S
Sujay Raghavendra Naganna
Researcher at National Institute of Technology, Karnataka
Publications - 31
Citations - 1192
Sujay Raghavendra Naganna is an academic researcher from National Institute of Technology, Karnataka. The author has contributed to research in topics: Artificial neural network & Computer science. The author has an hindex of 13, co-authored 23 publications receiving 711 citations. Previous affiliations of Sujay Raghavendra Naganna include Visvesvaraya Technological University & Siddaganga Institute of Technology.
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Support vector machine applications in the field of hydrology: A review
TL;DR: This paper reviews the state-of-the-art and focuses over a wide range of applications of SVMs in the field of hydrology, providing a brief synopsis of the techniques of SVM and other emerging ones (hybrid models), which have proven useful in the analysis of the various hydrological parameters.
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Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting
TL;DR: In this paper, two machine learning models, Multivariate Adaptive Regression Splines (MARS) and M5 Model Trees (MT), have been applied to simulate the groundwater level (GWL) fluctuations of three shallow open wells within diverse unconfined aquifers.
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Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms
Sujay Raghavendra Naganna,Paresh Chandra Deka,Mohammad Ali Ghorbani,Seyed Mostafa Biazar,Nadhir Al-Ansari,Zaher Mundher Yaseen +5 more
TL;DR: The hybridization of MLP with nature-inspired optimization algorithms boosted the estimation accuracy that is clearly owing to the tuning robustness, and the applied methodology showed very convincing results for both inspected climate zones.
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Hourly River Flow Forecasting: Application of Emotional Neural Network Versus Multiple Machine Learning Paradigms
Zaher Mundher Yaseen,Sujay Raghavendra Naganna,Zulfaqar Sa’adi,Pijush Samui,Mohammad Ali Ghorbani,Mohammad Ali Ghorbani,Sinan Q. Salih,Sinan Q. Salih,Shamsuddin Shahid +8 more
TL;DR: The results clearly advocate the ENN as a promising artificial intelligence technique for accurate forecasting of hourly river flow in the form of real-time.
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Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet–Support vector regression
TL;DR: In this paper, a hybrid Wavelet packet-Support vector regression (WP-SVR) model is proposed for forecasting monthly groundwater level fluctuations observed in three shallow unconfined coastal aquifers.