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Prediction of monthly groundwater level using a new hybrid intelligent approach in the Tabriz plain, Iran

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Abstract

Predicting the groundwater level (GWL) is essential in water resource management and irrigation planning in arid and semi-arid areas. In this study, an artificial neural network (ANN) was combined with newly developed wild horse optimizer (WHO) and egret swarm optimization algorithm (ESOA) techniques to predict a one month lead-time GWL in the Tabriz plain of Iran. For the prediction of the GWL, the number of months and years, the one month lag of average temperature, evaporation, precipitation, and GWL were used as inputs. Model performances were compared using root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), and relative strength ratio (RSR) statistical indicators and scatter diagrams, time series graph, violin graph, and Taylor diagram. As a result of the analysis, the most successful estimation results were obtained with the input combinations of year, month, average temperature, evaporation, precipitation, and GWL (t − 1) for the prediction of the one month lead-time GWL. According to the results of evaluation indicators in the testing phase, ANN with (R2 = 0.871, RMSE = 0.306 (m), NSE = 0.832, and RSR = 0.410), WHO–ANN (R2 = 0.932, RMSE = 0.200 (m), NSE = 0.929, and RSR = 0.267), and ESOA–ANN (R2 = 0.952, RMSE = 0.164 (m), NSE = 0.951, and RSR = 0.220). In addition, it was revealed that the ESOA–ANN hybrid model showed higher prediction success than the WHO–ANN and standalone ANN models. The study outputs contribute to decision-makers and planners for controlling land subsidence, assessing GWL and aquifer compaction, irrigation planning, and effective management of water resources.

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All data generated or analyzed during this study are included in this published article.

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Abbreviations

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural network

CEEMD:

Complementary ensemble empirical mode decomposition

EEMD:

Ensemble empirical mode decomposition

ESOA:

Egret swarm optimization algorithm

GEP:

Gene expression programming

GWL:

Groundwater level

GWO:

Gray wolf optimization

KNN-RF:

K-Nearest neighbor-random forest

LSTM:

Long short-term memory

MIC:

Maximum information coefficient

NSE:

Nash–Sutcliffe efficiency

PSO:

Particle swarm optimization

QPSO:

Quantum-particle swarm optimization

R 2 :

Coefficient of determination

RMSE:

Root mean square error

RSR:

Relative strength ratio

SAELM:

Self-adaptive extreme learning machine

SVM:

Support vector machine

SVR:

Support vector regression

WHO:

Wild horse optimizer

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The authors declare that no funds or grants were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

    Ehsan Mirzania

  2. Faculty of Nature and Life Sciences, Laboratory of Water and Environment, Hassiba Benbouali University of Chlef, Chlef, Algeria

    Mohammed Achite

  3. Construction and Project Management Research Institute, Housing and Building National Research Center, Giza, Egypt

    Nehal Elshaboury

  4. Department of Civil Engineering, Erzincan Binali Yıldırım University, Erzincan, Turkey

    Okan Mert Katipoğlu

  5. Department of Irrigation and Reclamation Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Tehran, Iran

    Mohsen Saroughi

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  1. Ehsan Mirzania
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All authors contributed to the study’s conception and design. All authors read and approved the final manuscript.

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Correspondence to Nehal Elshaboury or Mohsen Saroughi.

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Mirzania, E., Achite, M., Elshaboury, N. et al. Prediction of monthly groundwater level using a new hybrid intelligent approach in the Tabriz plain, Iran. Neural Comput & Applic 36, 12609–12624 (2024). https://doi.org/10.1007/s00521-024-09681-3

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