Abstract
Inevitable issues concerning the sustainability of groundwater resources are crucial under the present climatic situation. Therefore, the prevision of groundwater environments may able to reinforce the management system. In this respect present study considered a new method to predict long-term groundwater level framework as an alternative option of expensive physical models. The proposed Bidirectional Long Short-Term Memory (BLSTM) model can efficiently capture Spatio-temporal features from historical data. A highway LSTM network is also introduced within the architecture of the model to optimize the analysis. The relative performance of the proposed BLSTM with the highway LSTM (BHLSTM) network compared with simple BLSTM. Stack size increment of the BHLSTM and BLSTM layers can enhance the learning ability and improve by incorporating straight LSTM at the top of the architecture. The proposed model was applied to predict the groundwater level exemplary of the Varuna River basin for twenty years. The model incorporates the historical annual average of total precipitation, temperature, relative humidity, actual evapotranspiration, and groundwater level data to develop and validate the models. The result shows that the signals are captured reasonably well by a stack of four BHLSTM and straight LSTM models in forecasting groundwater levels. The predicted water level range (0—20 mbgl) has four categories low, medium, high, and very high which eventually, illustrates the water-threatened situation in upcoming years in the study area. It is also recommended to exploring this proposed method for further improvements and extensions towards interpreting spatial features.
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Data Availability
Data are available from the corresponding author by request.
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The codes used in this work are available from the corresponding author by request.
References
Adamowski J, Chan H (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40. https://doi.org/10.1016/j.jhydrol.2011.06.013
Afzaal H, Farooque AA, Abbas F, Acharya B, Esau T (2020) Groundwater estimation from major physical hydrology components using artificial neural networks and deep learning. Water 2(1):5
Alizamir M, Kisi O, Zounemat-Kermani M (2018) Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data. Hydrol Sci J 63(1):63–73
Bai Y, Bezak N, Zeng B, Li C, Sapač K, Zhang J (2021) Daily Runoff Forecasting Using a Cascade Long Short-Term Memory Model that Considers Different Variables. Water Resour Manag 19:1–5
Banerjee P, Prasad R, Singh V (2009) Forecasting of groundwater level in hard rock region using artificial neural network. Environ Geol 58(6):1239–1246
CGWB (2019) National Compilation on dynamic Ground Water Resources of India, 2017, Government of India, Ministry of Jal Shakti, Department of Water Resources, RD & GR, Central Ground Water Board, pp 298. http://www.cgwb.gov.in
Chitsazan M, Rahmani G, Neyamadpour A (2015) Forecasting groundwater level by artificial neural networks as an alternative approach to groundwater modeling. J Geol Soc India 85:98–106. https://doi.org/10.1007/s12594-015-0197-4
Coulibaly P, Anctil F, Bob’ee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230(3–4):244–257
Coulibaly P, Anctil F, Aravena R, Bobe’e B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37(4):885–896
Cui Z, Member S, Ke R, Member S, Wang Y (2018) 1801.02143 1–12. https://arxiv.org/abs/1801.02143
Dey S, Shukla UK, Mehrishi P, Mall RK (2021) Appraisal of groundwater potentiality of multilayer alluvial aquifers of the Varuna river basin, India, using two concurrent methods of MCDM. Environ Dev Sustain. https://doi.org/10.1007/s10668-021-01400-5
Djurovic N, Domazet M, Stricevic R, Pocuca V, Spalevic V, Pivic R, Gregoric E, Domazet U (2015) Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS. Sci World J 2015 https://doi.org/10.1155/2015/742138
Feng S, Kang S, Huo Z, Chen S, Mao X (2008) Neural networks to simulate regional groundwater levels affected by human activities. Groundwater 46:80–90. https://doi.org/10.1111/j.1745-6584.2007.00366.x
Garg V (2014) Modeling catchment sediment yield: a genetic programming approach. Nat Hazards 70(1):39–50
Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: Continual prediction with lstm. Neural Comput 12(10):2451–2471
Gong Y, Zhang Y, LanS WH (2016) A comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near lake Okeechobee. Florida Water Resour Manag 30(1):375–391
Gunnink JL, Burrough PA (1996) Interactive spatial analysis of soil attribute patterns using exploratory data analysis (EDA) and GIS. In: Masser I, Salge F (eds) Spatial Analytical Perspectives on GIS. Taylor & Francis, New York, pp 87–99
Guzman SM, Paz JO, Tagert ML (2017) The use of NARX neural networks to forecast daily groundwater levels. Water Resour Manag 31(5):1591–603
Hochreiter S, Schmidhuber J (1997) Ltsm Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
IPCC (2014) Climate change 2014. Synthesis report. Versióninglés, Climate Change Synthesis Report. Contribution of Working Groups i, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. https://doi.org/10.1017/CBO9781107415324
Jeong J, Park E (2019) Comparative applications of data-driven models representing water table fluctuations. J Hydrol 572:261–273
KumarS HS, Singhal DC (2011) Groundwater resources management through flow modeling in lower part of Bhagirathi - Jalangi interfluve, Nadia. West Bengal J Geol Soc India 78:587–598. https://doi.org/10.1007/s12594-011-0118-0
Lallahem S, Mania J, Hani A, Najjar Y (2005) On the use of neural networks to evaluate groundwater levels in fractured media. J Hydrol 307(1–4):92–111
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
Lipton ZC, Berkowitz J, Elkan CA (2015) Critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019
Mall R K, Gupta A, Singh R, Singh R, Rathore L S (2006) Water resources and climate change: An Indian perspective. Current science pp. 1610–1626
Mirzavand MGR (2015) A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods. Water Resour Manag 29(4):1315–1328
Natural Resources Management and Environment Department (NR) under Food and Agriculture Organization (FAO) of the United Nations (1998) Crop Evapotranspiration – Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Papers – 56.
Rakhshandehroo G, Akbari H, AfshariIgder M, Ostadzadeh E (2017) Long-Term Groundwater-Level Forecasting in Shallow and Deep Wells Using Wavelet Neural Networks Trained by an Improved Harmony Search Algorithm. J Hydrol Eng 23:04017058. https://doi.org/10.1061/(asce)he.1943-5584.0001591
Style G (2009) Application of artificial neural network in the field of Geohydrology, university of the free State, South Africa
Sudheer KP, Gosain AK, Ramasastri KS (2002) Adata-driven algorithm for constructing artificial neural network rainfall runoff models. Hydrol Process 16(6):1325–1330
Suryanarayana C, Sudheer C, Mahammood V, Panigrahi BK (2014) An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India. Neurocomputing 145:324–335. https://doi.org/10.1016/j.neucom.2014.05.026
Wunsch A, Liesch T, Broda S (2020) Groundwater Level Forecasting with Artificial Neural Networks: A Comparison of LSTM, CNN and NARX. Hydrol Earth Syst Sci Discuss 23:1–23
Zhang J, Zhu Y, Zhang X, Ye M, Yang J (2018) Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas. J Hydrol 561:918–929. https://doi.org/10.1016/j.jhydrol.2018.04.065
Zhu YY, Zhou HC (2009) Rough fuzzy inference model and its application in multi-factor medium and long-term hydrological forecast. Water Resour Manag 23(3):493–507
Acknowledgements
Sangita Dey thanks the Women Scientist Scheme (SR/WOS-A/EA-1004/2015), Department of Science and Technology, New Dealhi for support. Climate Data were acquired from India Meteorological Department, New Delhi, India. The authors are also thankful to DST- Mahamana Centre of Excellence in Climate Change Research (DST-MCECCR) for providing the lab facility and other support. The authors express their sincere gratitude to all reviewers and the Editor, Assistant Editor, for their constructive comments that have improved the paper.
Funding
Women Scientist Scheme A (Reference no. SR/ WOS-A/EA-1004/2015), Department of Science and Technology, New Delhi.
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All authors contributed to the study's conception and design. Material preparation, data collection, and analysis done by Arabin Kumar Dey and Sangita Dey. The first draft written by Sangita Dey and review and editing done by R. K. Mall and Sangita Dey. All authors read and approved the final manuscript.
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Dey, S., Dey, A.K. & Mall, R.K. Modeling Long-term Groundwater Levels By Exploring Deep Bidirectional Long Short-Term Memory using Hydro-climatic Data. Water Resour Manage 35, 3395–3410 (2021). https://doi.org/10.1007/s11269-021-02899-z
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DOI: https://doi.org/10.1007/s11269-021-02899-z