Comparison of machine learning models for predicting groundwater level, case study: Najafabad region

Zarafshan, Pejman; Etezadi, Hamed; Javadi, Saman; Roozbahani, Abbas; Hashemy, S. Mehdi; Zarafshan, Payam

doi:10.1007/s11600-022-00948-8

Artykuł - szczegóły

Tytuł artykułu

Comparison of machine learning models for predicting groundwater level, case study: Najafabad region

Autorzy

Zarafshan Pejman , Etezadi Hamed , Javadi Saman , Roozbahani Abbas , Hashemy S. Mehdi , Zarafshan Payam

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-022-00948-8

Warianty tytułu

Języki publikacji

Abstrakty

Water resources, consisting of surface water and groundwater, are considered to be among the crucial natural resources in most arid and semiarid regions. Groundwater resources as the sustainable yields can be predicted, whereas this is one of the important stages in water resource management. To this end, several models such as mathematical, statistical, empirical, and conceptual can be employed. In this paper, machine learning and deep learning methods as conceptual ones are applied for the simulations. The selected models are support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), and multilayer perceptron (MLP). Next, these models are optimized with the adaptive moment estimation (ADAM) optimization algorithm which results in hybrid models. The hyper-parameters of the stated models are optimized with the ADAM method. The root mean squared error (RMSE), mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R₂) are used to evaluate the accuracy of the simulated groundwater level. To this end, the aquifer hydrograph is used to compare the results with observations data. So, the RMSE and R₂ show that the accuracy of the machine learning and deep learning models is better than the numerical model for the given data. Moreover, the MSE is approximately the same in all three cases (ranging from 0.7113 to 0.6504). Also, the total value of R₂ and RMSE for the best hybrid model is 0.9617 and 0.7313, respectively, which are obtained from the model output. The results show that all three techniques are useful tools for modeling hydrological processes in agriculture and their computational capabilities and memory are similar.

Słowa kluczowe

SVR ANFIS MLP ADAM groundwater

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2023

Tom

Vol. 71, no. 4

Strony

1817--1830

Opis fizyczny

Bibliogr. 59 poz., rys., tab.

Twórcy

autor

Zarafshan Pejman

Department of Water Engineering, College of Aburaihan, University of Tehran, Tehran, Iran

autor

Etezadi Hamed

Department of Agro-Technology, College of Aburaihan, University of Tehran, Tehran, Iran

autor

Javadi Saman

Department of Water Engineering, College of Aburaihan, University of Tehran, Tehran, Iran

autor

Roozbahani Abbas

Department of Water Engineering, College of Aburaihan, University of Tehran, Tehran, Iran
Center for Sustainable Development (CSD), College of Arts and Sciences, Qatar University, Doha, Qatar

autor

Hashemy S. Mehdi

Department of Water Engineering, College of Aburaihan, University of Tehran, Tehran, Iran

autor

Zarafshan Payam

p.zarafshan@ut.ac.ir

Department of Agro-Technology, College of Aburaihan, University of Tehran, Tehran, Iran

https://orcid.org/0000-0003-2462-8217

Bibliografia

1. Affandi AK, Watanabe K (2007) Daily groundwater level fluctuation forecasting using soft computing technique. Nature and Sci 5(2):1–10
2. Ahn H (2000) Modeling of groundwater heads based on second-order difference time series models. J Hydrol 234(1–2):82–94
3. Barzegar R, Moghaddam AA, Baghban H (2016) A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer. Iran Stoch Environ Res Risk Assess 30(3):883–899
4. Bishop CM (1995) Training with noise is equivalent to Tikhonov regularization. Neural Comput 7(1):108–116
5. Butt MF, Albusoda A, Farmer AD, Aziz Q (2020) The anatomical basis for transcutaneous auricular vagus nerve stimulation. J Anat 236(4):588–611. https://doi.org/10.1111/joa.13122
6. Chen W, Panahi M, Khosravi K, Pourghasemi HR, Rezaie F, Parvinnezhad D (2019) Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization. J of Hydrol 572:435–448
7. Choubin B, Malekian A (2017) Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions. Environ Earth Sci 76(15):1–10
8. Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J of Hydrol 309(1–4):229–240
9. Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen CW, Han Z, Pham BT (2020) Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides 17(3):641–658. https://doi.org/10.1007/s10346-019-01286-5
10. Di Nunno F, Abba SI, Pham BQ, Islam ART, Talukdar S, Francesco G (2022) Groundwater level forecasting in Northern Bangladesh using nonlinear autoregressive exogenous (NARX) and extreme learning machine (ELM) neural networks. Arab J Geosci 15:647
11. Ghalamzan A, Das G, Gould I, Zarafshan P, Rajendran V, Heselden J, Badiee A, Wright I, Pearson S (2022) Applications of robotic and solar energy in precision agriculture and smart farming. In: Solar Energy Advancements in Agriculture and Food Production Systems. Elsevier, Book Chapter.
12. Gholami VCKW, Chau KW, Fadaee F, Torkaman J, Ghaffari A (2015) Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers. J of Hydrol 529:1060–1069
13. Ghose DK, Panda SS, Swain PC (2010) Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networks. J of Hydrol 394(3–4):296–304
14. Gómez C, Green DR (2017) Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping. Arab J Geosci 10(9):1–17
15. Guo Q, Wang Y, Gao X, Ma T (2007) A new model (DRARCH) for assessing groundwater vulnerability to arsenic contamination at basin scale: a case study in Taiyuan basin, northern China. Environ Geol 52(5):923–932
16. Guzmán SM, Paz JO, Tagert MLM, Mercer AE, Pote JW (2018) An integrated SVR and crop model to estimate the impacts of irrigation on daily groundwater levels. Agric Syst 159:248–259
17. Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) GANs Trained by a two time-scale update rule converge to a local nash equilibrium. Adv Neural Inf Process Syst, 30.
18. Iqbal M, Naeem UA, Ahmad A, Ghani U, Farid T (2020) Relating groundwater levels with meteorological parameters using ANN technique. Meas Tech 166:1063–1081
19. Jamab Consulting Engineers (2002) "Water resources planning in Zayandehrood river basin." JCE, Tehran, Iran (in Persian)
20. Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
21. Jebastina N, Arulraj GP (2018) Spatial Prediction of Nitrate Concentration Using GIS and ANFIS Modelling in Groundwater. Bull Environ Contam Toxicol 101(3):403–409
22. Jeihouni E, Eslamian S, Mohammadi M, Zareian MJ (2019) Simulation of groundwater level fluctuations in response to main climate parameters using a wavelet–ANN hybrid technique for the Shabestar Plain. Iran Environ Earth Sci 78(10):1–9
23. Kagoda PA, Ndiritu J, Ntuli C, Mwaka B (2010) Application of radial basis function neural networks to short-term streamflow forecasting. Phys Chem Earth Parts A/B/C 35(13–14):571–581. https://doi.org/10.1016/j.pce.2010.07.021
24. Khoshrou MI, Zarafshan P, Dehghani M, Chegini G, Arabhosseini A, Zakeri B (2021) Deep Learning Prediction of Chlorophyll Content in Tomato Leaves, Int. Conf. on Robotics and Mechatronics (ICRoM), pp. 580–585.
25. Kingma DP, Ba JL (2015) Adam: A Method for Stochastic Optimization Int Conf Learn Represent, pp. 1–13.
26. Kock Rasmussen E, Svenstrup Petersen O, Thompson JR, Flower RJ, Ahmed MH (2009) Hydrodynamic-ecological model analyses of the water quality of Lake Manzala (Nile Delta, Northern Egypt). Hydrobiologia 622(1):195–220. https://doi.org/10.1007/s10750-008-9683-7
27. Kumar D, Roshni T, Singh A, Jha MK, Samui P (2020) Predicting groundwater depth fluctuations using deep learning, extreme learning machine and Gaussian process: a comparative study. Earth Science Inform 13(4):1237–1250. https://doi.org/10.1007/s12145-020-00508-y
28. Lallahem S, Mania J, Hani A, Najjar Y (2005) On the use of neural networks to evaluate groundwater levels in fractured media. J of Hydrol 307(1–4):92–111
29. Li D, Huang F, Yan L, Cao Z, Chen J, Ye Z (2019) Landslide susceptibility prediction using particle-swarm-optimized multilayer perceptron: Comparisons with multilayer-perceptron-only, bp neural network, and information value models. Appl Sci 9(18):3664. https://doi.org/10.3390/app9183664
30. Malmir M, Javadi S, Moridi A, Neshat A, Razdar B (2021) A new combined framework for sustainable development using the DPSIR approach and numerical modeling. Geosci Front 12(4):101169. https://doi.org/10.1016/j.gsf.2021.101169
31. Mirzavand M, Khoshnevisan B, Shamshirband S, Kisi O, Ahmad R, Akib S (2015) Evaluating groundwater level fluctuation by Support vector regression and neuro-fuzzy methods: a comparative study. Nat Hazards 1(1):1–15
32. Moghaddam HK, Kivi ZR, Bahreinimotlagh M, Alizadeh MJ (2019) Developing comparative mathematic models, BN and ANN for forecasting of groundwater levels. Groundw Sustain Dev 9:1002–1037
33. 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. https://doi.org/10.1007/s11269-012-0239-2
34. Müller J, Park J, Sahu R, Varadharajan C, Arora B, Faybishenko B, Agarwal D (2021) Surrogate optimization of deep neural networks for groundwater predictions. J Glob Optim 81(1):203–231. https://doi.org/10.1007/s10898-020-00912-0
35. Najah A, Elshafie A, Karim OA, Jaffar O (2009) Prediction of Johor River water quality parameters using artificial neural networks. Eur J Res 28(3), pp. 422–435.
36. Nicholls RJ, Wong PP, Burkett V, Codignotto J, Hay J, McLean R, Ragoonaden S, Woodroffe CD, Abuodha PAO, Arblaster J, Brown B (2007) Coastal systems and low-lying areas.
37. Ouarda TBMJ, Shu C (2009) Regional low-flow frequency analysis using single and ensemble artificial neural networks. Water Resour Res 45(11). https://doi.org/10.1029/2008WR007196
38. Pham QB, Kumar M, Di Nunno F, Elbeltagi A, Granata F, Islam ARM, Talukdar S, Nguyen XC, Ahmed AN, Anh DT (2022) Groundwater level prediction using machine learning algorithms in a drought-prone area. Neural Comput Appl, pp. 1–23.
39. Raghavendra NS, Deka PC (2015) Multistep ahead groundwater level time-series forecasting using Gaussian Process Regression and ANFIS. In: Advanced Computing and Systems for Security (ACSS), pp 289–302
40. Rahmati O, Pourghasemi HR, Melesse AM (2016) Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region. Iran Catena 137:360–372
41. Rahmati O, Naghibi SA, Shahabi H, Bui DT, Pradhan B, Azareh A, Rafiei-Sardooi E, Samani AN, Melesse, AM (2018) Groundwater spring potential modelling: comprising the capability and robustness of three different modeling approaches. J Hydrol 565:248–261. https://doi.org/10.1016/j.jhydrol.2018.08.027
42. Safavi HR, Esmikhani M (2013) Conjunctive use of surface water and groundwater: application of support vector machines (SVMs) and genetic algorithms. Water Resour Manag 27(7):2623–2644. https://doi.org/10.1007/s11269-013-0307-2
43. Salari K, Zarafshan P, Khashehchi M, Pipelzadeh E, Chegini Gh (2022) Modeling and predicting of water production by capacitive deionization method using artificial neural networks, Desalination, 540.
44. Sattari MT, Mirabbasi R, Sushab RS, Abraham J (2018) Prediction of groundwater level in Ardebil plain using support vector regression and M5 tree model. Groundwater 56(4):636–646. https://doi.org/10.1111/gwat.12620
45. Sethi LN, Panda SN, Nayak MK (2006) Optimal crop planning and water resources allocation in a coastal groundwater basin, Orissa. India Agric Water Manag 83(3):209–220
46. 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. https://doi.org/10.1016/j.jhydrol.2020.125468
47. Shu C, Ouarda TBMJ (2008) Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. J of Hydrol 349(1–2):31–43
48. 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
49. Sussman TJ, Heller W, Miller GA, Mohanty A (2013) Emotional distractors can enhance attention. Psychol Sci 24(11):2322–2328
50. Tahmasebi P, Hezarkhani A (2012) A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Comput Geosci 42:18–27
51. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1:116–132
52. Talei A, Chua LHC, Wong TS (2010) Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling. J of Hydrol 391(3–4):248–262
53. Vapnik V (1995) The nature of statistical learning theory, 1st edn. Springer-Verlag, Berlin, Heidelberg
54. Vapnik V (2013) The nature of statistical learning theory, 1st edn. Springer science & business media
55. Yoon H, Hyun Y, Ha K, Lee KK, Kim GB (2016) A method to improve the stability and accuracy of ANN-and SVM-based time series models for long-term groundwater level predictions. Comput Geosci 90:144–155
56. Zarafshan P, Javadi S, Roozbahani A, Hashemy Shahdany SM, Zarafshan P, and Etezadi H (2021) Artificial Intelligence Hybrid Deep Learning Model for Groundwater Level Prediction Using MLP-ADAM. 20th Iranian Hydraulic Conference.
57. Zare M (2017) Application and Analysis of Physical and Data-driven Stochastic Hydrological Simulation-Optimization Methods for the Optimal Management of Surface-Groundwater Resources Systems: Iranian Cases Studies (Doctoral dissertation, Universitätsbibliothek Kassel).
58. Zhang N, Xiao C, Liu B, Liang X (2017) Groundwater depth predictions by GSM, RBF, and ANFIS models: a comparative assessment. Arab J Geosci 10(8):189
59. Zounemat-Kermani M, Mahdavi-Meymand A, Fadaee M, Batelaan O, Hinkelmann R (2022) Groundwater quality modeling: On the analogy between integrative PSO and MRFO mathematical and machine learning models. Environ Qual Manag 31(3):241–251. https://doi.org/10.1002/tqem.21775

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-a66e1c0d-0d3b-4b82-8fee-0cece4b6ee7d