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Water Resources Management

Erschienen in:

01.01.2013

Application of Several Data-Driven Techniques for Predicting Groundwater Level

verfasst von: Bagher Shirmohammadi, Mehdi Vafakhah, Vahid Moosavi, Alireza Moghaddamnia

Erschienen in: Water Resources Management | Ausgabe 2/2013

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Abstract

In this study, several data-driven techniques including system identification, time series, and adaptive neuro-fuzzy inference system (ANFIS) models were applied to predict groundwater level for different forecasting period. The results showed that ANFIS models out-perform both time series and system identification models. ANFIS model in which preprocessed data using fuzzy interface system is used as input for artificial neural network (ANN) can cope with non-linear nature of time series so it can perform better than others. It was also demonstrated that all above mentioned approaches could model groundwater level for 1 and 2 months ahead appropriately but for 3 months ahead the performance of the models was not satisfactory.

Vorheriger Artikel Analytical Support for Integrated Water Resources Management: A New Method for Addressing Spatial and Temporal Variability

Nächster Artikel A Decision Support Methodology for Integrated Urban Water Management in Remote Settlements

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Titel: Application of Several Data-Driven Techniques for Predicting Groundwater Level
verfasst von: Bagher Shirmohammadi
Mehdi Vafakhah
Vahid Moosavi
Alireza Moghaddamnia
Publikationsdatum: 01.01.2013
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 2/2013
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-012-0194-y

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