Skip to main content
SpringerLink
Log in

Data mining predictive algorithms for estimating soil water content

  • Application of soft computing
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Soil water content (SWC) plays a key role in the management of water and soil resources. Accurate prediction of SWC is an important issue in water and soil studies. Recently, some data mining and machine learning techniques were proposed for SWC prediction and achieved encouraging results. This paper presents four data mining predictive algorithms for SWC estimation. The used algorithms are random subspace ensemble, random tree (RT), reduced error-pruning tree (REPTree), and M5P. The motivation of this research is to investigate the performance of the popular data mining algorithms for SWC prediction. A benchmark dataset containing daily SWC parameters in three soil layers of 25 cm, 50 cm, and 100 cm from the Nebraska state station (central USA), Grand Island was used to evaluate the proposed techniques. Statistical indices of determination coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), root relative square error (RRMSE), and relative absolute error (RAE) were utilized to measure the performance of the proposed prediction techniques. The modeling results showed that the RT algorithm with R2 = 0.97, RMSE = 0.38, MAE = 0.10, RRMSE = 7.32%, and RAE = 1.82% outperformed counterpart techniques. This study concluded that the developed models will help agricultural water users, developers, and decision-makers for achieving agricultural sustainability.

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
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

References

  • Abbaspour-Gilandeh M, Abbaspour-Gilandeh Y (2019) Modelling soil compaction of agricultural soils using fuzzy logic approach and adaptive neuro-fuzzy inference system (ANFIS) approaches. Model Earth Syst Environ 5(1):13–20

    Article  Google Scholar 

  • Abolkhairian M, Imamqolizadeh P, Victim E, Marufpour A (2012) Comparison of estimation of time changes of soil moisture using artificial neural network and TDR. The second international conference on plant, water, soil and air modeling, Kerman, Iran

  • Adab H, Morbidelli R, Saltalippi C, Moradian M, Ghalhari GAF (2020) Machine learning to estimate surface soil moisture from remote sensing data. Water 12(11):3223

    Article  CAS  Google Scholar 

  • Ahmed AA, Deo RC, Ghahramani A, Raj N, Feng Q, Yin Z, Yang L (2021) LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4. 5 and RCP8. 5 global warming scenarios. Stoch Environ Res Risk Assess 35(9):1851–1881

    Article  Google Scholar 

  • AlShahrani AM, Al-Abadi MA, Al-Malki AS, Ashour AS, Dey N (2018) Automated system for crops recognition and classification. In: Computer vision: concepts, methodologies, tools, and applications, pp 1208–1223

  • Asha Kiranmai S, Jaya Laxmi A (2018) Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy. Prot Control Mod Power Syst 3:1–12

    Article  Google Scholar 

  • Broda M, Hajduk V, Levický D (2017) Universal statistical steganalytic method. J Electr Eng 68(2):117–124

    Google Scholar 

  • Campos de Oliveira MH, Sari V, dos Reis Castro NM, Pedrollo OC (2017) Estimation of soil water content in watershed using artificial neural networks. Hydrol Sci J 62(13):2120–2138

    Article  Google Scholar 

  • Carranza C, Nolet C, Pezij M, van der Ploeg M (2021) Root zone soil moisture estimation with Random Forest. J Hydrol 593:125840

    Article  Google Scholar 

  • Chatterjee S, Dey N, Sen S (2020) Soil moisture quantity prediction using optimized neural supported model for sustainable agricultural applications. Sustain Comput Inform Syst 28:100279

    Google Scholar 

  • Chen L, Xing M, He B, Wang J, Shang J, Huang X, Xu M (2021) Estimating soil moisture over winter wheat fields during growing season using machine-learning methods. IEEE J Sel Top Appl Earth Obs Remote Sens 14:3706–3718

    Article  ADS  Google Scholar 

  • Cobaner M, Unal B, Kisi O (2009) Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. J Hydrol 367:52–61

    Article  Google Scholar 

  • Dai X, Huo Z, Wang H (2011) Simulation for response of crop yield to soil moisture and salinity with artificial neural network. Field Crop Res 121(3):441–449

    Article  Google Scholar 

  • Dehghanisanij H, Emami H, Emami S, Rezaverdinejad V (2022) A hybrid machine learning approach for estimating the water-use efficiency and yield in agriculture. Sci Rep 12(1):6728

  • Deo RC, Tiwari MK, Adamowski JF, Quilty JM (2017) Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stoch Environ Res Risk Assess 31:1211–1240

    Article  Google Scholar 

  • Dobriyal P, Qureshi A, Badola R, Hussain SA (2012) A review of the methods available for estimating soil moisture and its implications for water resource management. J Hydrol 458:110–117

    Article  Google Scholar 

  • Elbeltagi A, Raza A, Hu Y, Al-Ansari N, Kushwaha NL, Srivastava A, Zubair M (2022) Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration. Appl Water Sci 12(7):152

    Article  ADS  Google Scholar 

  • Elbeltagi A, Al-Mukhtar M, Kushwaha NL, Al-Ansari N, Vishwakarma DK (2023) Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment. Appl Water Sci 13(2):42

    Article  ADS  Google Scholar 

  • Emeksiz C, Demir G (2018) An investigation of the effect of meteorological parameters on wind speed estimation using bagging algorithm. Int J Intell Syst Appl Eng 6(4):311–321

    Article  Google Scholar 

  • Esmaeelnejad L, Ramezanpour H, Seyedmohammadi J, Shabanpour M (2015) Selection of a suitable model for the prediction of soil water content in north of Iran. Span J Agric Res 13(1):e1202–e1202

    Article  Google Scholar 

  • Ghorbani MA, Jabehdar MA, Yaseen ZM, Inyurt S (2021) Solving the pan evaporation process complexity using the development of multiple mode of neurocomputing models. Theor Appl Climatol 145:1521–1539

    Article  ADS  Google Scholar 

  • Hachani A, Ouessar M, Paloscia S, Santi E, Pettinato S (2019) Soil moisture retrieval from Sentinel-1 acquisitions in an arid environment in Tunisia: application of Artificial Neural Networks techniques. Int J Remote Sens 40(24):9159–9180

    Article  Google Scholar 

  • Islam ARMT, Al Awadh M, Mallick J, Pal SC, Chakraborty R, Fattah MA, Ghose B, Kakoli MKA, Islam MA, Naqvi HR, Bilal M, Elbeltagi A (2023) Estimating ground-level PM 2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh. Air Qual Atmos Health 16:1117–1139

  • Javadi P, Asadi H, Vazifehdoust M (2022) Prediction of spatial variations of soil moisture using random forest method and environmental features derived from satellite images in Marghab Basin of Khuzestan. Iran J Soil Water Res 52(11):2859–2874

    Google Scholar 

  • Karandish F, Šimůnek J (2016) A comparison of numerical and machine-learning modeling of soil water content with limited input data. J Hydrol 543:892–909

    Article  Google Scholar 

  • Kodikara J, Rajeev P, Chan D, Gallag C (2014) Soil moisture monitoring at the field scale using neutron probe. Can Geotech J 51(3):332–345

    Article  Google Scholar 

  • Liu Y, Mei L, Ooi SK (2014) Prediction of soil moisture based on extreme learning machine for an apple orchard. In: 2014 IEEE 3rd international conference on cloud computing and intelligence systems, pp 400–404

  • Liu Q, Gu X, Chen X, Mumtaz F, Liu Y, Wang C, Zhan Y (2022) Soil moisture content retrieval from remote sensing data by artificial neural network based on sample optimization. Sensors 22(4):1611

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  • Loshelder JI, Coffman RA (2023) Prediction of soil moisture content through photographs of cobalt chloride filter paper in contact with soil specimens. Geotech Test J 46(2):351–363

    Article  Google Scholar 

  • Lunt IA, Hubbard SS, Rubin Y (2005) Soil moisture content estimation using ground penetrating radar reflection data. J Hydrol 307(1–4):254–269

  • Mahmoudi N, Majidi A, Jamei M, Jalali M, Maroufpoor S, Shiri J, Yaseen ZM (2022) Mutating fuzzy logic model with various rigorous meta-heuristic algorithms for soil moisture content estimation. Agric Water Manag 261:107342

    Article  Google Scholar 

  • Malik A, Kumar A (2020) Meteorological drought prediction using heuristic approaches based on effective drought index: a case study in Uttarakhand. Arab J Geosci 13:276

    Article  MathSciNet  Google Scholar 

  • Malik A, Kumar A, Singh RP (2019) Application of heuristic approaches for prediction of hydrological drought using multi-scalar streamflow drought index. Water Resour Manag 33:3985–4006

    Article  Google Scholar 

  • Maroufpoor S, Maroufpoor E, Bozorg-Haddad O, Shiri J, Yaseen ZM (2019) Soil moisture simulation using hybrid artificial intelligent model: hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm. J Hydrol 575:544–556

    Article  Google Scholar 

  • Matei O, Rusu T, Petrovan A, Mihuţ G (2017) A data mining system for real time soil moisture prediction. Procedia Eng 181:837–844

    Article  Google Scholar 

  • Mehrabigohari E, Sarmadian F, Taghizadehmehrjardi R (2012) Prediction of the amount of water at field capacity and permanent wilting point using artificial neural network and multivariate regression. J Irrig Water Eng 3(10):42–52

    Google Scholar 

  • Meisami-asl E, Sharifi A, Mobli H, Eyvani A, Alimardani R (2013) On-site measurement of soil moisture content using an acoustic system. Agric Eng Int CIGR J 15(4):1–8

    Google Scholar 

  • Mohanty M, Sinha NK, Painuli DK, Bandyopadhyay KK, Hati KM, Sammi Reddy K, Chaudhary RS (2015) Modelling soil water contents at field capacity and permanent wilting point using artificial neural network for Indian soils. Natl Acad Sci Lett 38(5):373–377

    Article  Google Scholar 

  • Mokhtar A, Elbeltagi A, Maroufpoor S, Azad N, He H, Alsafadi K, He W (2021) Estimation of the rice water footprint based on machine learning algorithms. Comput Electron Agric 191:106501

    Article  Google Scholar 

  • Mu T, Liu G, Yang X, Yu Y (2023) Soil-moisture estimation based on multiple-source remote-sensing images. Remote Sens 15(1):139

  • Namdarkhojaste D, Sharafa M, Omid M (2011) Estimation of volumetric soil moisture using artificial neural network. Iran Watershed Manag Sci Eng 5(14):53–60

    Google Scholar 

  • Norouzi H, Moghaddam AA (2020) Groundwater quality assessment using random forest method based on groundwater quality indices (case study: Miandoab plain aquifer, NW of Iran). Arab J Geosci 13:1–13

    Article  Google Scholar 

  • Peng J, Loew A, Merlin O, Verhoest NE (2017) A review of spatial downscaling of satellite remotely sensed soil moisture. Rev Geophys 55(2):341–366

    Article  ADS  Google Scholar 

  • Persson M, Bellie S, Ronny B, Ole Jacobsen H, Schjønning P (2002) Predicting the dielectric constant-water content relationship using artificial neural networks. Soil Sci Soc Am J 66:1424–1429

    Article  CAS  Google Scholar 

  • Piao Y, Piao M, Jin CH, Shon HS, Chung JM, Hwang B, Ryu KH (2015) A new ensemble method with feature space partitioning for high-dimensional data classification. Math Probl Eng 2015:1–12

  • Pinto LC, de Mello CR, Norton LD, Owens PR, Curi N (2016) Spatial prediction of soil–water transmissivity based on fuzzy logic in a Brazilian headwater watershed. CATENA 143:26–34

    Article  Google Scholar 

  • Pramudita AA, Wahyu Y, Rizal S, Prasetio MD, Jati AN, Wulansari R, Ryanu HH (2022) Soil water content estimation with the presence of vegetation using ultra wideband radar-drone. IEEE Access 10:85213–85227

    Article  Google Scholar 

  • Prasad R, Deo RC, Li Y, Maraseni T (2019) Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach. CATENA 177:149–166

    Article  Google Scholar 

  • Prehanto DR, Indriyanti AD, Mashuri C, Permadi GS (2019) Soil moisture prediction using fuzzy time series and moisture sensor technology on shallot farming. In: E3S Web of conferences, vol 125, p 23002

  • Quinlan JR (1992) Learning with continuous classes. In: Proceedings of the 5th Australian joint conference on artificial intelligence. Hobart, Singapore

  • Rahimiajdadi F (2016) Determination of soil tilth and feasibility study for estimation of soil moisture content using intelligent systems. Ph.D. thesis in the field of mechanics of agricultural machines, Faculty of Agriculture and Natural Resources, Mohaghegh Ardabili University, Ardabil, Iran

  • Ranjbar S, Akhoondzadeh M (2020) Volumetric soil moisture estimation using Sentinel 1 and 2 satellite images. J Geospatial Inf Technol 7(4):1–12

    Google Scholar 

  • Reynolds SG (1970) The gravimetric method of soil moisture determination Part IA study of equipment, and methodological problems. J Hydrol 11(3):258–273

    Article  Google Scholar 

  • Saha S, Gu F, Luo X, Lytton RL (2017) Prediction of soil-water characteristic curve using artificial neural network approach. In: PanAm unsaturated soils, pp 124–134

  • Sanuade OA, Adetokunbo P, Oladunjoye MA, Olaojo AA (2018) Predicting moisture content of soil from thermal properties using artificial neural network. Arab J Geosci 11(18):1–10

    Article  Google Scholar 

  • Sanuade OA, Hassan AM, Akanji AO, Olaojo AA, Oladunjoye MA, Abdulraheem A (2020) New empirical equation to estimate the soil moisture content based on thermal properties using machine learning techniques. Arab J Geosci 13:1–14

    Article  Google Scholar 

  • Skierucha W (2000) Accuracy of soil moisture measurement by TDR technique. Int Agrophys 14(4):417–426

  • Stenitzer E (1993) Monitoring soil moisture regimes of field crops with gypsum blocks. Theor Appl Climatol 48:159–165

  • Wagner W, Scipal K, Pathe C, Gerten D, Lucht W, Rudolf B (2003) Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data. J Geophys Res Atmos 108(D19):1–10

  • Wang Y, Witten IH (1997) Inducing model trees for continuous classes. In: Proceedings of the ninth European conference on machine learning. Springer, Prague, Czech Republic

  • Werner H (1992) Measuring soil moisture for irrigation water management. Cooperative Extension Service, South Dakota State University, US Department of Agriculture

  • Yin D, Wang Y, Huang Y (2023) Predicting soil moisture content of tea plantation using support vector machine optimized by arithmetic optimization algorithm. J Algorithms Comput Technol 17:17483026221151198

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the High Plains Regional Climate Center (HPRCC) for providing the requisite data. Special thanks go to Natalie Umphlett from the University of Nebraska-Lincoln, School of Natural Resources, USA, that provided the permission to access the measured data of HPRCC.

Funding

The authors declare that no funds, grants, or other supports were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Water Engineering, University of Tabriz, Tabriz, Iran

    Somayeh Emami

  2. Department of Water Engineering, Urmia University, Urmia, Iran

    Vahid Rezaverdinejad

  3. Agricultural Research, Education and Extension Organization, Agricultural Engineering Research Institute, P. O. Box 31585-845, Karaj, Alborz, Iran

    Hossein Dehghanisanij

  4. Department of Computer Engineering, University of Bonab, Bonab, Iran

    Hojjat Emami

  5. Agricultural Engineering Department, Mansoura University, Mansoura, 35516, Egypt

    Ahmed Elbeltagi

Authors
  1. Somayeh Emami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vahid Rezaverdinejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hossein Dehghanisanij
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hojjat Emami
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ahmed Elbeltagi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by SE, VR, HD, HE, and AE. The first draft of the manuscript was written by SE and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Somayeh Emami.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical standard

This study does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all subjects involved in the study.

Additional information

Publisher's Note

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

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

Emami, S., Rezaverdinejad, V., Dehghanisanij, H. et al. Data mining predictive algorithms for estimating soil water content. Soft Comput 28, 4915–4931 (2024). https://doi.org/10.1007/s00500-023-09208-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-09208-3

Keywords

Search

Navigation