P
Paresh Chandra Deka
Researcher at National Institute of Technology, Karnataka
Publications - 55
Citations - 1704
Paresh Chandra Deka is an academic researcher from National Institute of Technology, Karnataka. The author has contributed to research in topics: Adaptive neuro fuzzy inference system & Wavelet. The author has an hindex of 18, co-authored 52 publications receiving 1142 citations. Previous affiliations of Paresh Chandra Deka include University of Kentucky & Indian Institute of Technology Guwahati.
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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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An extreme learning machine approach for modeling evapotranspiration using extrinsic inputs
Amit Patil,Paresh Chandra Deka +1 more
TL;DR: ELM is a simple yet efficient algorithm which exhibited good performance and can be recommended for estimating weekly ETo and it was found that use of ETo values from another station can help in improving the efficiency of ML models in limited data scenario.
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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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Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time
Paresh Chandra Deka,R. Prahlada +1 more
TL;DR: The proposed wavelet model (WLNN) that makes use of multiresolution time series as input, allows for more accurate and consistent predictions with respect to classical ANN models.
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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.