Skip to main content

Advertisement

SpringerLink
Log in

Groundwater level prediction using machine learning algorithms in a drought-prone area

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Groundwater resources (GWR) play a crucial role in agricultural crop production, daily life, and economic progress. Therefore, accurate prediction of groundwater (GW) level will aid in the sustainable management of GWR. A comparative study was conducted to evaluate the performance of seven different ML models, such as random tree (RT), random forest (RF), decision stump, M5P, support vector machine (SVM), locally weighted linear regression (LWLR), and reduce error pruning tree (REP Tree) for GW level (GWL) prediction. The long-term prediction was conducted using historical GWL, mean temperature, rainfall, and relative humidity datasets for the period 1981–2017 obtained from two wells in the northwestern region of Bangladesh. The whole dataset was divided into training (1981–2008) and testing (2008–2017) datasets. The output of the seven proposed models was evaluated using the root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), correlation coefficient (CC), and Taylor diagram. The results revealed that the Bagging-RT and Bagging-RF models outperformed other ML models. The Bagging-RT models can effectively improve prediction precision as compared to other models with RMSE of 0.60 m, MAE of 0.45 m, RAE of 27.47%, RRSE of 30.79%, and CC of 0.96 for Rajshahi and RMSE of 0.26 m, MAE of 0.18 m, RAE of 19.87%, RRSE of 24.17%, and 0.97 for Rangpur during training, and RMSE of 0.60 m, MAE of 0.40 m, RAE of 24.25%, RRSE of 29.99%, and CC of 0.96 for Rajshahi and RMSE of 0.38 m, MAE of 0.24 m, RAE of 23.55%, RRSE of 31.77%, and CC of 0.95 for Rangpur during testing stages, respectively. Our study offers an effective and practical approach to the forecast of GWL that could help to formulate policies for sustainable GWR management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Availability of data and materials

The data that support the findings of this study are available from the first author, [Quoc Bao Pham, phambaoquoc@tdmu.edu.vn], upon reasonable request.

References

  1. Abba SI, Pham QB, Usman AG, Linh NTT, Aliyu DS, Nguyen Q, Bach Q-V (2020) Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant. J Water Process Eng. https://doi.org/10.1016/j.jwpe.2019.101081

    Article  Google Scholar 

  2. Adamala S, Srivastava A (2018) Comparative evaluation of daily evapotranspiration using artificial neural network and variable infiltration capacity models. Agric Eng Int CIGR J J 20(1):32–39

    Google Scholar 

  3. Adamowski J, Fung Chan H, Prasher SO, Ozga-Zielinski B, Sliusarieva A (2012) Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resour Res 48(1):1–14

    Article  Google Scholar 

  4. Ajmera TK, Goyal MK (2012) Development of stage discharge rating curve using model tree and neural networks: an application to Peachtree Creek in Atlanta. Expert Syst Appl 39(5):5702–5710

    Article  Google Scholar 

  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

    Article  Google Scholar 

  6. Amit Y, German D (1997) Shape Quantization and Recognition with Randomized Trees. Neural Comput 9(7):1545–1588

    Article  Google Scholar 

  7. Atkeson CG, Moore AW, Schaal S (1996) Locally weighted learning for control. In: Aha DW (ed) Lazy learning. Springer, Dordrecht

    Google Scholar 

  8. Avand M, Janizadeh S, Tien Bui D, Pham VH, Ngo PTT, Nhu V-H (2020) A tree-based intelligence ensemble approach for spatial prediction of potential groundwater. Int J Digital Earth 13:1408–1429

    Article  Google Scholar 

  9. Beg AH, Islam MZ (2016) Advantages and limitations of genetic algorithms for clustering records. In: 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA). IEEE, pp. 2478–2483

  10. Bharti B, Ashish Pandey SK, Tripathi DK (2017) Modelling of runoff and sediment yield using ANN, LS-SVR, REPTree and M5 models. Hydrol Res 48(6):1489–1507

    Article  Google Scholar 

  11. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  12. Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and Regression Trees. CRC Press, Boca Raton, FL, USA

    MATH  Google Scholar 

  13. Breiman L (1998) Arcing classifiers. Ann Stat 26(3):801–849

    MathSciNet  MATH  Google Scholar 

  14. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

    Article  MATH  Google Scholar 

  15. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  16. Busch JR, Ferrari PA, Flesia AG, Fraiman R, Grynberg SP, Leonardi F (2009) Testing statistical hypothesis on random trees and applications to the protein classification problem. J Appl Stat 3:542–563

    MathSciNet  MATH  Google Scholar 

  17. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol TIST 2(3):1–27

    Article  Google Scholar 

  18. Chen Y, Xu W, Zuo J, Yang K (2019) The fire recognition algorithm using dynamic feature fusion and IV-SVM classifier. Cluster Comput 22(3):7665–7675

    Article  Google Scholar 

  19. Chen W, Zhao X, Tsangaratos P, Shahabi H, Ilia I, Xue W, Wang X, Ahmad BB (2020) Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping. J Hydrol 583:124602

    Article  Google Scholar 

  20. Collobert SB (2001) SVMTorch support vector machines for large-scale regression problems. J Mach Learn Res 1(2001):143–160

    MathSciNet  MATH  Google Scholar 

  21. Cutler DR, Edwards TC Jr, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random forests for classification in ecology. Ecology 88(11):2783–2792

    Article  Google Scholar 

  22. Daud MNR, Corne DW (2007) Human readable rule induction in medical data mining: A survey of existing algorithms. WSEAS European Computing Conference, Athens, Greece

  23. Dey NC, Saha R, Parvez M, Bala SK, Islam AKMS, Paul JK et al (2017) Sustainability of groundwater use for irrigation of dry-season crops in northwest Bangladesh. Groundw Sustain Dev 4:66–77

    Article  Google Scholar 

  24. Di Nunno F, Granata F (2020) Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network. Environ Res 190:110062. https://doi.org/10.1016/j.envres.2020.110062

    Article  Google Scholar 

  25. Di Nunno F, Granata F, Gargano R, de Marinis G (2021) Forecasting of extreme storm tide events using NARX neural network-based models. Atmosphere 12(4):512. https://doi.org/10.3390/atmos12040512

    Article  Google Scholar 

  26. Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40(2):139–157

    Article  Google Scholar 

  27. Dietterich T, Kong EB (1995) Machine learning bias, statistical bias, and statistical variance of decision tree algorithms. Technical report http://datam.i2r.astar.edu.sg/datasets/krbd/

  28. Dumitru C, Maria V (2013) Advantages and disadvantages of using neural networks for predictions. Ovidius University Annals, Economic Science Series, pp. 13

  29. Elbeltagi A, Kumari N, Dharpure JK et al (2021) Prediction of combined terrestrial evapotranspiration index (Ctei) over large river basin based on machine learning approaches. Water (Switzerland) 13:1–18. https://doi.org/10.3390/w13040547

    Article  Google Scholar 

  30. Fallah-Mehdipour E, Haddad OB, Mariño MA (2013) Prediction and simulation of monthly groundwater levels by genetic programming. J Hydro-Environ Res 7(4):253–260

    Article  Google Scholar 

  31. Freund E, Rossmann J (1999) Projective virtual reality: Bridging the gap between virtual reality and robotics. IEEE Trans Robot Autom 15(3):411–422

    Article  Google Scholar 

  32. Gang C, Shouhui W, Xiaobo X (2016) Review of spatio-temporal models for short-term traffic forecasting. In: 2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE). IEEE, pp 8–12

    Chapter  Google Scholar 

  33. Ghorbani MA, Zadeh HA, Isazadeh M, Terzi O (2016) A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction. Environ Earth Sci. https://doi.org/10.1007/s12665-015-5096-x

    Article  Google Scholar 

  34. Gong M, Bai Y, Qin J, Wang J, Yang P, Wang S (2020) Gradient boosting machine for predicting return temperature of district heating system: a case study for residential buildings in Tianjin. J Build Eng 27:100950

    Article  Google Scholar 

  35. Gong Y, Zhang Y, Lan S, Wang H (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

    Article  Google Scholar 

  36. Goyal MK, Ojha CSP (2011) Estimation of scour downstream of a ski-jump bucket using support vector and M5 model tree. Water Resour Manag 25(9):2177–2195

    Article  Google Scholar 

  37. Granata F (2019) Evapotranspiration evaluation models based on machine learning algorithms—A comparative study. Agric Water Manag 217:303–315. https://doi.org/10.1016/j.agwat.2019.03.015

    Article  Google Scholar 

  38. Granata F, Gargano R, de Marinis G (2020) Artificial intelligence based approaches to evaluate actual evapotranspiration in wetlands. Sci Total Environ 703:135653. https://doi.org/10.1016/j.scitotenv.2019.135653

    Article  Google Scholar 

  39. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newslett 11(1):10

    Article  Google Scholar 

  40. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  41. Husna NE, Bari SH, Hussain MM, Ur-Rahman MT, Rahman M (2016) Ground water level prediction using artificial neural network. Int J Hydrol Sci Technol 6(4):371–381

    Article  Google Scholar 

  42. Islam ARMT, Mehra B, Salam R, Siddik NA, Patwary MA (2020) Insight into farmers’ agricultural adaptive strategy to climate change in northern Bangladesh. Environ Dev Sustain. https://doi.org/10.1007/s10668-020-00681-6

    Article  Google Scholar 

  43. Islam ARMT, Talukdar S, Mahato S et al (2021) Flood susceptibility modelling using advanced ensemble machine learning models. Geosci Front 12:101075. https://doi.org/10.1016/j.gsf.2020.09.006

    Article  Google Scholar 

  44. Islam ARMT, Ahmed N, Bodrud-Doza M, Chu R (2017) Characterizing groundwater quality ranks for drinking purposes in Sylhet district, Bangladesh, using entropy method, spatial autocorrelation index, and geostatistics. Environ Sci Pollut Res 24(34):26350–26374

    Article  Google Scholar 

  45. Islam MS, Islam ARMT, Rahman F, Ahmed F, Haque MN (2014) Geomorphology and land use mapping of northern part of Rangpur District, Bangladesh. J Geosci Geomat 2(4):145–150

    Google Scholar 

  46. Islam ARMT, Karim MR, Mondol MAH (2021) Appraising trends and forecasting of hydroclimatic variables in the north and northeast regions of Bangladesh. Theoret Appl Climatol 143(1–2):33–50. https://doi.org/10.1007/s00704-020-03411-0

    Article  Google Scholar 

  47. Jahan CS, Mazumder QH, Islam ATMM, Adham MI (2010) Impact of irrigation in Barind area, NW Bangladesh—an evaluation based on the meteorological parameters and fluctuation trend in groundwater table. J Geol Soc India 76(2):134–142

    Article  Google Scholar 

  48. Jajarmizadeh M, Lafdani EK, Harun S, Ahmadi A (2015) Application of SVM and SWAT models for monthly streamflow prediction, a case study in South of Iran. KSCE J Civ Eng 19:345–357

    Article  Google Scholar 

  49. Joseph KS, Ravichandran T (2012) A comparative evaluation of software effort estimation using REPTree and K* in handling with missing values. Aust J Basic Appl Sci 6:312–317

    Google Scholar 

  50. Kalhor K, Emaminejad N (2019) Sustainable development in cities: Studying the relationship between groundwater level and urbanization using remote sensing data. Groundw Sustain Dev 9:100243

    Article  Google Scholar 

  51. Kasiviswanathan KS, Saravanan S, Balamurugan M, Saravanan K (2016) Genetic programming based monthly groundwater level forecast models with uncertainty quantification. Model Earth Syst Environ 2:27

    Article  Google Scholar 

  52. Khalil B, Broda S, Adamowski J, Ozga-Zielinski B, Donohoe A (2015) Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models. Hydrogeol J 23:121–141

    Article  Google Scholar 

  53. Khatibi R, Nadiri AA (2021) Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity. Geosci Front 12:713–724. https://doi.org/10.1016/j.gsf.2020.07.011

    Article  Google Scholar 

  54. Kisi O (2015) Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree. J Hydrol 528:312–320

    Article  Google Scholar 

  55. Koch J, Berger H, Henriksen HJ, Sonnenborg TO (2019) Modelling of the shallow water table at high spatial resolution using random forests. Hydrol Earth Syst Sci 23(11):4603–4619. https://doi.org/10.5194/hess-23-4603-2019

    Article  Google Scholar 

  56. Landwehr N, Hall M, Frank E (2005) Logistic model trees. Mach Learn 59(1–2):161–205

    Article  MATH  Google Scholar 

  57. Malone BP, Minasny B, McBratney AB (2017) Using R for digital soil mapping, vol 35. Springer International Publishing, Cham, Switzerland

    Book  Google Scholar 

  58. Malekzadeh M, Kardar S, Saeb K, Shabanlou S, Taghavi L (2019) A novel approach for prediction of monthly ground water level using a hybrid wavelet and nontuned self-adaptive machine learning model. Water Resour Manag 33:1609–1628. https://doi.org/10.1007/s11269-019-2193-8

    Article  Google Scholar 

  59. Mirarabi A, Nassery HR, Nakhaei M, Adamowski J, Akbarzadeh AH, Alijani F (2019) Evaluation of data-driven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems. Environ Earth Sci 78(15):489. https://doi.org/10.1007/s12665-019-8474-y

    Article  Google Scholar 

  60. Mishra AK, Ratha BK (2016) Study of random tree and random forest data mining algorithms for microarray data analysis. Int J Adv Electr Comput Eng 3(4):5–7

    Google Scholar 

  61. Mohanty S, Jha MK, Raul SK, Panda RK, Sudheer KP (2015) Using artificial neural network approach for simultaneous forecasting of weekly groundwater levels at multiple sites. Water Resour Manag 29(15):5521–5532

    Article  Google Scholar 

  62. MPO (Master Plan Organization) (1987) Groundwater resources of Bangladesh, Technical Report no 5. (Dhaka: Master Plan Organization) Hazra, USA; Sir M MacDonald, UK; Meta, USA; EPC, Bangladesh

  63. Moore DS, Notz WI, Flinger MA (2018) The basic practice of statistics. W.H Freeman and Company, New York

    Google Scholar 

  64. Moosavi V, Vafakhah M, Shirmohammadi B, Behnia N (2013) A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resour Manag 27(5):1301–1321

    Article  Google Scholar 

  65. Nadiri AA, Naderi K, Khatibi R, Gharekhani M (2019) Modelling groundwater level variations by learning from multiple models using fuzzy logic. Hydrol Sci J 64(2):210–226

    Article  Google Scholar 

  66. Najock D, Heyde CO (1982) The number of terminal vertices in certain random trees with an application to stemma construction in philology. J Appl Probab 19:675–680

    Article  MathSciNet  MATH  Google Scholar 

  67. Nguyen PT, Ha DH, Avand M et al (2020) Soft computing ensemble models based on logistic regression for groundwater potential mapping. Appl Sci 10:2469. https://doi.org/10.3390/app10072469

    Article  Google Scholar 

  68. Nourani V, Hosseini Baghanam A, Adamowski J, Kisi O (2014) Applications of hybrid wavelet – artificial Intelligence models in hydrology: a review. J Hydrol 514:358–377

    Article  Google Scholar 

  69. Pham BT, Phong TV, Nguyen-Thoi T, Parial KK, Singh S, Ly H-B, Nguyen KT, Ho LS, Le HV, Prakash I (2020) Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers. Geocarto Int. https://doi.org/10.1080/10106049.2020.1737972

    Article  Google Scholar 

  70. Platt JC (1999) Using analytic QP and sparseness to speed training of support vector machines. Adv Neural Inf Process Syst 11:557–563

    Google Scholar 

  71. Quinlan JR (1992) Learning with continuous classes. Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, World Scientific, Singapore, pp. 343–348

  72. Raghavendra N, Deka PC (2015) Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet–Support vector regression. Cogent Eng 2:999414

    Article  Google Scholar 

  73. Raghavendra NS, Deka PC (2014) Forecasting monthly groundwater table fluctuations in coastal aquifers using support vector regression. In: International Multi Conference on innovations in engineering and technology (IMCIET-2014) (61–69). Elsevier Science and Technology, Bangalore

  74. Rahman ARMS, Hosono T, Quilty JM, Das J, Basak A (2020) Multiscale groundwater level forecasting: coupling new machine learning approaches with wavelet transforms. Adv Water Resour 141:103595

    Article  Google Scholar 

  75. Rahman MS, Islam ARMT (2019) Are precipitation concentration and intensity changing in Bangladesh overtimes? Analysis of the possible causes of changes in precipitation systems. Sci Total Environ 690:370–387

    Article  Google Scholar 

  76. Rajaee T, Ebrahimi H, Nourani V (2019) A review of the artificial intelligence methods in groundwater level modeling. J Hydrol. https://doi.org/10.1016/j.jhydrol.2018.12.037

    Article  Google Scholar 

  77. Rezaie-balf M, Naganna SR, Ghaemi A, Deka PC (2017) Wavelet coupled MARS and M5 model tree approaches for groundwater level forecasting. J Hydrol 553:356–373. https://doi.org/10.1016/j.jhydrol.2017.08.006

    Article  Google Scholar 

  78. Rodriguez JJ, Kuncheva LI, Carlos J (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619–1630

    Article  Google Scholar 

  79. Roy J, Saha S (2021) Integration of artificial intelligence with meta classifiers for the gully erosion susceptibility assessment in Hinglo river basin, Eastern India. Adv Space Res 67:316–333

    Article  Google Scholar 

  80. Sahoo S, Russo TA, Elliott J, Foster I (2017) Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US. Water Resour Res 53(5):3878–3895

    Article  Google Scholar 

  81. Salam R, Islam ARMT (2020) Potential of RT, Bagging and RS ensemble learning algorithms for reference evapotranspiration prediction using climatic data-limited humid region in Bangladesh. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.125241

    Article  Google Scholar 

  82. Salam R, Islam ARMT, Islam S (2020) Spatiotemporal distribution and prediction of groundwater level linked to ENSO teleconnection indices in the northwestern region of Bangladesh. Environ Dev Sustain 22(5):4509–4535. https://doi.org/10.1007/s10668-019-00395-4

    Article  Google Scholar 

  83. Salih SQ, Sharafati A, Khosravi K, Faris H, Kisi O, Tao H, Ali M, Yaseen ZM (2019) River suspended sediment load prediction based on river discharge information: application of newly developed data mining models. Hydrol Sci J 65(4):624–637

    Article  Google Scholar 

  84. Senthil Kumar AR, Ojha CSP, Goyal MK, Singh RD, Swamee PK (2012) Modelling of suspended sediment concentration at Kasol in India using ANN, fuzzy logic and decision tree algorithms. J Hydrol Eng. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000445

    Article  Google Scholar 

  85. Seyam M, Othman F, El-Shafie A (2017) Prediction of stream flow in humid tropical rivers by support vector machines. In: MATEC Web of Conferences, vol 111. EDP Sciences, p 01007.

    Google Scholar 

  86. Shahid S, Hazarika MK (2010) Groundwater drought in the northwestern districts of Bangladesh. Water Resour Manag 24:1989–2006

    Article  Google Scholar 

  87. Shamsudduha M, Taylor RG, Ahmed KM, Zahid A (2011) The impact of intensive groundwater abstraction on recharge to a shallow regional aquifer system: evidence from Bangladesh. Hydrogeol J 19:901–916

    Article  Google Scholar 

  88. Sharafati A, Asadollah SBHS, Neshat A (2020) A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran. J Hydrol 591:125468

    Article  Google Scholar 

  89. Sheikh Khozani Z, Bonakdari H, Zaji AH (2018) Estimating shear stress in a rectangular channel with rough boundaries using an optimized SVM method. Neural Comput Appl 30:1–13. https://doi.org/10.1007/s00521-016-2792-8

    Article  Google Scholar 

  90. Shiri J, Kisi O, Yoon H, Lee KK, Nazemi AH (2013) Predicting groundwater level fluctuations with meteorological effect implications – a comparative study among soft computing techniques. Comput Geosci 56:32–44

    Article  Google Scholar 

  91. Song Y, Zhou H, Wang P, Yang M (2019) Prediction of clathrate hydrate phase equilibria using gradient boosted regression trees and deep neural networks. J Chem Thermodyn 135:86–96

    Article  Google Scholar 

  92. 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

    Article  Google Scholar 

  93. Torgo L (1997). Functional models for regression tree leaves. In: Machine learning, Proceedings of the 14th International Conference (D. Fisher, ed.). Morgan Kaufmann, pp. 385–393.

  94. Vapnik VN (1995) The nature of statistical learning theory. Springer-Verlag, New York Inc

    Book  MATH  Google Scholar 

  95. Vapnik VN (1998) Statistical learning theory. Wiley

    MATH  Google Scholar 

  96. Vapnik VN, Golwich S, Smola AJ (1997) Support vector method for function approximation, regression estimation and signal processing. In: Mozer M, Jordan M, Petsche T (eds) Advances in Neural Information Processing Systems, 9. MIT Press, Cambridge, MA, USA, pp 281–287

    Google Scholar 

  97. Verbyla DL (1987) Classification trees: a new discrimination tool. Can J For Res 17(9):1150–1152

    Article  Google Scholar 

  98. Witten IH, Frank E (2000) Data mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco, CA

    Google Scholar 

  99. Witten IH, Frank E, Trigg L, Hall M, Holmes G, Cunningham SJ (1999) Weka: practical machine learning tools and techniques with Java implementations. Emerging Knowledge Engineering and Connectionist-Based Info. Systems, pp. 192–196

  100. Wöhling T, Burbery L (2020) Eigenmodels to forecast groundwater levels in unconfined river-fed aquifers during flow recession. Sci Total Environ 747:141220

    Article  Google Scholar 

  101. WARPO (Water Resources Planning Organization) (2000) National Water Management Plan. Volume 2: Main Report; Water Resources Planning Organization, Ministry of Water Resources: Dhaka, Bangladesh, 2000

  102. Yadav B, Ch S, Mathur S, Adamowski J (2017) Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction. J Water Land Dev 32(1):103–112

    Article  Google Scholar 

  103. Yadav B, Gupta PK, Patidar N, Himanshu SK (2019) Ensemble modelling framework for groundwater level prediction in urban areas of India. Sci Total Environ 712:135539

    Article  Google Scholar 

  104. Yadav B, Gupta PK, Patidar N, Himanshu SK (2020) Ensemble modelling framework for groundwater level prediction in urban areas of India. Sci Total Environ 712:135539

    Article  Google Scholar 

  105. Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396(1–2):128–138

    Article  Google Scholar 

  106. Yosefvand F, Shabanlou S (2020) Forecasting of groundwater level using ensemble hybrid wavelet–self-adaptive extreme learning machine-based models. Nat Resour Res. https://doi.org/10.1007/s11053-020-09642-2

    Article  Google Scholar 

  107. Yu PS, Yang TC, Chen SY, Kuo CM, Tseng HW (2017) Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting. J Hydrol 552:92–104

    Article  Google Scholar 

  108. Zannat F, Islam ARMT, Rahman MA (2019) Spatiotemporal variability of rainfall linked to ground water level under changing climate in northwestern region, Bangladesh. Eur J Geosci EURAASS 1(1):35–58

    Article  Google Scholar 

  109. 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

    Article  Google Scholar 

  110. Zinat MRM, Salam R, Badhan MA, Islam ARMT (2020) Appraising drought hazard during Boro rice growing period in western Bangladesh. Int J Biometeorol 64(10):1697–1697

    Article  Google Scholar 

Download references

Funding

No external funding.

Author information

Authors and Affiliations

  1. Institute of Applied Technology, Thu Dau Mot University, Binh Duong Province, Vietnam

    Quoc Bao Pham

  2. Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia, Będzińska Street 60, 41-200, Katowice, Sosnowiec, Poland

    Quoc Bao Pham

  3. Department of Soil and Water Conservation Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar, 263145, India

    Manish Kumar

  4. Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, DICEM, Via Di Biasio, 43, 03043, Cassino, Frosinone, Italy

    Fabio Di Nunno & Francesco Granata

  5. Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt

    Ahmed Elbeltagi

  6. Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh

    Abu Reza Md. Towfiqul Islam

  7. Department of Geography, Faculty of Natural Science, Jamia Millia Islamia, New Delhi, Delhi, India

    Swapan Talukdar

  8. Center for Advanced Chemistry, Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    X. Cuong Nguyen

  9. Faculty of Environmental and Chemical Engineering, Duy Tan University, Da Nang, 550000, Vietnam

    X. Cuong Nguyen

  10. Institute of Energy Infrastructure (IEI), Civil Engineering Department, College of Engineering, Universiti Tenaga Nasional(UNITEN), Kajang, 43000, Selangor, Malaysia

    Ali Najah Ahmed

  11. HUTECH University, 475A, Dien Bien Phu, Ward 25, Binh Thanh District, Ho Chi Minh City, Vietnam

    Duong Tran Anh

Authors
  1. Quoc Bao Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fabio Di Nunno
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed Elbeltagi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Francesco Granata
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Abu Reza Md. Towfiqul Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Swapan Talukdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. X. Cuong Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ali Najah Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Duong Tran Anh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

QBP contributed to project administration, conceptualization, writing—original draft, formal analysis, visualization. AE contributed to software, formal analysis, writing—original draft, visualization. MK, FDN, FG, ARMTI, ST contributed to writing, review and editing. XCN, ANA, DTA contributed to supervision, writing, review, editing.

Corresponding author

Correspondence to Duong Tran Anh.

Ethics declarations

Ethics approval

Not applicable.

Consent for participate

Not applicable.

Consent for publish

Not applicable.

Conflict of interests

This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. There are no conflicts of interest to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pham, Q.B., Kumar, M., Di Nunno, F. et al. Groundwater level prediction using machine learning algorithms in a drought-prone area. Neural Comput & Applic 34, 10751–10773 (2022). https://doi.org/10.1007/s00521-022-07009-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07009-7

Keywords

Search

Navigation