Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq

By Zaher Mundher Yaseen, Othman Jaafar, Ravinesh C. Deo, Ozgur Kisi, Jan Adamowski, John M. Quilty, Ahmed El-Shafie. 2016

299 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/5723220f8368d56f03a1e89e5d96b25a81326181

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zaher Mundher Yaseen
2. Qi Feng
3. Xiaohu Wen
4. Zhi-bin He
5. Jianhua Si
6. Lin-shan Yang
7. Zhenliang Yin
8. Ravinesh C. Deo

In Electrical and Electronic Engineering

1. Zaher Mundher Yaseen
2. Mohammad Ali Ghorbani
3. Ravinesh C. Deo
4. O. Terzi
5. Vahid Karimi
6. Xiaohu Wen
7. Zhi-bin He
8. Jianhua Si

In Environmental Engineering

1. Zaher Mundher Yaseen
2. Mohammad Ali Ghorbani
3. Vahid Karimi
4. O. Terzi
5. Xiaohu Wen
6. Qi Feng
7. Zhi-bin He
8. Jianhua Si

Citations in the corpus

• 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): “…types of datadriven models such as ANN, SVR, and ANFIS have been employed for hydrological modeling in general, and more specifically, in time series forecasting, with the performances of such studies appearing to yield very good outcomes (e.g., Deo and Şahin 2016; Yaseen et al. 2016, 2018).”
• A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction (2019, background): “Monthly stream flow prediction plays an important role in the field of water resources and hydrologic analysis for solving problems of water allocation, supply, irrigation scheduling, reservoir operation, etc. (Yaseen et al. 2016).”
• Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey (2018, background): “As with the case of the other hydrological data (e.g., streamflow) (Deo and Şahin 2016; Yaseen et al. 2016), the lake water level is also expected to display a high degree of serial correlation (or hydrological persistence) that arises from evaporation, transpiration, recharge and other…”
• A systematic literature review on machine learning applications for sustainable agriculture supply chain performance (2020, methodology): “The ML in irrigation management is used for estimation of evapotranspiration (Saggi and Jain, 2019; Torres et al. 2011), streamflows (Yaseen et al. 2016), and real-time management of reservoir release (Khalil et al. 2005).”