Relationship between rainfall and groundwater level using time-lagged regression

Document Type : Complete scientific research article

Authors

1 M.Sc. Graduate in Watershed Management, Dept. of Rangeland and Watershed Management, Faculty of Agriculture and Natural Resources, University of Gonbad Kavous, Iran.

2 Corresponding Author, Assistant Prof. in Engineering Hydrology, Dept. of Rangeland and Watershed Management, Faculty of Agriculture and Natural Resources, University of Gonbad Kavous, Iran.

3 . Associate Prof. in Environmental Hydrogeology, Dept. of Rangeland and Watershed Management, Faculty of Agriculture and Natural Resources, University of Gonbad Kavous, Iran.

4 Associate Prof. in Statistics, Dept. of Statistics, Faculty of Science, Golestan University, Iran.

Abstract

In Iran, the climatic conditions are such that even in the rainiest areas of the country, there is a need for groundwater resources, and this demand is increasing every year. Since, groundwater is one of the most valuable water resources in Iran, it is very necessary to predict its changes in order to use it optimally with the aim of sustainable development. One of the most complex hydrological processes in nature is the rainfall-groundwater level process, which is affected by various physical and hydrological parameters. Although, various models have been presented to predict the changes in the groundwater level using the rainfall patterns, but less attention has been paid to the transfer function model. Hence, the main objective of this research is to introduce and use the transfer function (TF) model to predict the monthly groundwater level using rainfall data and to compare its results with ANFIS and Artificial Neural Network (ANN) models.
In the present study, 30-year data (1992-2021) of meteorological stations and observation wells in 3 watersheds of Galikesh, Ramian and Mohamadabad were used to model the rainfall and groundwater level. of the Gorganroud river basin.Then, considering that the years closer to the present time have more accurate information about the situation of this time, the years were considered as a forward process in artificial neural networks. The model fitting and prediction of the groundwater level values using rainfall data for the next 12 months was performed with applying three models: Artificial Neural Network (ANN), Adaptive neuro fuzzy inference system (ANFIS), and transfer function (TF). For this purpose, MINITAB SAS, SPSS, and R software were used. Next step, the validation of the values predicted by the models was evaluated using three indices Mean Absolute Distance (MAD), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).
The results of the autocorrelation plots of the groundwater level of the wells revealed that all time series have a seasonal trend with a period of 12 months. Based on the cross-correlation plots, it was also found that rainfall has direct effect on the groundwater level in the two watersheds of Galikesh and Mohamadabad with lag time of three months and in the Ramian watershed with a delay of one month. The validation results of the models using three indexes MAD, RMSE and MAPE revealed that the artificial neural network model for predicting the groundwater level using monthly rainfall data in all three investigated watersheds had the most appropriate performance (RMSE=0.0778, 0.0243, 0.0532m) and the ANFIS model is ranked second (RMSE=0.1841, 0.0832, 0.1012m). Although the transfer function model was less accurate than the other two methods (RMSE=0.5711, 0.5023, 0.3234m), but this model has performed well in fitting the monthly groundwater level values. This model is very effective in identifying the delay in the impact between the input and output variables, as well as expressing the model based on which the impact of rainfall can be expressed as a model.
The results of this research show that all three models of artificial neural network, ANFIS and transfer function can be used to predict the groundwater level using monthly rainfall values. Consecutive overestimation and underestimation, which increases the error and decreases the performance of the models, was not observed for the three used models. Also, all three models perform well in detecting trends and data changes. However, the artificial neural network model is more accurate than the other models. In addition, when forward process is used in artificial neural network modeling, compared to the case where the complete series of data is used, the efficiency of the model is significantly improved.

Keywords

Main Subjects

References

1.Gunduz, O., & Simsek, C. (2011). Influence of climate change on shallow ground water resources: the link between precipitation and ground water levels in alluvial system. In proceedings of the NATO advanced research workshop (ARW) on climate change and its effects on water resources, edited by A. Baba, G. Tayffur, O. Gunduz, K.W.F. Howard, M.J. Fricdel. Journal of Hydrology, 23 (51), 225-234.
2.Fistikoglu, O., Gunduz, O., & Simsek, C. (2016). The correlation between statistically downscaled precipitation data and groundwater level Records in North-Western Turkey. Water Resources Management, 15 (7), 122-129.
3.Jyrkama, M. I., & Sykes, J. F. (2007). The impact of climate change on spatially varying ground water recharge in the Grand river watershed Ontario. Journal of hydrology, 5 (338), 237-250.
4.Bai, M. D., & Jha, M. K. (2012). Hydrologic time series analysis: theory and practice. Springer Science & Business Media.
5.Bai, C., Hong, M., Wang, D., Zhang, R., & Qian, L. (2014). Evolving an information diffusion model using a genetic algorithm for monthly river discharge time series interpolation and forecasting. Journal of Hydrometeorology, 15 (6), 2236-2249.
6.Bisht, D., & Jangid, A. (2011). Discharge modelling using adaptive neuro-fuzzy inference system. International Journal
of Advanced Science and Technology, 31 (1), 99-114.
7.Rashmi, N., & Sudhir, N. (2017). Multivariate rainfall-runoff modeling of Kulfo River. Journal of Current Environmental Engineering Continued as Current Environmental Management, 4 (3), 177-188.
8.Tsakiri, K. G., Marsellos, A., & Kapetanakis, S. (2018). Artificial neural network and multiple linear regression for flood prediction in Mohawk River, New York. Water, 10 (9), 1158.
9.Mohtasham, M., Dehghani, A.A., Akbarpour, A., & Meftah Halghi, M. (2017). Evaluation of Artificial Neural Networks and MODFLOW Numerical Model in Forecasting Groundwater Table (Case Study: Birjand Aquifer, Southern Khorasan). Iranian Journal of Irrigation and Drainage, 1 (4), 1-10. [In Persian]
10.Mokhtari, Z., Nazemi, A. H., & Nadiri, A. (2013). Forecasting the underground water level with artificial neural networks model (Case study: Shabestar plain). Journal of Geotechnical Geology, 8 (4), 345-353. [In Persian]
11.Abareshi, F., Meftah Halghi, M., Sanikhani, H., & Dehghani, A.A. (2014). Comparison of three intelligence techniques for predicting water table depth fluctuations (Case study: Zarringol plain). Journal of Water and Soil Conservation, 21 (1), 163-180. [In Persian]
12.Saeedi Razavi, B., & Arab, A. (2019). Groundwater Level Prediction of Ajabshir Plain using Fuzzy Logic, Neural Network Models and Time Series. Hydrogeology, 3 (2), 69-81. [In Persian]
13.Jandaghi, N., Azimmohseni, M., & Ghareh Mahmoodlu, M. (2021). Rainfall-runoff process modeling using time series transfer function. Environmental Erosion Research Journal, 11 (2), 111-128. [In Persian]
14.Jandaghi, N. (2022). Modeling of Monthly Groundwater Level Using Artificial Neural Network Model. 40th National Geosciences of Earth Sciences, Tehran, Iran. [In Persian]
15.Ghezelsofli, H., Jandaghi, N., Ghareh Mahmoodlu, M., Azimmohseni, M., & Seyedian, M. (2022). Modeling and forecasting of monthly runoff in the time domain (Case study: River basin Gharasou). Environmental Erosion Research Journal, 12 (3), 165-188. [In Persian]
16.Jandaghi, N. (2023). Study on the forward process in monthly rainfall modeling using Artificial Neural Network. 17th National Conference on Watershed Management Sciences and Engineering of Iran (Watershed Management & Sustainable Food Security), University of Jiroft, Iran. [In Persian]
17.Eslamiyan, S. S., Nasri, M., & Rahimi, N. (2009). Wet and dry periods and its effects on water resources changes in Buin plain watershed. Geography and Environmental Planning Journal, 20 (33), 75-90.
18.Amutha, R., & Porchelvan, P. (2011). Seasonal prediction of groundwater levels using ANFIS and Radial Basis Neural Network, International Journal of Geology. Earth and Environmental Sciences, 1 (1), 98-108.
19.Emamgholizadeh, S., Moslemi, Kh., & Karami, Gh. (2014). Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro- Fuzzy Inference System (ANFIS). Water Resources Management,
28 (15), 5433-5446.
20.Lohani, A. K., & Krishan, G. (2015). Application of Artificial Neural Network for Groundwater Level Simulation in Amritsar and Gurdaspur Districts of Punjab, India. Journal of Earth Science and Climatic Change, 6 (4), 1-5.
21.Rashidi, S., Mohammadian, M., & Vagharfard, H. (2016). Predicting of Groundwater Level Fluctuation Using ANN and ANFIS in Lailakh plain. International Journal of Advanced Biotechnology and Research, 7 (3), 1078-1084.
22.Mohanasundaram, S., Balaji, N., & Suresh Kumar, G. (2017). Transfer function noise modelling of groundwater level fluctuation using threshold rainfall-based binary-weighted parameter estimation approach. Hydrological Sciences Journal, 62 (1), 36-49.
23.Willem, J.Z., Stefanie, A.R.B., Aris, L., & Wilbert, L.B. (2019). Automated Time Series Modeling for Piezometers in the National Database of the Netherlands. Groundwater, 57 (6), 834-843.
24.Salem, A. (2021). Forecasting rainfall in Saudi Arabia via transfer function models. Journal of Research in Environmental and Earth Sciences, 7 (1), 6-11.
25.Heshmatpour, A., Jandaghi, N., Pasand, S., & Ghareh Mahmoodlu, M. (2020). Drought effects on surface water quality in Golestan province for Irrigation Purposes, Case study: Gorganroud River, Physical Geography Quarterly. 13 (48), 75-88. [In Persian]
26.Wunsch, A., Liesch, T., & Broda, S. (2020). Groundwater level forecasting with Artificial Neural Networks: A comparison of LSTM, CNN and NARX. Hydrology and Earth System Sciences. 552, 1-11.
27.Khan, M. Z., & Khan, M. F. (2019). Application of ANFIS, ANN and fuzzy time series models to CO2 emission from the energy sector and global temperature increase. International Journal of Climate Change Strategies and Management. 11 (5), 622-642.
28.Zhang, G. P., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European journal of operational research, 160 (2), 501-514.
29.Lohani, A.K., Kumar, R., & Singh, R. D. (2012). Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. Journal of Hydrology, 1-13.
30.Wang, L. X., & Mendel, J. M. (1992). Generating fuzzy rules by learning from examples. IEEE Transactions on systems, man, and cybernetics, 22 (6), 1414-1427.
31.Crayer, J. (1986). Time series analysis. PWP Publication, Boston. 286 p.
32.Bowerman, B. L., & O’Connel, R. (1993). Forecasting and time series: Anapplied approach, Third edition, mazon Publication, 722 p.
Journal of Water and Soil Conservation
Volume 30, Issue 2
July 2023
Pages 27-49

Files

Share

How to cite

Statistics