A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region
By Zhi-bin He, Xiaohu Wen, Hu Liu, Jun Du. 2014
330 citations recorded by the source corpus.
Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/72973298f34c8553d57c414f5ad2ff51f5847cec
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Environmental Engineering
- Xiaohu Wen
- Qi Feng
- Zhi-bin He
- Jianhua Si
- Lin-shan Yang
- Zhenliang Yin
- Mehdi Vafakhah
- Ebrahim Sharifi Garmdareh
In Global and Planetary Change
- Xiaohu Wen
- Qi Feng
- Jianhua Si
- Zhi-bin He
- Lin-shan Yang
- Zhenliang Yin
- Mehdi Vafakhah
- Ebrahim Sharifi Garmdareh
In Water Science and Technology
- Mehdi Vafakhah
- Ebrahim Sharifi Garmdareh
- S. Eslamian
- Tao Yang
- H. Tseng
- Xiaohu Wen
- Zhi-bin He
- Jianhua Si
Citations in the corpus
- Regional flood frequency analysis using support vector regression in arid and semi-arid regions of Iran (2018, result): “The present finding seems to be consistent with other research in this field (Shu and Ouarda 2008, Wu et al. 2008, Dogan et al. 2010, He et al. 2014).”
- Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions (2015, methodology): “Compared with ANN, SVM provides more reliable and better performance under the same training conditions (He et al. 2014).”
- A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework (2018, methodology): “These methods can be generally classified into two main categories: physical models and data-driven models (He et al. 2014; Zhang et al. 2015).”
- Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling (2019, methodology): “In addition, SVM performed better than NN and empirical models in modeling daily reference evapotranspiration [38], neuro fuzzy inference system (ANFIS) in river flow forecasting [29] and daily forecasting of dam water levels [39], and genetic programming (GP) in forecasting monthly discharge time s”
- Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment (2018, methodology): “As a consequence, the studies of hydrological flow modeling based on the past observed flow data of long and short term periods are becoming more and more important, especially in arid areas where water resources are limited (He et al. 2014, Terzi and Ergin 2013, Tongal and Berndtsson 2017).”