Uneingeschränkter Zugang

Estimation of zooplankton density with artificial neural networks (a new statistical approach) method, Elazığ-Türkiye

Hilal Bulut
Hilal Bulut
   | 31. Dez. 2023
Oceanological and Hydrobiological Studies's Cover Image
Über diesen Artikel
Vorheriger Artikel
Nächster Artikel
Zitieren
Article Category: Original Research Paper
Online veröffentlicht: 31. Dez. 2023
Seitenbereich: 502 - 515
Eingereicht: 08. Okt. 2023
Akzeptiert: 27. Nov. 2023
DOI: https://doi.org/10.26881/oahs-2023.4.11
© 2023 Hilal Bulut, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

Coordinates of Cip Reservoir
Coordinates of Cip Reservoir

Figure 2

Model Structure of ANNs
Model Structure of ANNs

Figure 3

Some water quality parameters of study field
Some water quality parameters of study field

Figure 4

Monthly changes of total zooplankton in Cip Reservoir at the 1st, 2nd, and 3rd station
Monthly changes of total zooplankton in Cip Reservoir at the 1st, 2nd, and 3rd station

Figure 5

Training, validation, testing and all data results of artificial neural networks for the 1st station
Training, validation, testing and all data results of artificial neural networks for the 1st station

Figure 6

Training, validation, testing and all data results of artificial neural networks for the 2nd station
Training, validation, testing and all data results of artificial neural networks for the 2nd station

Figure 7

Training, validation, testing and all data results of artificial neural networks for the 3rd station
Training, validation, testing and all data results of artificial neural networks for the 3rd station

Figure 8

Artificial neural networks training state at the 1st station
Artificial neural networks training state at the 1st station

Figure 9

Artificial neural networks training state at the 2nd station
Artificial neural networks training state at the 2nd station

Figure 10

Artificial neural networks training state at the 3rd station
Artificial neural networks training state at the 3rd station

Comparison with artificial neural networks of real values of zooplankton density for the 2nd station

Month rotifer cladocera copepeda
Real Data ANNs MAPE (%) Real Data ANNs MAPE (%) Real Data ANNs MAPE (%)
September 33113 32992 0.365 509 506 0.589 0 0.000 0.000
October 27510 27436 0.269 4585 4526 1.287 3057 3057 0.000
November 15285 15550 1.733 1019 1019 0.000 3057 3057 0.000
December 9681 9622 0.609 0 23.012 0.000 410 410 0.000
January 3260 3298 1.166 0 17.785 0.000 509 509 0.000
February 5605 5596 0.161 0 62.318 0.000 9171 9196 0.273
March 5604 5521 1.481 509 507 0.393 1224 1224 0.000
April 3057 3056 0.033 3057 3057 0.000 0 0.001 0.000
May 8151 8364 2.613 7132 7047 1.192 0 0.000 0.000
June 14776 14773 0.020 12942 12917 0.193 205 209 1.951
July 4076 4176 2.453 12738 12627 0.871 0 23.51 0.000
August 1019 1015 0.393 0 12588 0.000 509 509 0.000
Average MAPE (%) 0.941 0.377 0.185

Comparison with artificial neural networks of real values of zooplankton density for the 3rd station

Month rotifer cladocera copepeda
Real Data ANNs MAPE (%) Real Data ANNs MAPE (%) Real Data ANNs MAPE (%)
September 28532 28580 0.168 203 208 2.463 0 0.043 0.000
October 31080 31081 0.003 3567 3596 0.813 1019 1018 0.098
November 15489 15536 0.303 509 509 0.000 4076 4025 1.251
December 13247 13218 0.219 203 206 1.478 509 508 0.196
January 3770 3774 0.106 0 0.958 0.000 509 509 0.000
February 16814 16839 0.149 509 501 1.572 203 204 0.493
March 27543 27169 1.358 509 508 0.196 509 509 0.000
April 23438 23439 0.004 1019 1018 0.098 203 201 0.985
May 36175 36132 0.119 8662 8661 0.011 1019 1018 0.098
June 37705 37665 0.106 7134 7133 0.014 203 203 0.000
July 12738 12608 1.020 11210 11209 0.009 0 0.228 0.000
August 9681 9631 0.516 3567 3566 0.028 203 202 0.493
Average MAPE (%) 0.342 0.557 0.301

Comparison with artificial neural networks of real values of zooplankton density for the 1st station

Month rotifer cladocera copepeda
Real Data ANNs MAPE (%) Real Data ANNs MAPE (%) Real Data ANNs MAPE (%)
September 68273 68272 0.001 509 509 0.000 0 0.002 0.000
October 50950 49886 2.088 4585 4585 0.000 2038 2038 0.000
November 35667 34730 2.627 1019 1019 0.000 5605 5604 0.018
December 10190 10188 0.019 0 2.262 0.000 0 0.002 0.000
January 3973 3973 0.000 0 0.061 0.000 509 508 0.196
February 17323 17316 0.040 0 0.037 0.000 1528 1528 0.000
March 30061 30056 0.016 0 0.688 0.000 0 1.837 0.000
April 27515 28315 2.907 1019 1018 0.098 0 0.000 0.000
May 43817 43817 0.000 9681 9680 0.010 203 206 1.478
June 58533 58512 0.036 17833 17832 0.006 0 0.000 0.000
July 17324 18359 5.974 23948 23947 0.004 0 1.939 0.000
August 8662 8663 0.012 2548 2581 1.295 0 0.182 0.000
Average MAPE (%) 1.143 0.118 0.141
eISSN:
1897-3191
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
4 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Chemie, andere, Geowissenschaften, Biologie
Zeitschrift RSS Feed