Support vector machine applications in the field of hydrology: A review

By Sujay Raghavendra Naganna, Paresh Chandra Deka. Applied Soft Computing, 2014

687 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/21a1eade787a5a49302533e7e28911581cca428e

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. Kwok-Wing Chau
2. A. Danandeh Mehr
3. V. Karimi Khosrowshahi
4. Pınar Öztürk
5. Y. P. Li
6. Amir Mosavi
7. Mohammad Ali Ghorbani
8. Vahid Nourani

In Environmental Engineering

1. Amir Mosavi
2. Pınar Öztürk
3. Kwok-Wing Chau
4. Vahid Nourani
5. Tailian Liu
6. A. Danandeh Mehr
7. V. Karimi Khosrowshahi
8. Hristos Tyralis

In Water Science and Technology

1. Guohe Huang
2. Amir Mosavi
3. Pınar Öztürk
4. Vahid Nourani
5. Kwok-Wing Chau
6. Y. P. Li
7. A. Danandeh Mehr
8. V. Karimi Khosrowshahi

Citations in the corpus

• A hybrid support vector regression–firefly model for monthly rainfall forecasting (2018, methodology): “1 3 space vectors, the SVM regression (hereafter SVR) function is expressed as (Raghavendra and Deka, 2014):”
• Probabilistic Prediction for Monthly Streamflow through Coupling Stepwise Cluster Analysis and Quantile Regression Methods (2016, methodology): “…Burn 2012; Adamowski and Karapataki 2008; Adamowski et al. 2012; Sachindra et al. 2013), artificial intelligence (Cigizoglu 2005; Turan and Yurdusev 2009; Wang et al. 2009; Yang et al. 2015) and machine learning methods (Okkan and Serbes 2012; Sadri and Burn 2012; Sujay Raghavendra and Deka 2014).”
• A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources (2019, background): “[29] Support vector machine Rainfall forecasting, runoff forecasting, streamflow forecasting, sediment yield forecasting, evaporation and evapotranspiration forecasting, lake and water level forecasting, flood forecasting, drought forecasting, groundwater level forecasting, soil moisture estimation,”
• Short-term prediction of groundwater level using improved random forest regression with a combination of random features (2018, methodology): “Although SVM is superior to ANN in reflecting the dynamic variation in GWL, it takes more time because of trial and error (Raghavendra and Deka 2014).”
• Flood Prediction Using Machine Learning Models: Literature Review (2018, background): “In fact, the literature includes only a limited number of surveys on specific ML methods in specific hydrology fields [53–55].”