Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models

By A. Belayneh, Jan Adamowski, B. Khalil, B. Ozga-Zieliński. 2014

428 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ca4fe1e3e62d5e9ca10b635aa2c44899b0d660b0

How researchers see this paper

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

In Electrical and Electronic Engineering

1. Ravinesh C. Deo
2. Anurag Malik
3. Anil Kumar
4. Mukesh Tiwari
5. John M. Quilty
6. Xiao Pu
7. Liu Xiang
8. Yuhu Zhang

In Environmental Engineering

1. Ravinesh C. Deo
2. Anurag Malik
3. Mukesh Tiwari
4. Anil Kumar
5. John M. Quilty
6. Jan Adamowski
7. Xiao Pu
8. Liu Xiang

In Global and Planetary Change

1. Anurag Malik
2. Anil Kumar
3. Ravinesh C. Deo
4. Mukesh Tiwari
5. John M. Quilty
6. Jan Adamowski
7. M. Rathinasamy
8. Xiao Pu

Citations in the corpus

• Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling (2016, background): “…(response variable) and a set of potential drivers (candidate input variables or explanatory variables) that may contain relevant, irrelevant, and/or redundant information (e.g., Campisi et al., 2012; Rathinasamy et al., 2013; Belayneh et al., 2014; Nourani et al., 2014; Belayneh et al., 2016).”
• Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, methodology): “Drought is a complex hydrologic feature of arid and semiarid regions with strong implications on the sustainability of water resources, agriculture and environmental management (Saadat et al. 2011; Belayneh et al. 2014).”
• Meteorological drought prediction using heuristic approaches based on effective drought index: a case study in Uttarakhand (2020, result): “…studies conducted on meteorological drought forecasting using machine learning and stochastic models based on various drought indices such as (Mishra and Desai 2006; Belayneh et al. 2014; Deo and Şahin, 2015a, b; Deo et al. 2017a; Mouatadid et al. 2018; Ali et al. 2018; Abbasi et al. 2019).”
• Multi-models for SPI drought forecasting in the north of Haihe River Basin, China (2017, methodology): “Its modeling method is flexible, it also can grasp more information of original sequence (Mishra and Desai 2005, 2006; Mishra et al. 2007; Han et al. 2010; Belayneh et al. 2014).”