A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data
Modeling potential evapotranspiration (ET0) is an important issue for water resources planning and management projects involving droughts and flood hazards. Evapotranspiration, one of the main components of the hydrological cycle, is highly effective in drought monitoring. This study investigates the efficiency of two machine-learning methods, random vector functional link (RVFL) and relevance vector machine (RVM), improved with new metaheuristic algorithms, quantum-based avian navigation optimizer algorithm (QANA), and artificial hummingbird algorithm (AHA) in modeling ET0 using limited climatic data, minimum temperature, maximum temperature, and extraterrestrial radiation. The outcomes of the hybrid RVFL-AHA, RVFL-QANA, RVM-AHA, and RVM-QANA models compared with single RVFL and RVM models. Various input combinations and three data split scenarios were employed. The results revealed that the AHA and QANA considerably improved the efficiency of RVFL and RVM methods in modeling ET0. Considering the periodicity component and extraterrestrial radiation as inputs improved the prediction accuracy of the applied methods.
Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data
Modeling potential evapotranspiration (ET0) is an important issue for water resources planning and management projects involving droughts and flood hazards. Evapotranspiration, one of the main components of the hydrological cycle, is highly effective in drought monitoring. This study investigates the efficiency of two machine-learning methods, random vector functional link (RVFL) and relevance vector machine (RVM), improved with new metaheuristic algorithms, quantum-based avian navigation optimizer algorithm (QANA), and artificial hummingbird algorithm (AHA) in modeling ET0 using limited climatic data, minimum temperature, maximum temperature, and extraterrestrial radiation. The outcomes of the hybrid RVFL-AHA, RVFL-QANA, RVM-AHA, and RVM-QANA models compared with single RVFL and RVM models. Various input combinations and three data split scenarios were employed. The results revealed that the AHA and QANA considerably improved the efficiency of RVFL and RVM methods in modeling ET0. Considering the periodicity component and extraterrestrial radiation as inputs improved the prediction accuracy of the applied methods.
Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data
Reham R. Mostafa (author) / Ozgur Kisi (author) / Rana Muhammad Adnan (author) / Tayeb Sadeghifar (author) / Alban Kuriqi (author)
2023
Article (Journal)
Electronic Resource
Unknown
machine learning , modeling potential evapotranspiration , relevance vector machine (RVM) , random vector functional link (RVFL) hybrid modeling , quantum-based avian navigation optimizer algorithm (QANA) , artificial hummingbird algorithm (AHA) , Hydraulic engineering , TC1-978 , Water supply for domestic and industrial purposes , TD201-500
Metadata by DOAJ is licensed under CC BY-SA 1.0
Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique
| British Library Online Contents | 2003
ESTIMATING REFERENCE EVAPOTRANSPIRATION FROM LIMITED CLIMATIC DATA USING ARTIFICIAL NEURAL NETWORKS
| Taylor & Francis Verlag | 2009
Estimating Reference Evapotranspiration Using Limited Weather Data
| British Library Online Contents | 2009