Predicting Rainfall Using Inclusive Multiple Model and Radial Basis Function Neural Network

Mohammad Ehteram (), Akram Seifi () and Fatemeh Barzegari Banadkooki ()
Additional contact information
Mohammad Ehteram: Semnan University, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering
Akram Seifi: Vali-e-Asr University of Rafsanjan, Department of Water Science and Engineering, College of Agriculture
Fatemeh Barzegari Banadkooki: Payame Noor University, Agricultural Department

Chapter Chapter 12 in Application of Machine Learning Models in Agricultural and Meteorological Sciences, 2023, pp 101-115 from Springer

Abstract: Abstract This study used the salp swarm algorithm (SSA), Henry gas solubility optimization algorithm (HGSOA), and crow optimization algorithm (COA) to adjust the radial basis function neural network (RABFN) parameters for predicting monthly rainfall. Then, a new ensemble model was created using the outputs of RABFN, RABFN-SSA, RABFN-HGSOA, and RABFN-COA. The new ensemble model was named inclusive multiple model (IMM). This study indicated that the ensemble models improved the efficiency of the optimized RABFN models. The training MAE of the IMM, RABFN-HGSOA, RABFN-SSA, RABFN-PSO, and RBFN models was 0.987, 1.35, 1.47, 1.58, and 2.21 mm. The IMM reduced the testing MAE of the IMM, RABFN-HGSOA, RABFN-SSA, RABFN-PSO, and RBFN models by 32%, 37%, 42%, and 55%, respectively. Also, the HGSOA had better performance than the SSA and COA.

Keywords: Rainfall; Optimization algorithms; Individual models; Ensemble models (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-19-9733-4_12

Ordering information: This item can be ordered from
http://www.springer.com/9789811997334

DOI: 10.1007/978-981-19-9733-4_12

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().