Abstract
Excessive withdrawal of groundwater for agricultural irrigation can cause seawater intrusion into coastal aquifers. Such a case will in turn results in deterioration of irrigation water quality. Determination of irrigation water quality with traditional methods is a time-consuming and costly process. However, machine learning algorithms can be useful tools for modeling and estimating groundwater quality used for irrigation water purposes. In this study, TDS, PS, SAR, and Cl parameters of groundwater were estimated with models based on EC and pH variables. For this purpose, prediction performances of two different deep learning methods (convolutional neural network (CNN) and deep neural network (DNN)) and two different classical machine learning (Random Forest (RF) and extreme gradient boosting (XGBoost)) methods were compared. In addition, predictive uncertainty of the models was determined by quantile regression (QR) analysis. Performance criteria and results of uncertainty analysis revealed that CNN (in testing phase, NSE = 0.95 for TDS, NSE = 0.96 for PS, NSE = 0.67 for SAR and NSE = 0.93 for CI) and DNN (in testing phase, NSE = 0.91 for TDS, NSE = 0.91 for PS, NSE = 0.57 for SAR and NSE = 0.94 for Cl) models had quite a close performance in estimation of TDS, PS, SAR, and Cl parameters and higher than the other two classical machine learning methods. As a result, the CNN model can be considered the best performing model in estimating all quality parameters due to the highest NSE and lowest RMSE values. In addition, the Taylor diagram showed that the values estimated using the CNN model had the highest correlation with the measured data. It was determined that the model with the lowest uncertainty based on the PICP statistics was DNN, followed by the CNN model. However, the CNN model has predicted outliers more accurately. Present findings proved that deep learning models could offer efficient tools for predicting irrigation water quality parameters.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Not applicable.
References
Afzaal H, Farooque AA, Abbas F, Acharya B, Esau T (2019) Groundwater estimation from major physical hydrology components using artificial neural networks and deep learning. Water 12(1):5. https://doi.org/10.3390/w12010005
Al Naeem MFA, Yusoff I, Ng TF, Maity JP, Alias Y, May R, Alborsh H (2019) A study on the impact of anthropogenic and geogenic factors on groundwater salinization and seawater intrusion in Gaza coastal aquifer, Palestine: An integrated multi-techniques approach. J Afr Earth Sci 156:75–93. https://doi.org/10.1016/j.jafrearsci.2019.05.006
APHA (2005) Standard methods for the examination of water and waste water, 21st edn. American Public Health Association, Washington, DC
Arminger G, Enache D (1996) Statistical models and artificial neural networks. In: Bock H, Polasek W (eds) Data analysis and information systems. Springer, Heidelberg, pp 243–260 https://doi.org/10.1007/978-3-642-80098-6_21
Arslan H, Demir Y (2013) Impacts of seawater intrusion on soil salinity and alkalinity in Bafra Plain. Turkey Environ Monit Assess 185(2):1027–1040. https://doi.org/10.1007/s10661-012-2611-3
Arslan H, Cemek B, Demir Y (2012) Determination of seawater intrusion via hydrochemicals and isotopes in Bafra Plain. Turkey Water Resour Manag 26(13):3907–3922. https://doi.org/10.1007/s11269-012-0112-3
Ayers RS, Westcot DW (1994) Water quality for agriculture: FAO Irrigation and Drainage Paper 29. Revision 1:1–130
Barzegar R, Aalami MT, Adamowski J (2020) Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model. Stoch Environ Res Risk Assess 34(2):415–433. https://doi.org/10.1007/s00477-020-01776-2
Bedi S, Samal A, Ray C, Snow D (2020) Comparative evaluation of machine learning models for groundwater quality assessment. Environ Monit Assess 192(12):1–23. https://doi.org/10.1007/s10661-020-08695-3
Brédy J, Gallichand J, Celicourt P, Gumiere SJ (2020) Water table depth forecasting in cranberry fields using two decision-tree-modeling approaches. Agric Water Manag 233:106090. https://doi.org/10.1016/j.agwat.2020.106090
Breiman L (2001) Random Forests. Mach Learn 45(1):5–32
Bui DT, Khosravi K, Tiefenbacher J, Nguyen H, Kazakis N (2020) Improving prediction of water quality indices using novel hybrid machine-learning algorithms. Sci Total Environ 721:137612. https://doi.org/10.1016/j.scitotenv.2020.137612
Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of KDD 2016 – The 22nd ACM SIGKDD International conference on knowledge discovery and data mining. 13 - 17 August 2016, San Francisco California (USA), pp 785–794. https://doi.org/10.1145/2939672.2939785
Chollet F, Allaire JJ (2018) Deep Learning with R Manning Publications Co. Shelter Island, NY
Dechter R (1986) Learning while searching in constraint-satisfaction problems. AAAI-86 Proceedings, Palo Alto, pp 178–183.
DeSimone LA, Pope JP, Ransom KM (2020) Machine-learning models to map pH and redox conditions in groundwater in a layered aquifer system, Northern Atlantic Coastal Plain, eastern USA. J Hydrol Reg Stud 30:100697. https://doi.org/10.1016/j.ejrh.2020.100697
Doneen LD (1964) Notes on water quality in agriculture. Published as a water science and engineering paper 4001, Department of Water Science and Engineering, University of California, Davis
El Bilali A, Taleb A, Brouziyne Y (2021a) Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agric Water Manag 245:106625. https://doi.org/10.1016/j.agwat.2020.106625
El Bilali A, Taleb A, Nafii A, Alabjah B, Mazigh N (2021b) Prediction of sodium adsorption ratio and chloride concentration in a coastal aquifer under seawater intrusion using machine learning models. Environ Technol Innov 23:101641. https://doi.org/10.1016/j.eti.2021.101641
Fan J, Zheng J, Wu L, Zhang F (2021) Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models. Agric Water Manag 245:106547. https://doi.org/10.1016/j.agwat.2020.106547
Gebrehiwot A, Hashemi-Beni L, Thompson G, Kordjamshidi P, Langan TE (2019) Deep convolutional neural network for flood extent map** using unmanned aerial vehicles data. Sensors 19(7):1486. https://doi.org/10.3390/s19071486
Gholami V, Khaleghi MR, Pirasteh S, Booij MJ (2022) Comparison of self-organizing map, artificial neural network, and co-active neuro-fuzzy inference system methods in simulating groundwater quality: geospatial artificial intelligence. Water Resour Manag 36(2):451–469. https://doi.org/10.1007/s11269-021-02969-2
Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J (2019) Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens 11(2):196. https://doi.org/10.3390/rs11020196
Hengl T, Nussbaum M, Wright MN, Heuvelink GB, Gräler B (2018) Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ 6:e5518. https://doi.org/10.7717/peerj.5518
Jiang Y, Li C, Sun L, Guo D, Zhang Y, Wang W (2021) A deep learning algorithm for multi-source data fusion to predict water quality of urban sewer networks. J Clean Prod 318:128533. https://doi.org/10.1016/j.jclepro.2021.128533
Kazemi MH, Shiri J, Marti P, Majnooni-Heris A (2020) Assessing temporal data partitioning scenarios for estimating reference evapotranspiration with machine learning techniques in arid regions. J Hydrol 590:125252. https://doi.org/10.1016/j.jhydrol.2020.125252
Khanal S, Fulton J, Klopfenstein A, Douridas N, Shearer S (2018) Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield. Comput Electron Agric 153:213–225. https://doi.org/10.1016/j.compag.2018.07.016
Koenker R, Bassett G Jr (1978) Regression quantiles. Econometrica: J Econ Soc 46(1):33–50. https://doi.org/10.2307/1913643
Kouadri S, Pande CB, Panneerselvam B, Moharir KN, Elbeltagi A (2022) Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models. Environ Sci Pollut Res 29(14):21067–21091. https://doi.org/10.1007/s11356-021-17084-3
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I-A discussion of principles. J Hydrol 10(3):282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Ongley ED (2000) Water quality management: design, financing and sustainability considerations-II. In Invited presentation at the World Bank’s Water Week Conference: Towards a strategy for managing water quality management, April 3-4, 2000, Washington, D.C. USA
Panahi M, Sadhasivam N, Pourghasemi HR, Rezaie F, Lee S (2020) Spatial prediction of groundwater potential map** based on convolutional neural network (CNN) and support vector regression (SVR). J Hydrol 588:125033. https://doi.org/10.1016/j.jhydrol.2020.125033
Raheja H, Goel A, Pal M (2022) Prediction of groundwater quality indices using machine learning algorithms. Water Pract Technol 17(1):336–351. https://doi.org/10.2166/wpt.2021.120
Rahmati O, Choubin B, Fathabadi A, Coulon F, Soltani E, Shahabi H, Bui DT (2019) Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods. Sci Total Environ 688:855–866. https://doi.org/10.1016/j.scitotenv.2019.06.320
Ranjan P, Kazama S, Sawamoto M (2006) Effects of climate change on coastal fresh groundwater resources. Glob Environ Change 16(4):388–399. https://doi.org/10.1016/j.gloenvcha.2006.03.006
Sahour H, Gholami V, Vazifedan M (2020) A comparative analysis of statistical and machine learning techniques for map** the spatial distribution of groundwater salinity in a coastal aquifer. J Hydrol 591:125321. https://doi.org/10.1016/j.jhydrol.2020.125321
Saygın F, Dengiz O (2013) Classification and determination of different soils’ distribution on Fener village and its near vicinity located in left side of Bafra Plain. Soil Water Journal 2(2):63–72. https://dergipark.org.tr/en/pub/topraksu/issue/21415/229539. Accessed 04 Aug 2022
Sharafati A, Pezeshki E (2020) A strategy to assess the uncertainty of a climate change impact on extreme hydrological events in the semi-arid Dehbar catchment in Iran. Theor Appl Climatol 139(1):389–402. https://doi.org/10.1007/s00704-019-02979-6
Shrestha DL, Solomatine DP (2006) Machine learning approaches for estimation of prediction interval for the model output. Neural Netw 19(2):225–235. https://doi.org/10.1016/j.neunet.2006.01.012
Singha S, Pasupuleti S, Singha SS, Singh R, Kumar S (2021) Prediction of groundwater quality using efficient machine learning technique. Chemosphere 276:130265. https://doi.org/10.1016/j.chemosphere.2021.130265
Solomatine DP, Shrestha DL (2009) A novel method to estimate model uncertainty using machine learning techniques. Water Resour. Res., 45(12):W00B11. https://doi.org/10.1029/2008WR006839
Sorensen DL, McCarthy M, Middlebrooks EJ, Porcella DB (1977) “Suspended and dissolved solids effects on freshwater biota: A review”. US Environmental Protection Agency, EPA-600/3–77–042
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106(D7):7183–7192. https://doi.org/10.1029/2000JD900719
Trabelsi F, Bel Hadj Ali S (2022) Exploring Machine Learning Models in Predicting Irrigation Groundwater Quality Indices for Effective Decision Making in Medjerda River Basin. Tunisia Sustainability 14(4):2341. https://doi.org/10.3390/su14042341
Wang L, Long F, Liao W, Liu H (2020) Prediction of anaerobic digestion performance and identification of critical operational parameters using machine learning algorithms. Bioresour Technol 298:122495. https://doi.org/10.1016/j.biortech.2019.122495
Wang Z, Wu X, Wang H, Wu T (2021) Prediction and analysis of domestic water consumption based on optimized grey and Markov model. Water Supply 21(7):3887–3899. https://doi.org/10.2166/ws.2021.146
Weerts AH, Winsemius HC, Verkade JS (2011) Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales). Hydrol Earth Syst Sci 15(1):255–265. https://doi.org/10.5194/hess-15-255-2011
Wu J, Wang Z (2022) A hybrid model for water quality prediction based on an artificial neural network, wavelet transform, and long short-term memory. Water 14(4):610. https://doi.org/10.3390/w14040610
Yu H, Wen X, Wu M, Sheng D, Wu J, Zhao Y (2022a) Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture. Agric Water Manag 262:107423. https://doi.org/10.1016/j.agwat.2021.107423
Yu JW, Kim JS, Li X, Jong YC, Kim KH, Ryang GI (2022b) Water quality forecasting based on data decomposition, fuzzy clustering and deep learning neural network. Environ Pollut 303:119136. https://doi.org/10.1016/j.envpol.2022.119136
Zhang J, Li D, Wang Y (2020) Toward intelligent construction: Prediction of mechanical properties of manufactured-sand concrete using tree-based models. J Clean Prod 258:120665. https://doi.org/10.1016/j.jclepro.2020.120665
Zuo R, **. Earth-Sci Rev 192:1–14. https://doi.org/10.1016/j.earscirev.2019.02.023
Funding
This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) (Grant No. 214O706).
Author information
Authors and Affiliations
Contributions
Mehmet Taşan: sampling, analyses, methodology, writing – original draft, writing – review & editing. Sevda Taşan: methodology, writing – original draft, writing – review & editing. Yusuf Demir: conceptualization, project administration, funding acquisition.
Corresponding author
Ethics declarations
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent to publish
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible editor: **anliang Yi
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Taşan, M., Taşan, S. & Demir, Y. Estimation and uncertainty analysis of groundwater quality parameters in a coastal aquifer under seawater intrusion: a comparative study of deep learning and classic machine learning methods. Environ Sci Pollut Res 30, 2866–2890 (2023). https://doi.org/10.1007/s11356-022-22375-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-022-22375-4