A Stochastic Modelling Technique for Groundwater Level Forecasting in an Arid Environment Using Time Series Methods

In arid and semi-arid environments, groundwater plays a significant role in the ecosystem. In the last decades, groundwater levels have decreased due to the increasing demand for water, weak irrigation management and soil damage. For the effective management of groundwater, it is important to model and predict fluctuations in groundwater levels. In this study, several time series models were applied to predict groundwater level forecasting in Kashan plain, Isfahan province, Iran. At first, to reduce the calculation volume, the water table depths in 36 piezometric wells were clustered based on the Vard algorithm. Consequently, we categorized the 36 wells into five clusters. For each cluster, five time series models of auto-regressive (AR), moving-average (MA), auto-regressive moving-average (ARMA), auto-regressive integrated moving-average (ARIMA) and seasonal auto-regressive integrated moving-average (SARIMA) were applied. The results showed that the AR model with a two-times lag (AR(2)), shows the best forecasting of groundwater level for 60 months ahead of the five clusters, with a high accuracy of R2 (0.89, 0.89, 0.95, 0.95 and 0.75 in clusters 1 to 5, respectively). According to the results, the average groundwater level fluctuation in 2010 and 2016 was 74.58 and 80.71 m, respectively. With these conditions, the groundwater depletion rate would be 1.02 m per year in 2016. We combined several time series models for a better performance of prediction of groundwater level. We can conclude that combining time series models have an advantage in terms of groundwater level forecasting.

[1]  E. J. Hannan,et al.  Multiple time series , 1970 .

[2]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[3]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[4]  I. Rodríguez‐Iturbe,et al.  Random Functions and Hydrology , 1984 .

[5]  Gwo-Fong Lin,et al.  An aggregation-disaggregation approach for hydrologic time series modelling , 1992 .

[6]  Wendy D. Graham,et al.  Comparison of univariate and transfer function models of groundwater fluctuations , 1993 .

[7]  K. Hipel,et al.  Time series modelling of water resources and environmental systems , 1994 .

[8]  Richard A. Davis,et al.  Introduction to time series and forecasting , 1998 .

[9]  Richard N. Palmer,et al.  Water Resources Implications of Global Warming: A U.S. Regional Perspective , 1999 .

[10]  Jin‐Yong Lee,et al.  Use of hydrologic time series data for identification of recharge mechanism in a fractured bedrock aquifer system , 2000 .

[11]  Stefan Bender,et al.  Re-identifying register data by survey data: An empirical study , 2001 .

[12]  N. Arnell,et al.  Hydrology and water resources , 2001 .

[13]  Fang-Mei Tseng,et al.  Combining neural network model with seasonal time series ARIMA model , 2002 .

[14]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[15]  David M. Hannah,et al.  Techniques for assessing the climatic sensitivity of river flow regimes , 2004 .

[16]  M. Sophocleous.  Climate Change: Why Should Water Professionals Care? , 2004, Ground water.

[17]  Yunjung Hyun,et al.  Time series modeling for evaluation of groundwater discharge rates into an urban subway system , 2005 .

[18]  T. Barnett,et al.  Potential impacts of a warming climate on water availability in snow-dominated regions , 2005, Nature.

[19]  K. S. Gundogdu,et al.  Mapping multi-year groundwater depth patterns from time-series analyses of seasonally lowest depth-to-groundwater maps in irrigation areas , 2007 .

[20]  P. Mérot,et al.  Hedgerow impacts on soil‐water transfer due to rainfall interception and root‐water uptake , 2008 .

[21]  K. S. Gundogdu,et al.  Mapping multi‐year ground water salinity patterns in irrigation areas using time‐series analysis of ground water salinity maps , 2008 .

[22]  I. Jolly,et al.  A review of groundwater–surface water interactions in arid/semi‐arid wetlands and the consequences of salinity for wetland ecology , 2008 .

[23]  Durdu Ömer Faruk.  A hybrid neural network and ARIMA model for water quality time series prediction , 2010, Eng. Appl. Artif. Intell..

[24]  Jan-Yee Lee,et al.  Application of time series analysis on temporal variation of fluoride in groundwater around Southern Taiwan Science Park , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.

[25]  Ergin Erdem,et al.  ARMA based approaches for forecasting the tuple of wind speed and direction , 2011 .

[26]  J. Adamowski,et al.  A wavelet neural network conjunction model for groundwater level forecasting , 2011 .

[27]  A. Gupta,et al.  A stochastic modelling technique for predicting groundwater table fluctuations with Time Series Analysis , 2012 .

[28]  Mehdi Vafakhah,et al.  Application of Several Data-Driven Techniques for Predicting Groundwater Level , 2012, Water Resources Management.

[29]  R. Ghazavi,et al.  Impact of Flood Spreading on Groundwater Level Variation and Groundwater Quality in an Arid Environment , 2012, Water Resources Management.

[30]  Marc F. P. Bierkens,et al.  Software for hydrogeologic time series analysis, interfacing data with physical insight , 2012, Environ. Model. Softw..

[31]  COMPARISON OF ANN AND TIME SERIES APPROPRIATELY IN PREDICTION OF GROUND WATER TABLE (CASE STUDY: BAKHTEGAN BASIN) , 2013 .

[32]  Ping Feng,et al.  An Integrated Groundwater Management Mode Based on Control Indexes of Groundwater Quantity and Level , 2013, Water Resources Management.

[33]  Yaonan Zhang,et al.  Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions , 2014, Water Resources Management.

[34]  Fawen Li,et al.  Risk Assessment of Groundwater and its Application. Part II: Using a Groundwater Risk Maps to Determine Control Levels of the Groundwater , 2014, Water Resources Management.

[35]  Javier Ferrer,et al.  Modeling Water Resources and River-Aquifer Interaction in the Júcar River Basin, Spain , 2014, Water Resources Management.

[36]  Samad Emamgholizadeh,et al.  Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) , 2014, Water Resources Management.