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Neurocomputing intelligence models for lakes water level forecasting: a comprehensive review

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Abstract

Hydrological processes forecasting is an essential step for better water management and sustainability. Among several hydrological processes, lake water level (LWL) forecasting is one of the significant processes within a particular catchment. The complexity of the LWL fluctuation is owing to the diversity of the influential parameters including climate, hydrology and some other morphology. In this study, several versions of neurocomputing intelligence models are developed for LWL fluctuation forecasting at five great lakes Lake Superior, Lake Michigan, Lake Huron, Lake Erie, and Lake Ontario, located at the north of USA. The applied models are including M5-Tree, multivariate adaptive regression spline (MARS) and least square support vector regression (LSSVR). The models are developed using several input combinations that are configured based on the correlated lags in addition to the periodicity of time series. The sequential influence of the lakes order is considered in the modeling development. Also, cross-station modeling where lag time series of upstream lakes are used to forecast downstream LWL. Results are assessed using several statistical metrics and graphical visualization. Overall, the results indicated that the applied forecasting models efficient and trustworthy. The component of the periodicity time series enhances the forecasting performance. Cross-station modeling revealed an optimistic modeling strategy for learning transfer modeling of using information of nearby site.

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Acknowledgements

The authors would like to thank the support received from KTO Karatay University.

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The research received no funds.

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Authors and Affiliations

  1. Faculty of Engineering and Natural Sciences, KTO Karatay University, Konya, 42020, Turkey

    Vahdettin Demir

  2. Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia

    Zaher Mundher Yaseen

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  1. Vahdettin Demir
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  2. Zaher Mundher Yaseen
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Contributions

Vahdettin Demir: Conceptualization; validation; investigation; data curation; methodology; project leader; editing; formal analysis; visualization; writing. Zaher Mundher Yaseen: Supervision; validation; investigation; editing, conceptualization; writing.

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Correspondence to Zaher Mundher Yaseen.

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Appendices

Appendix 1

See Table 9.

Table 9 The statistical parameters of the selected lakes for the current research
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Appendix 2

See Table 10.

Table 10 Pearson correlation coefficients
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Appendix 3

See Table 11.

Table 11 The monthly statistical parameters of lake stations
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Appendix 4

See Table 12.

Table 12 Regularization constant and width of RBF kernel parameters of the optimal LSSVR models for Superior, Michigan, St. Clair, Erie and Ontario Lake stations
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Appendix 5

See Table 13.

Table 13 Regularization constant and width of RBF kernel parameters of the optimal P-LSSVR models for Superior, Michigan, St. Clair, Erie and Ontario Lake stations
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.

Appendix 6a

See Fig. 8.

Fig. 8
figure 8

Scatter plots of the observed and predicted lake level values during testing phase, produced by MARS, M5-Tree, LSSVR and P-LSSVR models for the Lake Superior

Full size image

Appendix 6b

See Fig. 9.

Fig. 9
figure 9

Scatter plots of the observed and predicted lake level values during testing phase, produced by MARS, M5-Tree, LSSVR and P-LSSVR models for the Lake Michigan-Huron

Full size image

Appendix 6c

See Fig. 10.

Fig. 10
figure 10

Scatter plots of the observed and predicted lake level values during testing phase, produced by MARS, M5-Tree, LSSVR and P-LSSVR models for the Lake St. Clair

Full size image

Appendix 6d

See Fig. 11.

Fig. 11
figure 11

Scatter plots of the observed and predicted lake level values during testing phase, produced by MARS, M5-Tree, LSSVR and P-LSSVR models for the Lake Erie

Full size image

Appendix 6e

See Fig. 12.

Fig. 12
figure 12

Scatter plots of the observed and predicted lake level values during testing phase, produced by MARS, M5-Tree, LSSVR and P-LSSVR models for the Lake Ontario

Full size image

Appendix 7a

See Fig. 13.

Fig. 13
figure 13

Violin plots of the observed and predicted lake level values during testing phase, produced by MARS, M5-Tree, LSSVR and P-LSSVR models for the Lake Superior

Full size image

Appendix 7b

See Fig. 14.

Fig. 14
figure 14

Violin plots of the observed and predicted lake level values during testing phase, produced by MARS, M5-Tree, LSSVR and P-LSSVR models for the Lake Michigan-Huron

Full size image

Appendix 7c

See Fig. 15.

Fig. 15
figure 15

Violin plots of the observed and predicted lake level values during testing phase, produced by MARS, M5-Tree, LSSVR and P-LSSVR models for the Lake St. Clair

Full size image

Appendix 7d

See Fig. 16.

Fig. 16
figure 16

Violin plots of the observed and predicted lake level values during testing phase, produced by MARS, M5-Tree, LSSVR and P-LSSVR models for the Lake Erie

Full size image

Appendix 7e

See Fig. 17.

Fig. 17
figure 17

Violin plots of the observed and predicted lake level values during testing phase, produced by MARS, M5-Tree, LSSVR and P-LSSVR models for the Lake Ontario

Full size image

Appendix 8

See Table 14.

Table 14 The optimal parameters of the LSSVR models in cross application
Full size table

Appendix 9

See Table 15.

Table 15 The optimal parameters of the P-LSSVR models in cross application
Full size table

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Demir, V., Yaseen, Z.M. Neurocomputing intelligence models for lakes water level forecasting: a comprehensive review. Neural Comput & Applic 35, 303–343 (2023). https://doi.org/10.1007/s00521-022-07699-z

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