ET0) estimation accuracy in coefficients of non-linear equations were optimised using the particle swarm optimisation (PSO) algorithm development two new ET0 Nigeria, Awhari1 (temperature-based) Awhari2 (mass transfer-based). ERA5 reanalysis data with a 0.1° × resolution was used. rigorously assessed against FAO-56 Penman–Monteith method, resulting Kling–Gupta efficiency (KGE) percentage bias (Pbias) values 0.75, 6.49, 0.92, 5.67, respectively. spatial distribution analysis performance metrics showed both exhibited superior across diverse climatic zones incorporation PSO model development, coupled analysis, highlights study's multidimensional approach. indicates that they can be valuable tools water resource management, irrigation planning, sustainable agriculture practices
Language: Английский
Citations
3Water Resources Management, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 22, 2025
Language: Английский
Citations
0Agricultural Water Management, Journal Year: 2024, Volume and Issue: 307, P. 109268 - 109268
Published: Dec. 24, 2024
Language: Английский
Citations
2Agricultural Water Management, Journal Year: 2024, Volume and Issue: 306, P. 109193 - 109193
Published: Nov. 26, 2024
Language: Английский
Citations
0