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A Machine Learning-based Approach for Groundwater Mapping

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Abstract

In Bangladesh, groundwater is the main source of both drinking water and irrigation. Suction lift pumps and force mode of operation are the predominant technologies for groundwater abstraction in Bangladesh. For a sustainable usage policy, it is thus important to identify which technology would be more appropriate for which area in Bangladesh. With that aim in mind, this paper proposes a methodology that leverages the power of machine learning that can potentially learn intricate relationships between the (annual maximum) groundwater level (GWL) and the relevant hydrogeological factors (HGFs). A number of machine learning algorithms—both classification and regression models—was trained. Our classification models were trained as a binary classifier to predict the abstraction technology of a particular point. Notably, our best classification model was based on the Random Forest algorithm, which achieved an accuracy of 91% and an excellent value of 96% for the area under receiver operating characteristics curve, indicating its strong discriminant capability. We also identified (elevation derived from) digital elevation model, specific yield and lithology as the three most important HGFs for GWL in Bangladesh. On the other hand, to predict the actual (annual maximum) GWL, we employed a two-stage approach, where we first employed the above-mentioned classification model to identify the suitable abstraction technology for the point of interest and subsequently predict the actual GWL using the appropriate Random Forest regressor. This also had a reasonable accuracy (minimum absolute error was less than 1 for suction mode and less than 5 for the force mode). Finally, using our prediction models, we prepared groundwater (technology) maps for the whole Bangladesh.

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Code Availability

The code is available at the following link: https://github.com/rizvi23061998/GWL_BD.

References

  • Abdollahi, S., Pourghasemi, H. R., Ghanbarian, G. A., & Safaeian, R. (2019). Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions. Bulletin of Engineering Geology and the Environment, 78(6), 4017–4034.

    Google Scholar 

  • Al-Abadi, A. M., Al-Temmeme, A. A., & Al-Ghanimy, M. A. (2016). A GIS-based combining of frequency ratio and index of entropy approaches for mapping groundwater availability zones at Badra–Al Al-Gharbi–Teeb areas, Iraq. Sustainable Water Resources Management, 2(3), 265–283.

    Google Scholar 

  • Alam, M. K., Hasan, K. M. S., & Khan, M. R. (1990). Geological map of Bangladesh. United States Geological Survey Bangladesh, Government of Bangladesh

  • Althuwaynee, O. F., Pradhan, B., Park, H.-J., & Lee, J. H. (2014). A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. CATENA, 114, 21–36.

    Google Scholar 

  • Altman, D. G., & Bland, J. M. (1994). Diagnostic tests. 1: Sensitivity and specificity. BMJ: British Medical Journal, 308(6943), 1552.

    Google Scholar 

  • Arabameri, A., Lee, S., Tiefenbacher, J. P., & Ngo, P. T. T. (2020). Novel ensemble of MCDM-artificial intelligence techniques for groundwater-potential mapping in arid and semi-arid regions (Iran). Remote Sensing, 12(3), 490.

    Google Scholar 

  • BADC (Bangladesh Agricultural Development Corporation). (2019). Minor irrigation survey report 2017–18. In Bangladesh Agricultural Development Corporation. Ministry of Agriculture, Government of Bangladesh.

  • Brammer, H. (2012). Physical geography of Bangladesh. The University Press Ltd.

    Google Scholar 

  • Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.

    Google Scholar 

  • Bricker, S. H., Banks, V. J., Galik, G., Tapete, D., & Jones, R. (2017). Accounting for groundwater in future city visions. Land Use Policy, 69, 618–630.

    Google Scholar 

  • Burgess, W. G., Shamsudduha, M., Taylor, R. G., Zahid, A., Ahmed, K. M., Mukherjee, A., et al. (2017). Terrestrial water load and groundwater fluctuation in the Bengal Basin. Scientific Reports, 7(1), 3872.

    Google Scholar 

  • Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.

    Google Scholar 

  • Daliakopoulos, I. N., Coulibaly, P., & Tsanis, I. K. (2005). Groundwater level forecasting using artificial neural networks. Journal of Hydrology, 309(1), 229–240.

    Google Scholar 

  • Dalin, C., Wada, Y., Kastner, T., & Puma, M. J. (2018). Erratum: Corrigendum: Groundwater depletion embedded in international food trade. Nature, 553(7688), 366–366.

    Google Scholar 

  • Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. In Proceedings of the 23rd international conference on Machine learning (pp. 233–240).

  • DPHE. (2008). Union Wise Water Technology Mapping, Government of the People’s Republic of Bangladesh (GoB), Department of Public Health Engineering (DPHE). Project Director Sanitation, Hygiene and Water Supply Project (GOB - UNICEF) DPHE, Dhaka.

  • EPC/MMP. (1991). Dhaka region groundwater and subsidence study. Final Report, Vol.2, main report, Dhaka WASA.

  • Earth Resources Observation and Science (EROS) Center. (2021). U.S. Geological Survey.

  • Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.

    Google Scholar 

  • Freedman, D. A. (2005). Statistical models: Theory and practice. Cambridge University Press.

    Google Scholar 

  • Harvey, C. F., Ashfaque, K. N., Yu, W., Badruzzaman, A. B. M., Ali, M. A., Oates, P. M., et al. (2006). Groundwater dynamics and arsenic contamination in Bangladesh. Chemical Geology, 228(1–3), 112–136.

    Google Scholar 

  • Hasda, R., Rahaman, M. F., Jahan, C. S., Molla, K. I., & Mazumder, Q. H. (2020). Climatic data analysis for groundwater level simulation in drought prone Barind Tract, Bangladesh: Modelling approach using artificial neural network. Groundwater for Sustainable Development, 10, 100361.

    Google Scholar 

  • Hoque, M. A., & Adhikary, S. K. (2020). Prediction of groundwater level using artificial neural network and multivariate time series models. 5th International Conference on Civil Engineering for Sustainable Development (ICCESD 2020),7~9 February 2020, KUET, Khulna, Bangladesh.

  • Hossain, M. M., & Zahid, A. (2014). Bangladesh Water Development Board: A bank of hydrological data essential for planning and design in water sector. In Proceedings of the 2nd International Conference on Advances in Civil Engineering 2014. Chittagong University of Engineering and Technology, Bangladesh, December 2014. Proceedings of the 2nd International Conference on Advances in Civil Engineering 2014. Chittagong University of Engineering and Technology, Bangladesh, December 2014.

  • Husna, N., Bari, S. H., Hussain, M. M., Ur-rahman, M. T., & Rahman, M. (2016). Ground water level prediction using artificial neural network. International Journal of Hydrology Science and Technology, 6(4), 371–381.

    Google Scholar 

  • Jalalkamali, A., Sedghi, H., & Manshouri, M. (2010). Monthly groundwater level prediction using ANN and neuro-fuzzy models: A case study on Kerman plain, Iran. Journal of Hydroinformatics, 13(4), 867–876.

    Google Scholar 

  • Kasiviswanathan, K. S., Saravanan, S., Balamurugan, M., & Saravanan, K. (2016). Genetic programming based monthly groundwater level forecast models with uncertainty quantification. Modeling Earth Systems and Environment, 2(1), 27.

    Google Scholar 

  • Khatibi, R., & Nadiri, A. A. (2021). Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity. Geoscience Frontiers, 12(2), 713–724.

    Google Scholar 

  • Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI, 14(1), 1137–1145.

    Google Scholar 

  • Lallahem, S., Mania, J., Hani, A., & Najjar, Y. (2005). On the use of neural networks to evaluate groundwater levels in fractured media. Journal of Hydrology, 307(1), 92–111.

    Google Scholar 

  • Lee, C. F., Li, J., Xu, Z. W., & Dai, F. C. (2001). Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology, 40(3), 381–391.

    Google Scholar 

  • Lumley, T., & Lumley, M. T. (2013). Package ‘leaps.’ Regression Subset Selection. Thomas Lumley Based on Fortran Code by Alan Miller. Available online: http://CRAN.R-project.org/package=leaps (Accessed on 18 March 2018)

  • Machiwal, D., Jha, M. K., & Mal, B. C. (2011). Assessment of groundwater potential in a semi-arid region of India using remote sensing, GIS and MCDM techniques. Water Resources Management, 25(5), 1359–1386.

    Google Scholar 

  • Miraki, S., Zanganeh, S. H., Chapi, K., Singh, V. P., Shirzadi, A., Shahabi, H., & Pham, B. T. (2019). Mapping groundwater potential using a novel hybrid intelligence approach. Water Resources Management, 33(1), 281–302.

    Google Scholar 

  • Moazamnia, M., Hassanzadeh, Y., Nadiri, A. A., Khatibi, R., & Sadeghfam, S. (2019). Formulating a strategy to combine artificial intelligence models using Bayesian model averaging to study a distressed aquifer with sparse data availability. Journal of Hydrology, 571, 765–781.

    Google Scholar 

  • Mohsen, B., Keyvan, A., & Coppola, E. A. (2010). Comparative study of SVMs and ANNs in aquifer water level prediction. Journal of Computing in Civil Engineering, 24(5), 408–413.

    Google Scholar 

  • 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.

    Google Scholar 

  • Nadiri, A. A., Razzagh, S., Khatibi, R., & Sedghi, Z. (2021). Predictive groundwater levels modelling by inclusive multiple modelling (IMM) at multiple levels. Earth Science Informatics, 14(2), 749–763.

    Google Scholar 

  • Nguyen, P. T., Ha, D. H., Nguyen, H. D., Van Phong, T., Trinh, P. T., Al-Ansari, N., et al. (2020). Improvement of credal decision trees using ensemble frameworks for groundwater potential modeling. Sustainability, 12(7), 2622.

    Google Scholar 

  • Nourani, V., Mogaddam, A. A., & Nadiri, A. O. (2008). An ANN-based model for spatiotemporal groundwater level forecasting. Hydrological Processes, 22(26), 5054–5066.

    Google Scholar 

  • Nowreen, S., Newton, I. H., Zzaman, R. U., Islam, A. K. M. S., Islam, G. M. T., & Alam, M. S. (2021). Development of potential map for groundwater abstraction in the northwest region of Bangladesh using RS-GIS-based weighted overlay analysis and water-table-fluctuation technique. Environmental Monitoring and Assessment, 193(1), 24.

    Google Scholar 

  • Oke, S. A., & Fourie, F. (2017). Guidelines to groundwater vulnerability mapping for Sub-Saharan Africa. Groundwater for Sustainable Development, 5, 168–177.

    Google Scholar 

  • Powers, D. M. W. (2020). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint

  • Qureshi, A. S., Ahmed, Z., & Krupnik, T. J. (2014). Groundwater management in Bangladesh: an analysis of problems and opportunities. Cereal Systems Initiative for South Asia Mechanization and Irrigation (CSISA-MI) Project, Research Report No. 2, Dhaka, Bangladesh: CIMMYT.

  • Ravenscroft, P., Burgess, W. G., Ahmed, K. M., Burren, M., & Perrin, J. (2005). Arsenic in groundwater of the Bengal Basin, Bangladesh: Distribution, field relations, and hydrogeological setting. Hydrogeology Journal, 13(5–6), 727–751. https://doi.org/10.1007/s10040-003-0314-0

    Article  Google Scholar 

  • Razandi, Y., Pourghasemi, H. R., Neisani, N. S., & Rahmati, O. (2015). Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS. Earth Science Informatics, 8(4), 867–883.

    Google Scholar 

  • Salam, R., Towfiqul Islam, A. R. M., & Islam, S. (2020). Spatiotemporal distribution and prediction of groundwater level linked to ENSO teleconnection indices in the northwestern region of Bangladesh. Environment, Development and Sustainability, 22(5), 4509–4535.

    Google Scholar 

  • Salem, G. S. A., Kazama, S., Shahid, S., & Dey, N. C. (2018). Impacts of climate change on groundwater level and irrigation cost in a groundwater dependent irrigated region. Agricultural Water Management, 208, 33–42.

    Google Scholar 

  • Sameen, M. I., Pradhan, B., & Lee, S. (2019). Self-learning random forests model for mapping groundwater yield in data-scarce areas. Natural Resources Research, 28(3), 757–775.

    Google Scholar 

  • Sarkar, B. C., Deota, B. S., Raju, P. L. N., & Jugran, D. K. (2001). A Geographic Information System approach to evaluation of groundwater potentiality of Shamri micro-watershed in the Shimla Taluk, Himachal Pradesh. Journal of the Indian Society of Remote Sensing, 29(3), 151–164.

    Google Scholar 

  • Shahid, S. (2008). Spatial and temporal characteristics of droughts in the western part of Bangladesh. Hydrological Processes, 22(13), 2235–2247.

    Google Scholar 

  • Shamsudduha, M., Chandler, R. E., Taylor, R. G., & Ahmed, K. M. (2009). Recent trends in groundwater levels in a highly seasonal hydrological system: The Ganges-Brahmaputra-Meghna Delta. Hydrology and Earth System Sciences, 13(12), 2373–2385. https://doi.org/10.5194/hess-13-2373-2009

    Article  Google Scholar 

  • Shiri, J., & Kişi, Ö. (2011). Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Computers and Geosciences, 37(10), 1692–1701.

    Google Scholar 

  • WARPO. (2000). Draft Development Strategy (DDS), Estimation of groundwater resources, Annex-C, Appendix 6, National Water Management Plan, Water Resources Planning Organization, Ministry of Water Resources.

  • Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses: A guide to conservation planning. US Department of Agriculture.

    Google Scholar 

  • Yamamoto, J. K. (2000). An alternative measure of the reliability of ordinary kriging estimates. Mathematical Geology, 32(4), 489–509.

    Google Scholar 

  • Yoon, H., Hyun, Y., Ha, K., Lee, K.-K., & Kim, G.-B. (2016). A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions. Computers and Geosciences, 90, 144–155.

    Google Scholar 

  • Zhou, T., Wang, F., & Yang, Z. (2017). Comparative analysis of ANN and SVM models combined with wavelet preprocess for groundwater depth prediction. Water, 9(10), 781.

    Google Scholar 

  • Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (statistical Methodology), 67(2), 301–320.

    Google Scholar 

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Acknowledgments

This work is part of the project titled ‘Development of IoT enabled data logger to monitor groundwater and analysis of the collected data’ under the innovation fund of ICT Division, Bangladesh. It was further supported by the AI for Earth Grant for a project titled “GWMap: Applying Machine Learning to map groundwater levels in Bangladesh.”

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Author notes

  1. Rashed Uz Zzaman and Sara Nowreen have contributed equaly to this work.

Authors and Affiliations

  1. Institute of Water and Flood Management, BUET, Dhaka, 1000, Bangladesh

    Rashed Uz Zzaman & Sara Nowreen

  2. Department of CSE, BUET, ECE Building, West Palashi, Dhaka, 1205, Bangladesh

    Irtesam Mahmud Khan, Md. Rajibul Islam, Nabil Ibtehaz, M. Saifur Rahman & M. Sohel Rahman

  3. Bangladesh Water Development Board, Dhaka, Bangladesh

    Anwar Zahid

  4. Department of Public Health Engineering (DPHE), Dhaka, Bangladesh

    Dilruba Farzana

  5. Bangladesh Agricultural Development Corporation, Dhaka, Bangladesh

    Afroza Sharmin

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  1. Rashed Uz Zzaman
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  2. Sara Nowreen
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  3. Irtesam Mahmud Khan
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Correspondence to Sara Nowreen or M. Sohel Rahman.

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Zzaman, R.U., Nowreen, S., Khan, I.M. et al. A Machine Learning-based Approach for Groundwater Mapping. Nat Resour Res 31, 281–299 (2022). https://doi.org/10.1007/s11053-021-09977-4

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