Skip to main content
SpringerLink
Log in

Predicting Soil Moisture Content Based on Laser-Induced Breakdown Spectroscopy-Informed Machine Learning

  • Research Article-Physics
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

This study presents a pioneering approach that combines artificial intelligence and laser-induced breakdown spectroscopy (LIBS) to predict soil moisture content (MC). The traditional laboratory-based method of MC measurement, involving soil weight comparison before and after heating, is time-consuming, labor-intensive, and prone to low accuracy. In this work, we propose a non-destructive soil MC measurement technique utilizing robust nonlinear models based on LIBS-derived elemental intensities. Support vector regression (SVR) and AdaBoost-based SVR models (SVR-ADB), employing Gaussian Kernel and input features from LIBS data, were employed for MC prediction. Model performance was assessed using standard metrics such as root mean square error, mean absolute error, Nash–Sutcliffe efficiency (NSE), and correlation coefficient (CC) between predicted and actual moisture content. The study employed 485 datapoints generated in our laboratory. An advanced feature optimization technique based on the correlation between the soil MC and the descriptors was employed to select relevant mineral elements as input features. Three feature combinations (Combo-1, Combo-2, and Combo-3) were evaluated to identify the most effective configurations for accurate soil MC predictions. SVR-ADB-3 (Combo-3) demonstrated the highest prediction efficiency in the testing phase, achieving an impressive CC of 0.9998 and NSE of 0.9997. Consistently, Combo-3 outperformed other configurations, emphasizing the importance of the selected features. Validation of the developed models on soils treated with cement and lime stabilizers, whose data were not used during model calibration and verification, confirmed the generalization capability of the models. This study provides valuable insights for policymakers and industry stakeholders, facilitating optimized soil moisture management practices.

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

Similar content being viewed by others

Abbreviations

SVR:

Support vector regression

ADB:

AdaBoost

CC:

Correlation coefficient

RMSE:

Root mean square error

MAE:

Mean absolute error

NSE:

Nash–Sutcliffe coefficient efficiency

MC:

Moisture content

Combo:

Optimal combination

SD:

Standard deviation

IoT:

Internet of things

References

  1. Zajícová, K.; Chuman, T.: Application of ground penetrating radar methods in soil studies: a review. Geoderma 343, 116–129 (2019). https://doi.org/10.1016/J.GEODERMA.2019.02.024

    Article  ADS  Google Scholar 

  2. Kim, G.; Yoon, Y.J.; Kim, H.A.; Joocho, H.; Park, K.: Elemental composition of Arctic soils and aerosols in Ny-Ålesund measured using laser-induced breakdown spectroscopy. Spectrochim. Acta Part B At. Spectrosc. 134, 17–24 (2017). https://doi.org/10.1016/J.SAB.2017.06.006

    Article  CAS  ADS  Google Scholar 

  3. Al-Najjar, O.A.; Wudil, Y.S.; Ahmad, U.F.; Al-Amoudi, O.S.B.; Al-Osta, M.A.; Gondal, M.A.: Applications of laser induced breakdown spectroscopy in geotechnical engineering: a critical review of recent developments, perspectives and challenges. Appl. Spectrosc. Rev. 21, 1–37 (2022). https://doi.org/10.1080/05704928.2022.2136192

    Article  CAS  Google Scholar 

  4. Wang, C.; Li, Z.; Cai, B.; Tan, Q.; Li, Y.; He, L.; Tang, Q.; Huang, W.; Duan, X.; Deng, Y.: Effect of root system of the Dicranopteris dichotoma on the soil unconfined compressive strength of collapsing walls in hilly granite area of South China. Catena (Amst) 216, 106411 (2022). https://doi.org/10.1016/J.CATENA.2022.106411

    Article  Google Scholar 

  5. Khumaeni, A.; Budi, W.S.; Hedwig, R.; Gondal, M.A.; Kurniawan, K.H.: Signal intensity augmentation of elements detected in blood serum using dual pulse laser induced plasma spectroscopy under ambient he gas environment. Arab. J. Sci. Eng. 2023, 1–12 (2023). https://doi.org/10.1007/S13369-023-08447-8/METRICS

    Article  Google Scholar 

  6. Rehan, I.; Gondal, M.A.; Sultana, S.; Dastageer, M.A.; Aldakheel, R.K.; Almessiere, M.A.; Muhammad, R.; Rehan, K.; Domyati, D.: Elemental compositions of earthquake-stricken soil from the vicinity of the Epicenter at Eurasian and Indian tectonic plates using calibration free laser induced breakdown spectroscopy. Arab. J. Sci. Eng. 46, 6101–6108 (2021). https://doi.org/10.1007/S13369-021-05503-Z/FIGURES/7

    Article  Google Scholar 

  7. Rehan, I.; Khan, S.; Gondal, M.A.; Abbas, Q.; Ullah, R.: Non-invasive diabetes mellitus diagnostics using laser-induced breakdown spectroscopy and support vector machine algorithm. Arab. J. Sci. Eng. 2023, 1–9 (2023). https://doi.org/10.1007/S13369-023-08269-8/METRICS

    Article  Google Scholar 

  8. Awan, M.A.; Ahmed, S.H.; Aslam, M.R.; Qazi, I.A.; Baig, M.A.: Determination of heavmetals in ambient air particulate matter using laser-induced breakdown spectroscopy. Arab. J. Sci. Eng. 38, 1655–1661 (2013). https://doi.org/10.1007/S13369-013-0548-7/METRICS

    Article  CAS  Google Scholar 

  9. Zhang, Y.; Zhang, T.; Li, H.: Application of laser-induced breakdown spectroscopy (LIBS) in environmental monitoring. Spectrochim. Acta Part B At. Spectrosc. 181, 106218 (2021)

    Article  CAS  Google Scholar 

  10. Gondal, M.A.; Aldakheel, R.K.; Almessiere, M.A.; Nasr, M.M.; Almusairii, J.A.; Gondal, B.: Determination of heavy metals in cancerous and healthy colon tissues using laser induced breakdown spectroscopy and its cross-validation with ICP-AES method. J. Pharm. Biomed. Anal. 183, 113153 (2020). https://doi.org/10.1016/J.JPBA.2020.113153

    Article  CAS  PubMed  Google Scholar 

  11. Gondal, M.A.; Habibullah, Y.B.; Baig, U.; Oloore, L.E.: Direct spectral analysis of tea samples using 266 nm UV pulsed laser-induced breakdown spectroscopy and cross validation of LIBS results with ICP-MS. Talanta 152, 341–352 (2016). https://doi.org/10.1016/J.TALANTA.2016.02.030

    Article  CAS  PubMed  Google Scholar 

  12. Khoso, M.A.; Shaikh, N.M.; Kalhoro, M.S.; Jamali, S.; Ujan, Z.A.; Ali, R.: Comparative elemental analysis of soil of wheat, corn, rice, and okra cropped field using CF-LIBS. Optik (Stuttg) 261, 169247 (2022). https://doi.org/10.1016/j.ijleo.2022.169247

    Article  CAS  ADS  Google Scholar 

  13. Völker, T.; Millar, S.; Strangfeld, C.; Wilsch, G.: Identification of type of cement through laser-induced breakdown spectroscopy. Constr. Build. Mater. 258, 120345 (2020). https://doi.org/10.1016/J.CONBUILDMAT.2020.120345

    Article  Google Scholar 

  14. Al-Adel, F.F.; Dastageer, M.A.; Gasmi, K.; Gondal, M.A.: Optimization of a laser induced breakdown spectroscopy method for the analysis of liquid samples. J. Appl. Spectrosc. 80, 767–770 (2013). https://doi.org/10.1007/S10812-013-9839-8

    Article  CAS  ADS  Google Scholar 

  15. Wudil, Y.S.; Gondal, M.A.; Rao, S.G.; Kunwar, S.; Alsayoud, A.Q.: Substrate temperature-dependent thermoelectric figure of merit of nanocrystalline Bi2Te3 and Bi2Te2.7Se0.3 prepared using pulsed laser deposition supported by DFT study. Ceram. Int. 46, 24162–24172 (2020). https://doi.org/10.1016/j.ceramint.2020.06.196

    Article  CAS  Google Scholar 

  16. Wudil, Y.S.; Gondal, M.A.; Rao, S.G.; Kunwar, S.; Alsayoud, A.Q.: Improved thermoelectric performance of ternary Cu/Ni/Bi2Te2.7Se0.3 nanocomposite prepared by pulsed laser deposition. Mater. Chem. Phys. 21, 123321 (2020). https://doi.org/10.1016/j.matchemphys.2020.123321

    Article  CAS  Google Scholar 

  17. Wudil, Y.S.S.; Gondal, M.A.A.; Rao, S.G.G.; Kunwar, S.: Thermal conductivity of PLD-grown thermoelectric Bi2Te2.7Se0.3 films using temperature-dependent Raman spectroscopy technique. Ceram. Int. 1, 21 (2019). https://doi.org/10.1016/j.ceramint.2019.11.219

    Article  CAS  Google Scholar 

  18. Sobral, H.; Sanginés, R.; Trujillo-Vázquez, A.: Detection of trace elements in ice and water by laser-induced breakdown spectroscopy. Spectrochim. Acta Part B At. Spectrosc. 78, 62–66 (2012). https://doi.org/10.1016/J.SAB.2012.09.005

    Article  CAS  ADS  Google Scholar 

  19. Babos, D.V.; Barros, A.I.; Nóbrega, J.A.; Pereira-Filho, E.R.: Calibration strategies to overcome matrix effects in laser-induced breakdown spectroscopy: direct calcium and phosphorus determination in solid mineral supplements. Spectrochim. Acta Part B At. Spectrosc. 155, 90–98 (2019). https://doi.org/10.1016/j.sab.2019.03.010

    Article  CAS  ADS  Google Scholar 

  20. Hou, D.; Bolan, N.S.; Tsang, D.C.W.; Kirkham, M.B.; O’Connor, D.: Sustainable soil use and management: an interdisciplinary and systematic approach. Sci. Total. Environ. 729, 138961 (2020). https://doi.org/10.1016/J.SCITOTENV.2020.138961

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  21. Hurraß, J.; Schaumann, G.E.: Properties of soil organic matter and aqueous extracts of actually water repellent and wettable soil samples. Geoderma 132, 222–239 (2006). https://doi.org/10.1016/J.GEODERMA.2005.05.012

    Article  ADS  Google Scholar 

  22. Kim, G.; Yoon, Y.-J.; Kim, H.-A.; Cho, H.; Park, K.: Elemental composition of Arctic soils and aerosols in Ny-Ålesund measured using laser-induced breakdown spectroscopy. Spectrochim. Acta Part B At. Spectrosc. 134, 17–24 (2017). https://doi.org/10.1016/j.sab.2017.06.006

    Article  CAS  ADS  Google Scholar 

  23. Gondal, M.A.; Dastageer, A.; Maslehuddin, M.; Alnehmi, A.J.; Al-Amoudi, O.S.B.: Detection of sulfur in the reinforced concrete structures using a dual pulsed LIBS system. Opt. Laser Technol. 44, 566–571 (2012). https://doi.org/10.1016/J.OPTLASTEC.2011.09.001

    Article  CAS  ADS  Google Scholar 

  24. Senesi, G.S.; Dell’Aglio, M.; Gaudiuso, R.; De Giacomo, A.; Zaccone, C.; De Pascale, O.; Miano, T.M.; Capitelli, M.: Heavy metal concentrations in soils as determined by laser-induced breakdown spectroscopy (LIBS), with special emphasis on chromium. Environ. Res. 109, 413–420 (2009). https://doi.org/10.1016/J.ENVRES.2009.02.005

    Article  CAS  PubMed  Google Scholar 

  25. He, M.; Tang, L.; Li, C.; Ren, J.; Zhang, L.; Li, X.: Dynamics of soil organic carbon and nitrogen and their relations to hydrothermal variability in dryland. J. Environ. Manag. 319, 115751 (2022). https://doi.org/10.1016/J.JENVMAN.2022.115751

    Article  CAS  Google Scholar 

  26. Xu, X.; Ma, F.; Zhou, J.; Du, C.: Applying convolutional neural networks (CNN) for end-to-end soil analysis based on laser-induced breakdown spectroscopy (LIBS) with less spectral preprocessing. Comput. Electron. Agric. 199, 107171 (2022). https://doi.org/10.1016/j.compag.2022.107171

    Article  Google Scholar 

  27. Akinpelu, A.A.; Ali, Md.E.; Owolabi, T.O.; Johan, M.R.; Saidur, R.; Olatunji, S.O.; Chowdbury, Z.: A support vector regression model for the prediction of total polyaromatic hydrocarbons in soil: an artificial intelligent system for mapping environmental pollution. Neural Comput. Appl. 32(18), 14899–14908 (2020). https://doi.org/10.1007/S00521-020-04845-3

    Article  Google Scholar 

  28. Alrebdi, T.A.; Wudil, Y.S.; Ahmad, U.F.; Yakasai, F.A.; Mohammed, J.; Kallas, F.H.: Predicting the thermal conductivity of Bi2Te3-based thermoelectric energy materials: a machine learning approach. Int. J. Therm. Sci. 181, 107784 (2022). https://doi.org/10.1016/J.IJTHERMALSCI.2022.107784

    Article  CAS  Google Scholar 

  29. Ma, J.; Xia, D.; Guo, H.; Wang, Y.; Niu, X.; Liu, Z.; Jiang, S.: Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study. Landslides 19, 2489–2511 (2022). https://doi.org/10.1007/S10346-022-01923-6/TABLES/9

    Article  Google Scholar 

  30. Song, H.; Ahmad, A.; Farooq, F.; Ostrowski, K.A.; Maślak, M.; Czarnecki, S.; Aslam, F.: Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Constr. Build. Mater. 308, 125021 (2021). https://doi.org/10.1016/J.CONBUILDMAT.2021.125021

    Article  CAS  Google Scholar 

  31. Zhang, L.V.; Marani, A.; Nehdi, M.L.: Chemistry-informed machine learning prediction of compressive strength for alkali-activated materials. Constr. Build. Mater. 316, 126103 (2022). https://doi.org/10.1016/J.CONBUILDMAT.2021.126103

    Article  CAS  Google Scholar 

  32. Zhu, G.; Wen, T.; Zhang, D.: Machine learning based approach for the prediction of flow boiling/condensation heat transfer performance in mini channels with serrated fins. Int. J. Heat Mass Transf. 166, 120783 (2021). https://doi.org/10.1016/j.ijheatmasstransfer.2020.120783

    Article  Google Scholar 

  33. Pan, Z.; Zhou, Y.; Zhang, L.: Photoelectrochemical properties, machine learning, and symbolic regression for molecularly engineered halide perovskite materials in water. ACS Appl. Mater. Interfaces 14, 9933–9943 (2022). https://doi.org/10.1021/ACSAMI.2C00568/ASSET/IMAGES/LARGE/AM2C00568_0007.JPEG

    Article  CAS  PubMed  Google Scholar 

  34. Souiyah, M.; Owolabi, T.O.; Saliu, S.; Qahtan, T.F.; Aldhafferi, N.; Alqahtani, A.: Specific surface area characterization of spinel ferrite nanostructure based compounds for photocatalysis and other applications using extreme learning machine method. Math. Probl. Eng. (2022). https://doi.org/10.1155/2022/1259131

    Article  Google Scholar 

  35. Cakiroglu, C.; Demir, S.; Hakan Ozdemir, M.; Latif Aylak, B.; Sariisik, G.; Abualigah, L.: Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis. Expert Syst. Appl. 237, 121464 (2024). https://doi.org/10.1016/J.ESWA.2023.121464

    Article  Google Scholar 

  36. Liu, T.; Cakiroglu, C.; Islam, K.; Wang, Z.; Nehdi, M.L.: Explainable machine learning model for predicting punching shear strength of FRC flat slabs. Eng. Struct. 301, 117276 (2024). https://doi.org/10.1016/J.ENGSTRUCT.2023.117276

    Article  Google Scholar 

  37. Zhang, W.; Gu, X.; Tang, L.; Yin, Y.; Liu, D.; Zhang, Y.: Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: COMPREHENSIVE review and future challenge. Gondwana Res. 109, 1–17 (2022). https://doi.org/10.1016/J.GR.2022.03.015

    Article  ADS  Google Scholar 

  38. Zhang, W.; Wu, C.; Zhong, H.; Li, Y.; Wang, L.: Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geosci. Front. 12, 469–477 (2021). https://doi.org/10.1016/J.GSF.2020.03.007

    Article  ADS  Google Scholar 

  39. Khan, A.Q.; Naveed, M.H.; Rasheed, M.D.; Miao, P.: Prediction of compressive strength of fly ash-based geopolymer concrete using supervised machine learning methods. Arab. J. Sci. Eng. 2023, 1–16 (2023). https://doi.org/10.1007/S13369-023-08283-W/METRICS

    Article  Google Scholar 

  40. Ngo, N.T.; Pham, A.D.; Truong, T.T.H.; Truong, N.S.; Huynh, N.T.; Pham, T.M.: An ensemble machine learning model for enhancing the prediction accuracy of energy consumption in buildings. Arab. J. Sci. Eng. 47, 4105–4117 (2022). https://doi.org/10.1007/S13369-021-05927-7/METRICS

    Article  Google Scholar 

  41. Torabi-Kaveh, M.; Sarshari, B.: Predicting convergence rate of Namaklan twin tunnels using machine learning methods. Arab. J. Sci. Eng. 45, 3761–3780 (2020). https://doi.org/10.1007/S13369-019-04239-1/FIGURES/16

    Article  Google Scholar 

  42. Hazir, E.; Ozcan, T.; Koç, K.H.: Prediction of adhesion strength using extreme learning machine and support vector regression optimized with genetic algorithm. Arab. J. Sci. Eng. 45, 6985–7004 (2020). https://doi.org/10.1007/S13369-020-04625-0/TABLES/12

    Article  Google Scholar 

  43. Wudil, Y.S.: Ensemble learning-based investigation of thermal conductivity of Bi2Te2.7Se0.3-based thermoelectric clean energy materials. Results Eng. 18, 101203 (2023). https://doi.org/10.1016/J.RINENG.2023.101203

    Article  CAS  Google Scholar 

  44. Wudil, Y.S.; Al-Najjar, O.A.; Al-Osta, M.A.; Baghabra Al-Amoudi, O.S.; Gondal, M.A.: Investigating the soil unconfined compressive strength based on laser-induced breakdown spectroscopy emission intensities and machine learning techniques. ACS Omega (2023). https://doi.org/10.1021/ACSOMEGA.3C02514/ASSET/IMAGES/LARGE/AO3C02514_0014.JPEG

    Article  PubMed  PubMed Central  Google Scholar 

  45. Al-Najjar, O.A.; Wudil, Y.S.; Al-Osta, M.A.; Imam, A.; Al-Amoudi, O.S.B.; Gondal, M.A.: Laser-induced breakdown spectroscopy-based assessment of unconfined compressive strength of normal and chemically stabilized soils. Arab. J. Sci. Eng. 2023, 1–15 (2023). https://doi.org/10.1007/S13369-023-08055-6/METRICS

    Article  Google Scholar 

  46. Boucher, T.F.; Ozanne, M.V.; Carmosino, M.L.; Dyar, M.D.; Mahadevan, S.; Breves, E.A.; Lepore, K.H.; Clegg, S.M.: A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy. Spectrochim. Acta Part B At. Spectrosc.Spectrosc. 107, 1–10 (2015). https://doi.org/10.1016/J.SAB.2015.02.003

    Article  CAS  ADS  Google Scholar 

  47. Fabianpedregosa, F.P.; Michel, V.; Oliviergrisel, O.G.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Vanderplas, J.; Cournapeau, D.; Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Thirion, B.; Grisel, O.; Dubourg, V.; Passos, A.; Brucher, M.; Perrot, M.; Duchesnay, É.; Edouardduchesnay, F.D.: Scikit-learn: machine learning in python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL Matthieu Perrot. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  Google Scholar 

  48. Alade, I.O.; Abd Rahman, M.A.; Saleh, T.A.: Modeling and prediction of the specific heat capacity of Al2O3/water nanofluids using hybrid genetic algorithm/support vector regression model. Nano-Struct. Nano Obj. 17, 103–111 (2019). https://doi.org/10.1016/j.nanoso.2018.12.001

    Article  CAS  Google Scholar 

  49. Olatunji, S.O.; Owolabi, T.O.: Modeling superconducting transition temperature of doped MgB2 superconductor from structural distortion and ambient temperature resistivity measurement using hybrid intelligent approach. Comput. Mater. Sci. 192, 110392 (2021). https://doi.org/10.1016/J.COMMATSCI.2021.110392

    Article  CAS  Google Scholar 

  50. Alade, I.O.; Oyehan, T.A.; Popoola, I.K.; Olatunji, S.O.; Bagudu, A.: Modeling thermal conductivity enhancement of metal and metallic oxide nanofluids using support vector regression. Adv. Powder Technol. 29, 157–167 (2018). https://doi.org/10.1016/j.apt.2017.10.023

    Article  CAS  Google Scholar 

  51. Wudil, Y.S.; Imam, A.; Gondal, M.A.; Ahmad, U.F.; Al-Osta, M.A.: Application of machine learning regressors in estimating the thermoelectric performance of Bi2Te3-based materials. Sens. Actuators A Phys. 351, 114193 (2023). https://doi.org/10.1016/J.SNA.2023.114193

    Article  CAS  Google Scholar 

  52. Mustafa, Y.M.H.; Zami, M.S.; Al-Amoudi, O.S.B.; Al-Osta, M.A.; Wudil, Y.S.: Analysis of unconfined compressive strength of rammed earth mixes based on artificial neural network and statistical analysis. Materials 15, 9029 (2022). https://doi.org/10.3390/MA15249029

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  53. Mishra, S.; Mishra, D.; Santra, G.H.: Adaptive boosting of weak regressors for forecasting of crop production considering climatic variability: an empirical assessment. J. King Saud Univ. Comput. Inf. Sci. 32, 949–964 (2020). https://doi.org/10.1016/J.JKSUCI.2017.12.004

    Article  Google Scholar 

  54. Lin, N.; Jiang, R.; Li, G.; Yang, Q.; Li, D.; Yang, X.: Estimating the heavy metal contents in farmland soil from hyperspectral images based on Stacked AdaBoost ensemble learning. Ecol. Indic. 143, 109330 (2022). https://doi.org/10.1016/J.ECOLIND.2022.109330

    Article  CAS  Google Scholar 

  55. Sun, W.; Q.G.-J. of C. Production, undefined 2019, Exploration of energy saving potential in China power industry based on Adaboost back propagation neural network, Elsevier (n.d.)

  56. Rehan, I.; Gondal, M.A.; Almessiere, M.A.; Dakheel, R.A.; Rehan, K.; Sultana, S.; Dastageer, M.A.: Nutritional and toxic elemental analysis of dry fruits using laser induced breakdown spectroscopy (LIBS) and inductively coupled plasma atomic emission spectrometry (ICP-AES). Saudi J. Biol. Sci. 28, 408–416 (2021)

    Article  CAS  PubMed  Google Scholar 

  57. Feng, T.; Chen, T.; Li, M.; Chi, J.; Tang, H.; Zhang, T.; Li, H.: Discrimination of the pollution grade of metal elements in atmospherically deposited particulate matter via laser-induced breakdown spectroscopy combined with machine learning method. Chemom. Intell. Lab. Syst. 231, 104691 (2022). https://doi.org/10.1016/J.CHEMOLAB.2022.104691

    Article  CAS  Google Scholar 

  58. Bi, Y.; Zhang, Y.; Yan, J.; Wu, Z.; Li, Y.: Classification and discrimination of minerals using laser induced breakdown spectroscopy and Raman spectroscopy. Plasma Sci. Technol. 17, 923–927 (2015). https://doi.org/10.1088/1009-0630/17/11/06

    Article  CAS  ADS  Google Scholar 

  59. Barman, D.; Dash, S.K.: Stabilization of expansive soils using chemical additives: a review. J. Rock Mech. Geotech. Eng. 14, 1319–1342 (2022). https://doi.org/10.1016/J.JRMGE.2022.02.011

    Article  Google Scholar 

  60. Abba, S.I.; Benaafi, M.; Aljundi, I.H.: Intelligent process optimisation based on cutting-edge emotional learning for performance evaluation of NF/RO of seawater desalination plant. Desalination 550, 116376 (2023). https://doi.org/10.1016/J.DESAL.2023.116376

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We acknowledge the support of KFUPM-DROC under Project No. INCB2310. The support of the Physics and Civil Engineering Departments is also appreciated.

Author information

Authors and Affiliations

  1. Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia

    Y. S. Wudil & Mohammed A. Al-Osta

  2. Laser Research Group, Physics Department, King Fahd University of Petroleum and Minerals, Mailbox 5047, 31261, Dhahran, Saudi Arabia

    Y. S. Wudil, M. A. Gondal & S. Kunwar

  3. Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Eastern Province, Saudi Arabia

    Mohammed A. Al-Osta

  4. K.A.CARE Energy Research and Innovation Center, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia

    M. A. Gondal

Authors
  1. Y. S. Wudil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammed A. Al-Osta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. A. Gondal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Kunwar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Y. S. Wudil or M. A. Gondal.

Ethics declarations

Conflict interest

No conflicts to declare.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) 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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wudil, Y.S., Al-Osta, M.A., Gondal, M.A. et al. Predicting Soil Moisture Content Based on Laser-Induced Breakdown Spectroscopy-Informed Machine Learning. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-08762-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13369-024-08762-8

Keywords

Search

Navigation