Approaches for Optimizing the Performance of Adaptive Neuro-Fuzzy Inference System and Least-Squares Support Vector Machine in Precipitation Modeling-Reference-Cited by-同舟云学术

Approaches for Optimizing the Performance of Adaptive Neuro-Fuzzy Inference System and Least-Squares Support Vector Machine in Precipitation Modeling

Published:2021-04 Issue:4 Volume:26 Page:04021010
ISSN:1084-0699
Container-title:Journal of Hydrologic Engineering
language:en
Short-container-title:J. Hydrol. Eng.

Author:

Azad Armin¹,Farzin Saeed²^ORCID,Sanikhani Hadi³,Karami Hojat⁴,Kisi Ozgur⁵^ORCID,Singh Vijay P.⁶

Affiliation:

1. Post Graduate Student, Dept. of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan Univ., Semnan 35131-19111, Iran.

2. Assistant Professor, Dept. of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan Univ., Semnan 35131-19111, Iran (corresponding author). ORCID: .

3. Assistant Professor, Faculty of Agriculture, Dept. of Water Engineering, Univ. of Kurdistan, Kurdistan 66177-15175, Iran.

4. Assistant Professor, Dept. of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan Univ., Semnan 35131-19111, Iran.

5. Professor, Dept. of Civil Engineering, Ilia State Univ., Tbilisi 0162, Georgia; Institute of Research and Development, Duy Tan Univ., Da Nang 550000, Vietnam. ORCID: .

6. Distinguished Professor, Regents Professor, and Caroline and William N.Lehrer Distinguished Chair in Water Engineering, Dept. of Biological and Agricultural Engineering and Zachry Dept. of Civil and Environmental Engineering, Texas A&M Univ., College Station, TX 77843.

Publisher

American Society of Civil Engineers (ASCE)

Subject

General Environmental Science,Water Science and Technology,Civil and Structural Engineering,Environmental Chemistry

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29HE.1943-5584.0002069

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