Boosted artificial intelligence model using improved alpha-guided grey wolf optimizer for groundwater level prediction: Comparative study and insight for federated learning technology

• Published in: Journal of hydrology (Amsterdam) 2022-03, Vol.606, p.127384, Article 127384
• Main Authors: Cui, Fang, Al-Sudani, Zainab Abdulelah, Hassan, Geehan Sabah, Afan, Haitham Abdulmohsin, Ahammed, Sumaiya Jarin, Yaseen, Zaher Mundher
• Format: Article
• Language: English
• Subjects: Adaptive Neuro-Fuzzy Inference System; Artificial intelligence; Groundwater level; Improved-Alpha-Guided Grey Wolf Algorithm; Prediction model
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2021.127384

[Display omitted] •New hybrid machine learning model is developed for groundwater level prediction.•31 years (1981–2011) monthly groundwater level data were used for the modeling.•A case study in intensively irrigated region of Northwest Bangladesh was inspected.•The developed ANFIS-IA-GWO model was provided an accurate prediction.•The methodology was evidenced a potential alternative in hydrological engineering. Modeling groundwater level (GWL) is a challenging task particularly in intensive groundwater-based irrigated regions due to its dependency on multiple natural and anthropogenic factors. The main motivation of the current investigation is to develop a new advanced artificial intelligence (AI) model for GWL simulation. An Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by Improved Alpha-Guided Grey Wolf optimization (IA-GWO) algorithm is proposed in this study for reliable prediction of GWL in an intensively irrigated region of Northwest Bangladesh. Natural and anthropogenic factors including rainfall, evapotranspiration, groundwater abstraction, and irrigation return flow were considered as input variables for the development of the models. The efficacy of the proposed model was compared with standalone ANFIS and ANN models and their hybrid versions using particle swarm optimization (ANFIS-PSO) models. Both standard statistical metrics and visual inspection of scatter plots, violin plots, and Taylor diagrams were employed for performance evaluation. Thirty-one years (1981–2011) monthly groundwater level data were used for the calibration and validation of the models. The results revealed the better performance of ANFIS-IA-GWO with normalized root mean square error (NRMSE) of 0.06–0.11 and Kling-Gupta efficiency (KGE) of 0.96–0.98 compared to ANFIS-PSO (NRMSE ∼ 0.38–0.55 and KGE ∼ 0.70–0.86) and ANN-IA-GWO (NRMSE ∼ 0.42–0.57 and KGE ∼ 0.75–0.91) and ANN-PSO (NRMSE ∼ 0.50–0.63 and KGE ∼ 0.63–0.83). The visual comparison of results showed that ANFIS-IA-GWO model was able to replicate the mean, distribution, interquartile range, and standard deviation of observed GWL more appropriately compared to other models