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| Titel |
Groundwater level forecasting using an artificial neural network trained with particle swarm optimization. |
| VerfasserIn |
E. Tapoglou, I. C. Trichakis, Z. Dokou, G. P. Karatzas |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250060410
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| Zusammenfassung |
| The purpose of this study is to examine the use of particle swarm optimization algorithm in
order to train a feed-forward multi-layer artificial neural network, which can simulate
hydraulic head change at an observation well.
Particle swarm optimization is a relatively new evolutionary algorithm, developed by
Eberhart and Kennedy (1995), which is used to find optimal solutions to numerical and
quantitative problems. Three different variations of particle swarm optimization
algorithm are considered, the classic algorithm with the improvement of inertia weight,
PSO-TVAC and GLBest-PSO. The best performance among all the algorithms was
achieved by GLBest-PSO, where the distance between the overall best solution found
and the best solution of each particle plays a major role in updating each particle’s
velocity.
The algorithm is implemented using field data from the region of Agia, Chania, Greece.
The particle swarm optimization algorithm shows an improvement of 9.3% and 18% in
training and test errors respectively, compared to the errors of the back propagation
algorithm. The trained neural network can predict the hydraulic head change at a well,
without being able to predict extreme and transitional phenomena. The maximum divergence
from the observed values is 0.35m.
When the hydraulic head change is converted into hydraulic head, using the observed
hydraulic head of the previous day, the deviations of simulated values from the
actual hydraulic head appear comparatively smaller, with an average deviation of
0.041m.
The trained neural network was also used for midterm prediction. In this case, the
hydraulic head of the first day of the simulation is used together with the hydraulic head
change derived from the simulation. The values obtained by this process were smaller than
the observed, while the maximum difference is approximately 1m. However, this error, is not
accumulated during the two hydrological years of simulation, and the error at the end of the
simulation period is minimal.
Finally, climate change scenarios were examined, based on the prediction that on the
island of Crete during the period of 2010-2040, it will be a 12(±25)% average reduction in
precipitation and a 1.9(±0.9)oC increase in mean temperature (Tsanis et al., 2011). In order
to study these scenarios, data time series were created for the period 2010-2020, using a
stochastic weather generator for three cases (best, worst and average case scenarios). The
prediction results indicate a significant negative effect on the groundwater level only for the
worst case scenario (37% reduction in precipitation), while in the other cases the results vary
from neutral to positive.
References:
Eberhart, R., & Kennedy, J. (1995). A New Optimizer Using Particle Swarm
Theory. Sixth International Symposium on Micro Machine and Human Science,
IEEE.
Tsanis, I., Koutroulis, A., Daliakopoulos, I., & Jacob, D. (2011). Severe climate-induced
water shortage and extremes in Crete - A letter. Climatic Change, 106, 667-677. |
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