Prediction of fluctuations in the equivalent thickness of groundwater using satellite information and artificial intelligence hybrid models

Document Type : Research Paper

Authors

1 Department of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran, Iran

2 Department of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran

3 Department of Chemical Engineering, Arak Branch, Islamic Azad University, Arak, Iran

4 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

Abstract

 
The aim of this research is to predict fluctuations in the equivalent thickness of groundwater using GRACE satellite data and modeling it using artificial intelligence hybrid models. The study area of this research is the basin area of Lake Urmia located in the northwest of Iran. For this purpose, 180 GRACE satellite data between April 2002 and March 2017 were used. The output of GRACE satellites includes 6 pixels located on the selected watershed, of which 2 points that overlapped the most with the watershed area were selected for modeling with artificial intelligence tools. The GA-ANN, ICA-ANN and PSO-ANN hybrid models were used for this purpose. The results showed that the output of the ICA-ANN model had the best fit with the observation data with a correlation coefficient equal to 0.915 and 0.942 in the two selected pixels 2 and 5 in the test phase, and the results of this model had the best and closest distribution of points. Considering the importance of knowing the changes in the equivalent thickness of groundwater as one of the most important parameters of the water budget, the artificial intelligence models used in this research can be recommended, especially for areas without basic statistics or in situations where it is not possible to use mathematical models. Without the need for complex relationships and equations to investigate the effect of surface and groundwater interaction and only based on satellite data, the equivalent thickness of groundwater can be predicted in the studied plain in dry and wet periods with great accuracy.

Keywords

Main Subjects


Prediction of fluctuations in the equivalent thickness of groundwater using satellite information and artificial intelligence hybrid models

EXTENDED ABSTRACT

 

Introduction

Fluctuations in the equivalent thickness of groundwater are one of the main components of the hydrogeological cycle and one of the required variables for many water resources exploitation models. The lack of reliable and comprehensive data is one of the most important challenges in analyzing the decline and predictions of the equivalent thickness of groundwater in water management. In recent years, the use of different satellite information has been noticed as a reliable solution. The aim of this research is to predict fluctuations in the equivalent thickness of groundwater using GRACE satellite data and modeling it using artificial intelligence hybrid models.

Methods and Materials

The study area of this research is the basin area of Lake Urmia located in the northwest of Iran. For this purpose, 180 GRACE satellite data between April 2002 and March 2017 were used. GRACE satellites point information is taken as 1º x 1º, which leads to a 360 x 180 matrix for the whole earth. The output of GRACE satellites includes 6 pixels located on the selected watershed, of which 2 points that overlapped the most with the watershed area were selected for modeling with artificial intelligence tools. One of the effective methods in this field is combining the MLP model with the optimization algorithm in the form of a hybrid model. The GA-ANN, ICA-ANN and PSO-ANN hybrid models were used for this purpose. In the structure of these models, optimal weights are obtained by optimization algorithms. The objective function in these models is to minimize the RMSE value. The generation and modification of weights in the model structure continued until the minimum error was reached, and the number of iterations of the algorithm was adjusted accordingly.

Results and Discussion

The performance evaluation of the GA-ANN, ICA-ANN and PSO-ANN hybrid artificial intelligence models showed that these models are very accurate in predicting fluctuations in the equivalent thickness of groundwater. The results showed that the output of the ICA-ANN model had the best fit with the observational data with a correlation coefficient equal to 0.915 and 0.942 in the two selected pixels 2 and 5 in the test phase, and the results of this model had the best and closest distribution of points. It was 45 degrees around the line and it is considered the most accurate model. Also, the ICA-ANN model had the lowest RMSE value so that the value of RMSE in this method in the two stages of train and test in the Urmia lake basin for study point 2 was 7.3 and 5.73 respectively and for study point 5 was 7.5 and 5.75 respectively. Considering the importance of knowing the changes in the equivalent thickness of groundwater as one of the most important parameters of the water budget, the artificial intelligence models used in this research can be recommended, especially for areas without basic statistics or in situations where it is not possible to use mathematical models. did In this case, without the need for complex relationships and equations to investigate the effect of surface and groundwater interaction and only based on satellite data, the equivalent thickness of groundwater can be predicted in the studied plain in dry and wet periods with great accuracy.

Conclusion

The possibility of predicting the equivalent thickness of groundwater for a long-term period based on a very small amount of information compared to complex models and using only satellite data is one of the most important achievements of this research. In this case, without the need for extensive information and without the need for complex maps and software, and without spending a lot of time and money for the calibration and validation of mathematical models, the equivalent thickness of groundwater based on artificial intelligence methods and using GRACE satellite data is forecasted. This is of great help to experts in the water resources sector in basins that lack statistics or aquifers that lack basic information and accurate maps, or plains that are faced with widespread statistical deficiencies. Because by using artificial intelligence models, very valuable management information regarding the prediction of the equivalent thickness of groundwater in dry and wet years can be obtained with very little time and cost.

REFERENCES

Amiri, S., Rajabi, A., Shabanlou, S., Yosefvand, F., & Izadbakhsh, M.A. (2023). Prediction of groundwater level variations using deep learning methods and GMS numerical model. Earth Science Informatic. https://doi.org/10.1007/s12145-023-01052-1.
Andersen, O.B., Seneviratne, S.I., Hinderer, J., & Viterbo, P. (2005), GRACE-derived terrestrial water storage depletion associated with the 2003 European heat wave, Geophys. Res. Lett., 32, L18405, doi:10.1029/2005GL023574.
Azari, A., Zeynoddin, M., Ebtehaj, I., Sattar, A.M.A., Gharabaghi, B., & Bonakdari, H. (2021). Integrated preprocessing techniques with linear stochastic approaches in groundwater level forecasting, Acta Geophysica, 69, 1395-1411.
Azizi, E., Yosefvand, F., Yaghoubi, B., Izadbakhsh, M.A. & Shabanlou, S. (2023) Modelling and prediction of groundwater level using wavelet transform and machine learning methods: A case study for the Sahneh Plain, Iran. Irrigation and Drainage, 72(3), 747–762. https://doi.org/10.1002/ird.2794
Azizpour, A., Izadbakhsh, M.A., Shabanlou, S. Yosefvand, F., & Rajabi, A. (2021). Estimation of water level fluctuations in groundwater through a hybrid learning machine. Groundwater for Sustainable Development, 15, 100687. https://doi.org/10.1016/j.gsd.2021.100687.
Azizpour, A., Izadbakhsh, M.A., Shabanlou, S. Yosefvand, F., & Rajabi, A. (2022). Simulation of time-series groundwater parameters using a hybrid metaheuristic neuro-fuzzy model. Environment Science and Pollution Research. https://doi.org/10.1007/s11356-021-17879-4
Chen, J., Li, J., Zhang, Z., & Ni, S.I. (2014). Long-term groundwater variations in Northwest India from satellite gravity measurements. Global and Planetary Change, 116, 130-138.
Chen, J. L., Wilson, C. R., Famiglietti, J. S., and Rodell, M. (2005), Spatial sensitivity of the Gravity Recovery and Climate Experiment (GRACE) time-variable gravity observations, J. Geophys. Res., 110, B08408, doi:10.1029/2004JB003536.
Chen, J. L., and Wilson, C. R. (2008), Low degree gravity changes from GRACE, Earth rotation, geophysical models, and satellite laser ranging, J. Geophys. Res., 113, B06402, doi:10.1029/2007JB005397.
Dapeng, M., Zhongchang, S., & Jinyun. G. (2014). Estimating continental water storage variations in Central Asia area using GRACE data. IOP Conference Series: Earth and Environmental Science, Volume 17, 35th International Symposium on Remote Sensing of Environment (ISRSE35) 22–26 April 2013, Beijing, China.
Ebtehaj, I., Bonakdari, H., & Shamshirband, S. (2016). Extreme learning machine assessment for estimating sediment transport in open channels, Engineering Computations, 32, 691-704.
Ebtehaj, I., Bonakdari, H., Zeynoddin, M., Gharabaghi, B., & Azari, A. (2020). Evaluation of preprocessing techniques for improving the accuracy of stochastic rainfall forecast models, International Journal of Environmental Science and Technology, 17, 505-524.
Esmaeili, F., Shabanlou, S., & Saadat, M.A. (2021). Wavelet-outlier robust extreme learning machine for rainfall forecasting in ardabil city, Iran, Earth Science Informatics, 14, 2087-2100.
Fallahi, M.M., Shabanlou, S., Rajabi, A. Yosefvand, F., & Izadbakhsh, M.A. (2023). Effects of climate change on groundwater level variations affected by uncertainty (case study: Razan aquifer). Applied Water Science, 13, 143. https://doi.org/10.1007/s13201-023-01949-8
Fatolazadeh, F., Voosoghi, B., & Naeeni, M.R. (2016). Wavelet and Gaussian approaches for estimation of groundwater variations using GRACE data. Groundwater, 54(1), 74-81.
Forootan, E., Rietbroek, R., Kusche, J., Sharifi, M.A., Awange, J.L., Schmidt, M., Omondi, P., & Famiglietti, J. (2014). Separation of large-scale water storage patterns over Iran using GRACE, altimetry and hydrological data. Remote Sensing of Environment, 140, 580-595.
Guzman, S.M., Paz, J.O., Tagert, M.L.M. & Mercer, A.E. (2019). Evaluation of Seasonally Classified Inputs for the Prediction of Daily Groundwater Levels: NARX Networks Vs Support Vector Machines. Environmental Modeling & Assessment, 24(2), 223-234.
Henry, C.M., D.M. Allen., & Huang, J. (2011). Groundwater storage variability and annual recharge using well-hydrograph and GRACE satellite data. Hydrogeology Journal, 19(4), 741-755.
Hofmann-Wellenhof, B., & Moritz, H. (2006). Physical geodesy. Springer Science & Business Media.
Joodaki, G., Earth mass change tracking using GRACE satellite gravity data. 2014.
Jin, S., Hassan, A., & Feng, G. (2012). Assessment of terrestrial water contributions to polar motion from GRACE and hydrological models. Journal of Geodynamics, 62, 40-48.
Joodaki, G., Wahr, J., & Swenson, S. (2014). Estimating the human contribution to groundwater depletion in the Middle East, from GRACE data, land surface models, and well observations. Water Resources Research, 50(3), 2679-2692.
Malekzadeh, M., Kardar, S., Saeb, K., Shabanlou, S., & Taghavi, L. (2019a). A novel approach for prediction of monthly ground water level using a hybrid wavelet and non-tuned self-adaptive machine learning model. Water resources management, 33, 1609-1628.        
Malekzadeh, M., Kardar, S., & Shabanlou, S. (2019b). Simulation of groundwater level using MODFLOW, extreme learning machine and Wavelet-Extreme Learning Machine models, Groundwater for Sustainable Development, 9, 100279, https://doi.org/10.1016/j.gsd.2019.100279.
Mazraeh, A., Bagherifar, M., Shabanlou, S., & Ekhlasmand, R. (2023). A Hybrid Machine Learning Model for Modeling Nitrate Concentration in Water Sources. Water, Air, & Soil Pollution, 234(11), 1-22.
Mazraeh, A., Bagherifar, M., Shabanlou, S., & Ekhlasmand, R. (2024). A novel committee-based framework for modeling groundwater level fluctuations: A combination of mathematical and machine learning models using the weighted multi-model ensemble mean algorithm, Groundwater for Sustainable Development, 24, 101062, https://doi.org/10.1016/j.gsd.2023.101062.
Mohammed, K.S., Shabanlou, S., Rajabi, A., Yosefvand, F., & Izadbakhsh, M.A. (2023). Prediction of groundwater level fluctuations using artificial intelligence-based models and GMS. Applied Water Science, 13, 54. https://doi.org/10.1007/s13201-022-01861-7
Mo S, Zhong Y, Forootan E, Mehrnegar N, Yin X, Wu J, ... and Shi (2022) Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap. J. Hydrol. 127244.‏ https://doi.org/10.1016/j.jhydrol.2021.127244
Moghim, S. (2020). Assessment of water storage changes using GRACE and GLDAS. Water Resources Management, 34(2), 685-697.
Nadiri, A.A., Naderi, K., Khatibi, R., & Gharekhani, M. (2019). Modelling groundwater level variations by learning from multiple models using fuzzy logic. Hydrological sciences journal, 64(2), 210-226.
Nourani, V., Mogaddam, A.A. & Nadiri, A.O. (2008). An ANN‐based model for spatiotemporal groundwater level forecasting. Hydrological Processes: An International Journal, 22(26), 5054-5066.
Pereira, A., & Pacino, M.C. (2012). Annual and seasonal water storage changes detected from GRACE data in the La Plata Basin. Physics of the Earth and Planetary Interiors, 212, 88-99.
Poursaeid, M., Mastouri, R., Shabanlou, S., & Najarchi, M. (2020). Estimation of total dissolved solids, electrical conductivity, salinity and groundwater levels using novel learning machines. Environ Earth Sci. 79, 453. https://doi.org/10.1007/s12665-020-09190-1
Poursaeid, M., Mastouri, R., Shabanlou, S., & Najarchi, M. (2021). Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks. Water and Environment, 35, 67-83.
Poursaeid, M., Poursaeid, A.H. & Shabanlou, S. (2022). Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction. Water Resour Manage. https://doi.org/10.1007/s11269-022-03070-y
Rajabi, A., & Shabanlou, S. (2012) Climate index changes in future by using SDSM in Kermanshah, Iran. J Environ Res Dev 7(1), 37–44
Rodell, M. (2000). Estimating changes in terrestrial water storage. The University of Texas at Austin. https://repositories.lib.utexas.edu/handle/2152/68627
Rodell, M., Chen, J., Kato, H., Famiglietti, J.S., Nigro, J., & Wilson, C.R. (2007). Estimating groundwater storage changes in the Mississippi River basin (USA) using GRACE. Hydrogeol J 15, 159–166.
Rodell, M., Velicogna, I., & Famiglietti, J.S. (2009). Satellite-based estimates of groundwater depletion in India. Nature, 460(7258), 999-1002.
Scanlon, B. R., Longuevergne, L., & Long, D. (2012), Ground referencing GRACE satellite estimates of groundwater storage changes in the California Central Valley, USA, Water Resour. Res., 48, W04520, doi:10.1029/2011WR011312.
Scanlon BR, Rateb A, Anyamba A, Kebede S, MacDonald AM, Shamsudduha M, ... and Xie H (2022) Linkages between GRACE water storage, hydrologic extremes, and climate teleconnections in major African aquifers. Environ. Res. Lett. 014046.‏ https://doi.org/10.18738/T8/HLXCMY
Shamsudduha, M., Taylor, R. G., & Longuevergne, L. (2012), Monitoring groundwater storage changes in the highly seasonal humid tropics: Validation of GRACE measurements in the Bengal Basin, Water Resour. Res., 48, W02508, doi:10.1029/2011WR010993.
Soltani, K., & Azari, A. (2022). Forecasting groundwater anomaly in the future using satellite information and machine learning. Journal of Hydrology, 612 (2), 128052.
Soltani., K., and Azari, A. 2023. Terrestrial water storage anomaly estimating using machine learning techniques and satellite-based data (a case study of Lake Urmia Basin). Irrigation and Drainage, 72 (4). https://doi.org/10.1002/ird.2863
Soltani, K., Ebtehaj, I., Amiri, A., Azari, A., Gharabaghi, B., & Bonakdari, H. (2021). Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future. Science of The Total Environment 770, 145288.
Strassberg, G., Scanlon, B.R., & Chambers, D. (2009). Evaluation of groundwater storage monitoring with the GRACE satellite: Case study of the High Plains aquifer, central United States. Water Resources Research, 45, W05410.
Tapley, B.D., Bettadpur, S., Watkins, M., & Reigber, C. (2004). The gravity recovery and climate experiment: Mission overview and early results. Geophysical Research Letters, 31, L09607.
Velicogna, I., Tong, J., Zhang, T., & Kimball, J.S. (2012). Increasing subsurface water storage in discontinuous permafrost areas of the Lena River basin, Eurasia, detected from GRACE, Geophys. Res. Lett., 39, L09403.
Voss, K.A., et al., Groundwater depletion in the Middle East from GRACE with implications for transboundary water management in the Tigris‐Euphrates‐Western Iran region. Water resources research, 2013. 49(2): p. 904-914.
Wahr, J., S. Swenson, and I. Velicogna, (2006). Accuracy of GRACE mass estimates. Geophysical Research Letters, 2006. 33(6).
Wahr, J., Molenaar, M., & Bryan, F. (1998). Time variability of the Earth's gravity field: Hydrological and oceanic effects and their possible detection using GRACE. Journal of Geophysical Research: Solid Earth, 1998. 103(B12): 30205-30229.
Wahr, J., Swenson, S., Zlotnicki, V., & Velicogna, I. (2004), Time-variable gravity from GRACE: First results, Geophys. Res. Lett., 31, L11501.
Wang F, Lai H, Li Y, Feng K, Zhang Z, Tian Q, ... and Yang H (2022) Identifying the status of groundwater drought from a GRACE mascon model perspective across China during 2003–2018. Agric. Water Manage. 260:107251.‏ https://doi.org/10.1016/j.agwat.2021.107251
Wei M, Zhou H, Luo Z, Dai M (2022) Tracking inter-annual terrestrial water storage variations over Lake Baikal basin from GRACE and GRACE Follow-On missions. Journal of Hydrology: Regional Studies 40:101004.‏ https://doi.org/10.1016/j.ejrh.2022.101004
Yosefvand, F., & Shabanlou, S. (2020). Forecasting of Groundwater Level Using Ensemble Hybrid Wavelet–Self-adaptive Extreme Learning Machine-Based Models. Nat Resour Res 29, 3215–3232 (2020). https://doi.org/10.1007/s11053-020-09642-2
Zeynoddin, M., Bonakdari H., Azari, A., Ebtehaj, I., Gharabaghi, B., & Madavar, H.R. (2018). Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate, Journal of environmental management 222, 190-206.
Zeynoddin, M., Bonakdari, H., Ebtehaj, I., Azari, A., & Gharabaghi, B., (2020). A generalized linear stochastic model for lake level prediction, Science of The Total Environment, 723, 138015.