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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 12/2020

16.05.2019 | Soft Computing Techniques: Applications and Challenges

Groundwater level forecasting using soft computing techniques

verfasst von: N. Natarajan, Ch. Sudheer

Erschienen in: Neural Computing and Applications | Ausgabe 12/2020

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Abstract

Knowledge of groundwater level is very important in studies dealing with utilization and management of groundwater supply. Earlier studies have reported that ELM performs better than SVM for groundwater level prediction. This has been verified by comparing the prediction of groundwater levels at six locations in the district of Vizianagaram, Andhra Pradesh, using ANN, GP, SVM and ELM. Based on the comparison, it is observed that the performance of ELM is the best compared to other models. ELM is capable of predicting the nonlinear behavior of the groundwater levels. SVM performs better than GP and ANN. The performance of GP and ANN is analogous. Furthermore, an attempt has been made to enhance the performance of SVM by using SVM hybrid models such as SVM-QPSO and SVM-RBF, and the same has been compared with SVM and ELM. Results indicate that the performance of SVM-QPSO is far better compared to the performance of SVM and SVM-RBF. Moreover, performance of ELM is observed to be the best, but on some occasions, SVM-QPSO performs on par with ELM.
Vorheriger Artikel A framework for crime data analysis using relationship among named entities
Nächster Artikel A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks

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Metadaten
Titel
Groundwater level forecasting using soft computing techniques
verfasst von
N. Natarajan
Ch. Sudheer
Publikationsdatum
16.05.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 12/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04234-5

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