Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point

By Mohammad Ali Ghorbani, Shahaboddin Shamshirband, D. Z. Haghi, A. Azani, Hossein Bonakdari, Isa Ebtehaj. 2017

143 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Environmental Engineering, Soil Science

View Paper: https://www.semanticscholar.org/paper/e03fffb5e4a859dfe2ea0ff5c824beac9a05aab4

How researchers see this paper

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

In Civil and Structural Engineering

1. Bahram Gharabaghi
2. Mohammad Zeynoddin
3. Isa Ebtehaj
4. Hossein Bonakdari
5. Hamid Moeeni
6. Kwok-Wing Chau
7. Ravinesh C. Deo
8. Zaher Mundher Yaseen

In Environmental Engineering

1. Babak Mohammadi
2. Hossein Bonakdari
3. Isa Ebtehaj
4. Zaher Mundher Yaseen
5. Ravinesh C. Deo
6. Kwok-Wing Chau
7. Mohammad Ali Ghorbani
8. Bahram Gharabaghi

In Soil Science

1. Babak Mohammadi
2. Hossein Bonakdari

Citations in the corpus

• A hybrid support vector regression–firefly model for monthly rainfall forecasting (2018, background): “2016), velocity estimation in sewer pipes (Ebtehaj and Bonakdari 2016), and field capacity prediction (Ghorbani et al. 2017), to the best of the authors’ knowledge, the potential of SVR–FFA to forecast monthly rainfall has never been explored so far.”
• Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA (2019, background): “Since there are many natural sources of inspiration, a host of nature-inspired optimization algorithms can be found in the literature, but just a few of these algorithms have been used in hydrological-related studies (Mohammadi et al. 2015; Ghorbani et al. 2017; Ghorbani et al. 2018; Deo et al. 2018”
• New insights into soil temperature time series modeling: linear or nonlinear? (2019, background): “…artificial intelligence (AI)-based techniques for predicting real-world problems has grown tremendously (Sharafi et al. 2016; Bonakdari et al. 2017; Ghorbani et al. 2017; Najafzadeh et al. 2014, 2015, 2017) and especially for predicting ST (Talaee 2014; Shirvani et al. 2015; Tabari et al. 2015;…”
• Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran (2018, background): “2016), prediction of soil field capacity and permanent point (Ghorbani et al. 2017), and the prediction electric loads (Kavousi-Fard et al.”
• Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran (2018, methodology): “Ghorbani et al. (2017) utilized SVR and SVR coupled with a firefly algorithm for predicting the field capacity (FC) and permanent wilting point (PWP) of 215 soil samples collected from the East Azerbaijan province in Iran, finding that the coupled method performed better than the SVR method.”