Principal Component Analysis (PCA) and feature importance-based dimension reduction for Reference Evapotranspiration (ET0) predictions of Taif, Saudi Arabia

[1]  Yingbao Yang,et al.  The impact of clear-sky biases of land surface temperature on monthly evapotranspiration estimation , 2024, Int. J. Appl. Earth Obs. Geoinformation.

[2]  Claudia Gonzalez Viejo,et al.  Actual evapotranspiration and energy balance estimation from vineyards using micro-meteorological data and machine learning modeling , 2024, Agricultural Water Management.

[3]  F. Granata,et al.  Advanced evapotranspiration forecasting in Central Italy: Stacked MLP-RF algorithm and correlated Nystrom views with feature selection strategies , 2024, Comput. Electron. Agric..

[4]  Puyi Guo,et al.  Establishment of a Reference Evapotranspiration Forecasting Model Based on Machine Learning , 2024, Agronomy.

[5]  W. El-Sofany,et al.  Water scarcity in the Kingdom of Saudi Arabia. , 2024, Environmental science and pollution research international.

[6]  D. She,et al.  The intercomparison of six 0.1°×0.1° spatial resolution evapotranspiration products across mainland China , 2024, Journal of Hydrology.

[7]  S. Bararkhanpour Ahmadi,et al.  Evaluation of TerraClimate gridded data in investigating the changes of reference evapotranspiration in different climates of Iran , 2024, Journal of Hydrology: Regional Studies.

[8]  Bo-Jein Kuo,et al.  Using Artificial Intelligence Algorithms to Estimate and Short-Term Forecast the Daily Reference Evapotranspiration with Limited Meteorological Variables , 2024, Agriculture.

[9]  Chuqiang Chen,et al.  Machine Learning-Based Estimation of Daily Cropland Evapotranspiration in Diverse Climate Zones , 2024, Remote. Sens..

[10]  H. Elzain,et al.  Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study. , 2024, Journal of environmental management.

[11]  Shima Amani,et al.  Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration , 2024, Water Resources Management.

[12]  S. Heddam,et al.  Evaluation of CatBoost Method for Predicting Weekly Pan Evaporation in Subtropical and Sub-Humid Regions , 2024, Pure and Applied Geophysics.

[13]  Xuelong Chen,et al.  Long-term monthly 0.05° terrestrial evapotranspiration dataset (1982–2018) for the Tibetan Plateau , 2024, Earth System Science Data.

[14]  Shang Chen,et al.  Parameterization of the Ångström–Prescott formula based on machine learning benefit estimation of reference crop evapotranspiration with missing solar radiation data , 2024, Hydrological Processes.

[15]  Zhaomei Qiu,et al.  Estimating maize evapotranspiration based on hybrid back-propagation neural network models and meteorological, soil, and crop data. , 2024, International journal of biometeorology.

[16]  A. Abdelbaki,et al.  Gradually optimization of cropping pattern in Saudi Arabia for sustainable agricultural development until 2030 , 2024, Ain Shams Engineering Journal.

[17]  S. Evett,et al.  Reference Evapotranspiration Estimation Using Genetic Algorithm-Optimized Machine Learning Models and Standardized Penman–Monteith Equation in a Highly Advective Environment , 2023, Water.

[18]  Yong Jie Wong,et al.  Enhancing Sustainable Urban Planning through GIS and Multiple-Criteria Decision Analysis: A Case Study of Green Space Infrastructure in Taif Province, Saudi Arabia , 2023, Water.

[19]  M. Najim,et al.  Water Scarcity Management to Ensure Food Scarcity through Sustainable Water Resources Management in Saudi Arabia , 2023, Sustainability.

[20]  Khalid Alkhuzai Management of irrigation water in Al-Baha Region, Saudi Arabia using simple and alternative equation to Penman-Monteith equation , 2023, Proceedings of the Institution of Civil Engineers - Water Management.

[21]  Zhongmin Hu,et al.  The increasing contribution of greening to the terrestrial evapotranspiration in China , 2023, Ecological Modelling.

[22]  Jiabing Cai,et al.  Evaluation and verification of two evapotranspiration models based on precision screening and partitioning of field temperature data , 2023, Agricultural Water Management.

[23]  J. Tanny,et al.  Quantifying winter wheat evapotranspiration and crop coefficients under sprinkler irrigation using eddy covariance technology in the North China Plain , 2023, Agricultural Water Management.

[24]  Tiejun Wang,et al.  Comparison of environmental controls on daily actual evapotranspiration dynamics among different terrestrial ecosystems in China. , 2023, The Science of the total environment.

[25]  Rafael Gomes Alves,et al.  Development of a Digital Twin for smart farming: Irrigation management system for water saving , 2023, Journal of Cleaner Production.

[26]  L. Incrocci,et al.  IoT based dynamic Bayesian prediction of crop evapotranspiration in soilless cultivations , 2023, Comput. Electron. Agric..

[27]  Xianli Xu,et al.  Assessing the variations of evapotranspiration and its environmental controls over a subalpine wetland valley in China , 2023, Journal of Hydrology.

[28]  Mona Ghafouri-Azar,et al.  Meteorological Influences on Reference Evapotranspiration in Different Geographical Regions , 2023, Water.

[29]  S. Samadianfard,et al.  Investigating the roles of different extracted parameters from satellite images in improving the accuracy of daily reference evapotranspiration estimation , 2023, Applied Water Science.

[30]  S. Kanwal,et al.  Remote Sensing in Precision Agriculture for Irrigation Management , 2023, The 1st International Precision Agriculture Pakistan Conference 2022 (PAPC 2022)—Change the Culture of Agriculture.

[31]  Sushma Jain,et al.  Modeling Evapotranspiration in IoT based WSN for Irrigation Scheduling: An Optimized DL Approach , 2022, GLOBECOM 2022 - 2022 IEEE Global Communications Conference.

[32]  Pouya Aghelpour,et al.  Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions , 2022, Scientific Reports.

[33]  M. Ma,et al.  Evaluation of Empirical and Machine Learning Approaches for Estimating Monthly Reference Evapotranspiration with Limited Meteorological Data in the Jialing River Basin, China , 2022, International journal of environmental research and public health.

[34]  Jinling Kong,et al.  Spatiotemporal Variation in Actual Evapotranspiration and the Influencing Factors in Ningxia from 2001 to 2020 , 2022, International journal of environmental research and public health.

[35]  A. Fares,et al.  Calibration and Evaluation of Empirical Methods to Estimate Reference Crop Evapotranspiration in West Texas , 2022, Water.

[36]  K. Nikolakopoulos,et al.  Artificial Neural Networks for the Prediction of the Reference Evapotranspiration of the Peloponnese Peninsula, Greece , 2022, Water.

[37]  Hao Li,et al.  Hybrid Machine Learning Approach for Evapotranspiration Estimation of Fruit Tree in Agricultural Cyber–Physical Systems , 2022, IEEE Transactions on Cybernetics.

[38]  M. Shao,et al.  Assessing soil water balance to optimize irrigation schedules of flood-irrigated maize fields with different cultivation histories in the arid region , 2022, Agricultural Water Management.

[39]  P. Heramb,et al.  Modelling reference evapotranspiration using Gene Expression Programming and Artificial Neural Network at Pantnagar, India , 2022, Information Processing in Agriculture.

[40]  Pei Wang,et al.  Diurnal Evapotranspiration and Its Controlling Factors of Alpine Ecosystems during the Growing Season in Northeast Qinghai-Tibet Plateau , 2022, Water.

[41]  Yuk Feng Huang,et al.  Reference evapotranspiration prediction using high-order response surface method , 2022, Theoretical and Applied Climatology.

[42]  Matteo Gentilucci,et al.  Calculation of Potential Evapotranspiration and Calibration of the Hargreaves Equation Using Geostatistical Methods over the Last 10 Years in Central Italy , 2021, Geosciences.

[43]  Jalal Shiri,et al.  Estimation of Reference Evapotranspiration Using Spatial and Temporal Machine Learning Approaches , 2021, Hydrology.

[44]  Shijun Sun,et al.  Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods , 2020 .

[45]  R. Allen,et al.  Conditioning point and gridded weather data under aridity conditions for calculation of reference evapotranspiration , 2020 .

[46]  G. A. Abubakar,et al.  Spatial and temporal variability analysis of green and blue evapotranspiration of wheat in the Egyptian Nile Delta from 1997 to 2017 , 2020, Journal of Hydrology.

[47]  Imran Sarwar Bajwa,et al.  Internet of Things and Machine-Learning-Based Leaching Requirements Estimation for Saline Soils , 2020, IEEE Internet of Things Journal.

[48]  Vili Podgorelec,et al.  Decision trees , 2018, Encyclopedia of Database Systems.

[49]  R. Bashir,et al.  Machine Learning Based Prediction of Reference Evapotranspiration (ET0) Using IoT , 2022, IEEE Access.

[50]  Ryan G. McClarren,et al.  Decision Trees and Random Forests for Regression and Classification , 2021, Machine Learning for Engineers.