A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting
Abstract
:1. Introduction
2. Methodology of the Research
- To discuss the conventional methodology for GWL.
- To explore the current GWL methodologies.
- Deep-learning-based models for GWL.
- Machine -learning-perspectives-based groundwater modeling.
3. Groundwater and Surface Water Data Sources and Availability
4. Groundwater Level Prediction Techniques
4.1. Physically Based Numerical Method—MODFLOW
4.2. Machine Learning—Artificial Neural Networks (ANN)
4.3. Adaptive Neuro-Fuzzy Inference System (ANFIS)
4.4. Genetic Programming (GP)
4.5. Deep Learning
5. Performance Evaluation
6. Future Research Direction and Discussion
6.1. Wavelet-Bi-LSTM (W-Bi-LSTM)
6.2. Wavelet-Bi-LSTM (W-Bi-LSTM)
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Main Terminology | Sub-Terminology | Prediction Possibilities | |
---|---|---|---|
Weather data | Precipitation | Clear, rain or snow | Hourly, daily, weekly, monthly, and yearly |
Temperature | Minimum or maximum, positive or negative | ||
Solar radiation | |||
Relative humidity | |||
Wind speed | |||
Evaporation | |||
Aquifer layers | Saturated and unsaturated zone | ||
Hydraulic conductivity | |||
Transmissivity | |||
Aquifer storage | |||
Number of layers | |||
Thickness | |||
Land cover | Crop, Urban, rural, or industrial | ||
Stream flow | Variation measurement | ||
Soil | Soil texture | ||
Morphology | Digital elevation model (DEM) |
Data Shape | Data Classification | Data Source | Access Date | Availability |
---|---|---|---|---|
Shapefile | Classification of land use | https://land.copernicus.eu/pan-european/corine-land-cov | (10 February 2023) | Europe |
Shapefile | Property of soil classification | https://www.isric.org/explore/wosis | (10 February 2023) | Worldwide |
Shapefile or Vectorial | Rock permeability and porosity | https://borealisdata.ca/dataset.xhtml?persistentId=doi%3A10.5683/SP2/TTJNIU | (10 February 2023) | Worldwide |
Raster | Climatic data | https://apps.ecmwf.int/datasets/ | (10 February 2023) | Worldwide |
Raster | MODIS products | https://appeears.earthdatacloud.nasa.gov/ | (10 February 2023) | Worldwide |
Raster | Digital surface model (DSM) | https://asterweb.jpl.nasa.gov/gdem.asp | (10 February 2023) | Worldwide |
Database | Climatic data | https://swat.tamu.edu/data/cfsr | (10 February 2023) | Worldwide |
Database | Climate projections | https://esgf-data.dkrz.de/search/esgf-dkrz/ | (10 February 2023) | Worldwide |
Database | River network spatial data | https://water.nier.go.kr/web/gisKrf?pMENU_NO=89 | (10 February 2023) | Korea |
Database | National Water Resources Management Comprehensive Information System | http://www.wamis.go.kr/ | (10 February 2023) | Worldwide |
Database | Geographic information | Water Environment Geographic Information | (10 February 2023) | Worldwide |
References | Deep Learning | GP | MODFLOW | ANFIS | ANN |
---|---|---|---|---|---|
P. Shukla and R. M. Singh [49] | ✓ | ||||
Lallahem et al. [4] | ✓ | ||||
Krishna et al. [56] | ✓ | ||||
Sreekanth et al. [5] | ✓ | ✓ | |||
Kouziokas et al. [57] | ✓ | ||||
Zhang et al. [62] | ✓ | ||||
Gong et al. [55] | ✓ | ✓ | |||
Khaki et al. [64] | ✓ | ✓ | |||
Emamgholizadeh et al. [65] | ✓ | ✓ | |||
Kasiviswanathan et al. [69] | ✓ | ||||
Shiri et al. [71] | ✓ | ||||
Zhang et al. [6] | ✓ | ✓ | |||
Ren et al. [72] | ✓ | ||||
bowes et al. [73] | ✓ | ||||
Shin et al. [74] | ✓ | ||||
Javadinejad et al. [75] | ✓ | ||||
Moravej et al. [76] | ✓ | ||||
Khedri et al. [77] | ✓ | ||||
Seifi et al. [78] | ✓ | ||||
Demirci et al. [79] | ✓ | ✓ | |||
Djurovic et al. [80] | ✓ | ||||
Jalalkamali et al. [81] | ✓ | ||||
Zeydalinejad et al. [2] | ✓ | ||||
Mukherjee et al. [32] | ✓ | ||||
Moosavi et al. [68] | ✓ | ||||
Sun et al. [82] | ✓ | ||||
Ghose et al. [83] | ✓ | ||||
Shan et al. [84] | ✓ | ✓ | |||
Ghasemlounia et al. [85] | ✓ | ||||
Yin et al. [86] | ✓ |
Reference | Performance Evaluation Metrices | Prediction | Target Prediction | Year |
---|---|---|---|---|
Ren et al. [72] | MAPE, RMSE, NSE, KGE | Weekly | GWL | 2022 |
Malik and Bhagwat [87] | , RMSE | Annually | GWL fluctuations | 2021 |
Bahmani and Ouarda [88] | RMSE, BIAS , rBIAS | Monthly | GWL | 2021 |
Kombo et al. [89] | MAE,, NSE, RMSE | Daily | GWL | 2020 |
Iqbal et al. [90] | , MAE, MSE | Daily | GWL | 2020 |
Seifi et al. [78] | RMSE, NSE, MAE, PBIAS | Monthly | GWL | 2020 |
Kenda et al. [91] | Daily | GWL/SWL | 2020 | |
Cao et al. [92] | RMSE, R, MAPE | Daily | GWL | 2020 |
Di and Granata [93] | , RAE, MAE, RMSE, RAE | Daily | GWL | 2020 |
Yadav et al. [94] | NMSE, , RMSE, | Monthly | GWL | 2020 |
Sharafati et al. [95] | , NRMSE | Monthly | GWL | 2020 |
Shin et al. [74] | NSE, RMSE | Daily | GWL | 2020 |
Khedri et al. [77] | NSE, MAE, RMSE, R | Monthly | GWL | 2020 |
Bozorg-Haddad et al. [96] | RMSE, | Monthly | GWL | 2020 |
Chen et al. [97] | RMSE, | Monthly | GWL | 2020 |
Evans et al. [98] | MAE | 3-month interval | Depth to GW | 2020 |
Hasda et al. [99] | MSE, | Weekly | GWL | 2020 |
Mohanasundaram et al. [100] | , RMSE | Monthly | GWL | 2019 |
Malekzadeh et al. [101] | Bias, R, VAF, RMSE, SI, MAE, NSE, RMSRE, MAPE | Monthly | GWL | 2019 |
Moghaddam et al. [102] | , RMSE, NSE | Monthly | GWL | 2019 |
Zhang et al. [6] | , RMSE, NSE | Half-hourly | GWL | 2019 |
Gemitzi and Stefanopoulos [103] | MaxAE, MAE | Monthly | GWL | 2011 |
Jalalkamali et al. [81] | , MAPE, RMSE | Monthly | GWL | 2011 |
Ghose et al. [83] | MSE | Daily | GWL | 2010 |
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Khan, J.; Lee, E.; Balobaid, A.S.; Kim, K. A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting. Appl. Sci. 2023, 13, 2743. https://doi.org/10.3390/app13042743
Khan J, Lee E, Balobaid AS, Kim K. A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting. Applied Sciences. 2023; 13(4):2743. https://doi.org/10.3390/app13042743
Chicago/Turabian StyleKhan, Junaid, Eunkyu Lee, Awatef Salem Balobaid, and Kyungsup Kim. 2023. "A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting" Applied Sciences 13, no. 4: 2743. https://doi.org/10.3390/app13042743