Pan evaporation estimation by relevance vector machine tuned with new metaheuristic algorithms using limited climatic data

• Published in: Engineering applications of computational fluid mechanics Vol. 17; no. 1
• Main Authors: Adnan, Rana Muhammad, Mostafa, Reham R., Dai, Hong-Liang, Heddam, Salim, Kuriqi, Alban, Kisi, Ozgur
• Format: Journal Article
• Language: English
• Published: Hong Kong Taylor & Francis 31.12.2023; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Accuracy; Algorithms; Data acquisition; Developing countries; Errors; Evaporation; Extraterrestrial radiation; Feasibility studies; Heuristic methods; improved Manta-Ray foraging optimization; LDCs; Machine learning; Multilayer perceptrons; Multilayers; Neural networks; Optimization; Pan evaporation prediction; relevance vector machine; temperature-based modelling

This study investigates the feasibility of a relevance vector machine tuned with improved Manta-Ray foraging optimization (RVM-IMRFO) in predicting monthly pan evaporation using limited climatic input data (e.g. temperature). The accuracy of the RVM-IMRFO was evaluated by comparing it with RVM tuned...