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A hybrid deep neural network approach to estimate reference evapotranspiration using limited climate data

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Abstract

Reference evapotranspiration (ET0) plays an undeniably important role in irrigation management. Thus, accurate estimation of ET0 is necessary to avoid over or under irrigation to increase agricultural productivity and manage water resources effectively. Due to the limited availability of climate datasets in developing countries, the estimation of ET0 remains the biggest challenge. This study presents two-hybrid deep neural network models for the estimation of reference evapotranspiration: Convolution—Long Short Term Memory (Conv-LSTM), which performs the convolution operation in LSTM cells and Convolution Neural Network—LSTM (CNN-LSTM) that uses the convolution layer for feature extraction of input data and then extracted features are fed to LSTM layers. The study also focuses on climate data scarcity conditions, and thus, different input combinations of climate parameters have been used to investigate the minimum required parameters to model the ET0 process. The climate dataset of two stations of India: Ludhiana and Amritsar, is adopted to develop proposed models. It includes daily maximum temperature (Tmax), minimum temperature (Tmin), wind speed measured at the height of 2 m (U2), solar radiation (Rs), relative humidity (Rh), vapor pressure (Vp), and sunshine hours (Ssh) data from the period 2003 to 2015 of Ludhiana station and 2000 to 2016 of Amritsar station. Several performance measures are used to assess the precision of the model and to perform sensitivity analysis. Temperature and radiation are observed as the prime data inputs required to estimate ET0 values. The proposed hybrid models are then compared with existing temperature and radiation-based empirical models such as Hargreaves, Makkink, and Ritchie. The comparison reveals that CNN-LSTM and Conv-LSTM outperform these existing models. Also, Conv-LSTM performs best among all for the estimation of ET0.

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References

  1. Gleeson T, Wada Y, Bierkens MFP, van Beek LPH (2012) Water balance of global aquifers revealed by groundwater footprint. Nature 488:197–200. https://doi.org/10.1038/nature11295

    Article  Google Scholar 

  2. Fischer G, Tubiello FN, van Velthuizen H, Wiberg DA (2007) Climate change impacts on irrigation water requirements: Effects of mitigation, 1990–2080. Technol Forecast Soc Change 74:1083–1107. https://doi.org/10.1016/j.techfore.2006.05.021

    Article  Google Scholar 

  3. Dhillon R, Rojo F, Upadhyaya SK et al (2019) Prediction of plant water status in almond and walnut trees using a continuous leaf monitoring system. Precis Agric 20:723–745. https://doi.org/10.1007/s11119-018-9607-0

    Article  Google Scholar 

  4. Gu Z, Qi Z, Ma L et al (2017) Development of an irrigation scheduling software based on model predicted crop water stress. Comput Electron Agric 143:208–221. https://doi.org/10.1016/j.compag.2017.10.023

    Article  Google Scholar 

  5. Jones HG (2004) Irrigation scheduling: advantages and pitfalls of plant-based methods. J Exp Bot 55:2427–2436. https://doi.org/10.1093/jxb/erh213

    Article  Google Scholar 

  6. Rossini M, Fava F, Cogliati S et al (2013) Assessing canopy PRI from airborne imagery to map water stress in maize. ISPRS J Photogramm Remote Sens 86:168–177. https://doi.org/10.1016/j.isprsjprs.2013.10.002

    Article  Google Scholar 

  7. Feng Y, Cui N, Zhao L et al (2016) Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China. J Hydrol 536:376–383. https://doi.org/10.1016/j.jhydrol.2016.02.053

    Article  Google Scholar 

  8. Perera KC, Western AW, Nawarathna B, George B (2014) Forecasting daily reference evapotranspiration for Australia using numerical weather prediction outputs. Agric For Meteorol 194:50–63. https://doi.org/10.1016/j.agrformet.2014.03.014

    Article  Google Scholar 

  9. Ding R, Kang S, Li F et al (2010) Evaluating eddy covariance method by large-scale weighing lysimeter in a maize field of northwest China. Agric Water Manag 98:87–95. https://doi.org/10.1016/j.agwat.2010.08.001

    Article  Google Scholar 

  10. Allen RG, Pereira LS, Raes D, et al (1998) Fao,1998. Irrig Drain Pap No 56, FAO 300. https://doi.org/10.1016/j.eja.2010.12.001

  11. Snyder R, Brown P, Echings S et al (2004) ASCE’s standardized reference evapotranspiration equation. 1–11. https://doi.org/10.1061/9780784408056

  12. Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1:96–99. https://doi.org/10.13031/2013.26773

  13. Allen RG, Pruitt WO (1986) Rational use of The FAO Blaney-Criddle formula. J Irrig Drain Eng 112:139–155. https://doi.org/10.1061/(ASCE)0733-9437(1986)112:2(139)

    Article  Google Scholar 

  14. Makkink GF (1957) Testing the Penman formula by means of lysismeters. Int Water

  15. Jones JWRJ (1990) Crop growth model. In: Hoffman GJ, Howell TA, Solomon KH (eds) Management of farm irrigation systems. ASAE, USA, pp 63–69

  16. Tabari H, Grismer ME, Trajkovic S (2013) Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrig Sci 31:107–117. https://doi.org/10.1007/s00271-011-0295-z

    Article  Google Scholar 

  17. Kişi O, Çimen M (2009) Evapotranspiration modelling using support vector machines. Hydrol Sci J 54:918–928. https://doi.org/10.1623/hysj.54.5.918

    Article  Google Scholar 

  18. Mehdizadeh S, Behmanesh J, Khalili K (2017) Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration. Comput Electron Agric 139:103–114. https://doi.org/10.1016/j.compag.2017.05.002

    Article  Google Scholar 

  19. Ferreira LB, da Cunha FF (2020) New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning. Agric Water Manag 234:106113. https://doi.org/10.1016/j.agwat.2020.106113

    Article  Google Scholar 

  20. Kisi O (2013) Least squares support vector machine for modeling daily reference evapotranspiration. Irrig Sci 31:611–619. https://doi.org/10.1007/s00271-012-0336-2

    Article  Google Scholar 

  21. Guo X, Sun X, Ma J (2011) Prediction of daily crop reference evapotranspiration (ET o) values through a least-squares support vector machine model. Hydrol Res 42:268–274. https://doi.org/10.2166/nh.2011.072

    Article  Google Scholar 

  22. Traore S, Luo Y, Fipps G (2016) Deployment of artificial neural network for short-term forecasting of evapotranspiration using public weather forecast restricted messages. Agric Water Manag 163:363–379. https://doi.org/10.1016/j.agwat.2015.10.009

    Article  Google Scholar 

  23. Valipour M, Sefidkouhi MAG, Raeini-Sarjaz M, Guzman SM (2019) A hybrid data-driven machine learning technique for evapotranspiration modeling in various climates. Atmosphere (Basel) 10:311. https://doi.org/10.3390/atmos10060311

    Article  Google Scholar 

  24. Mattar MA (2018) Using gene expression programming in monthly reference evapotranspiration modeling: a case study in Egypt. Agric Water Manag 198:28–38. https://doi.org/10.1016/j.agwat.2017.12.017

    Article  Google Scholar 

  25. Shamshirband S, Kamsin A (2016) Comparative analysis of reference evapotranspiration equations modelling by extreme learning machine. 127:56–63. https://doi.org/10.1016/j.compag.2016.05.017

  26. Abdullah SS, Malek MA, Abdullah NS et al (2015) Extreme Learning Machines: a new approach for prediction of reference evapotranspiration. J Hydrol 527:184–195. https://doi.org/10.1016/j.jhydrol.2015.04.073

    Article  Google Scholar 

  27. Fan J, Yue W, Wu L et al (2018) Agricultural and forest meteorology evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in di ff erent climates of China. Agric For Meteorol 263:225–241. https://doi.org/10.1016/j.agrformet.2018.08.019

    Article  Google Scholar 

  28. 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 

  29. Feng Y, Cui N, Gong D et al (2017) Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agric Water Manag 193:163–173. https://doi.org/10.1016/j.agwat.2017.08.003

    Article  Google Scholar 

  30. Fang W, Huang S, Huang Q et al (2018) Reference evapotranspiration forecasting based on local meteorological and global climate information screened by partial mutual information. J Hydrol 561:764–779. https://doi.org/10.1016/j.jhydrol.2018.04.038

    Article  Google Scholar 

  31. Saggi MK, Jain S (2019) Reference evapotranspiration estimation and modeling of the Punjab Northern India using deep learning. Comput Electron Agric 156:387–398. https://doi.org/10.1016/j.compag.2018.11.031

    Article  Google Scholar 

  32. Han Y, Wu J, Zhai B et al (2019) Coupling a bat algorithm with XGBoost to estimate reference evapotranspiration in the arid and semiarid regions of China. Adv Meteorol 2019:1–16. https://doi.org/10.1155/2019/9575782

    Article  Google Scholar 

  33. Torres AF, Walker WR, Mckee M (2011) Forecasting daily potential evapotranspiration using machine learning and limited climatic data. Agric Water Manag 98:553–562. https://doi.org/10.1016/j.agwat.2010.10.012

    Article  Google Scholar 

  34. Tang D, Feng Y, Gong D et al (2018) Evaluation of artificial intelligence models for actual crop evapotranspiration modeling in mulched and non-mulched maize croplands. Comput Electron Agric 152:375–384. https://doi.org/10.1016/j.compag.2018.07.029

    Article  Google Scholar 

  35. Walls S, Binns AD, Levison J (2020) Prediction of actual evapotranspiration by artificial neural network models using data from a Bowen ratio energy balance station. Neural Comput Appl 32:14001–14018. https://doi.org/10.1007/s00521-020-04800-2

    Article  Google Scholar 

  36. Nourani V, Elkiran G, Abdullahi J (2019) Multi-station arti fi cial intelligence based ensemble modeling of reference evapotranspiration using pan evaporation measurements. J Hydrol 577:123958. https://doi.org/10.1016/j.jhydrol.2019.123958

    Article  Google Scholar 

  37. Tabari H, Martinez C, Ezani A, Hosseinzadeh Talaee P (2013) Applicability of support vector machines and adaptive neurofuzzy inference system for modeling potato crop evapotranspiration. Irrig Sci 31:575–588. https://doi.org/10.1007/s00271-012-0332-6

    Article  Google Scholar 

  38. Ruan L, Bai Y, Li S et al (2021) Workload time series prediction in storage systems: a deep learning based approach. Cluster Comput 0123456789. https://doi.org/10.1007/s10586-020-03214-y

  39. Ignatov A (2018) Real-time human activity recognition from accelerometer data using convolutional neural networks. Appl Soft Comput J 62:915–922. https://doi.org/10.1016/j.asoc.2017.09.027

    Article  Google Scholar 

  40. Priyadharshini RA (2019) Maize leaf disease classification using deep convolutional neural networks. Neural Comput Appl 31:8887–8895. https://doi.org/10.1007/s00521-019-04228-3

    Article  Google Scholar 

  41. Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270:654–669. https://doi.org/10.1016/j.ejor.2017.11.054

    Article  MathSciNet  MATH  Google Scholar 

  42. Saravi B, Nejadhashemi AP, Tang B (2020) Quantitative model of irrigation effect on maize yield by deep neural network. Neural Comput Appl 32:10679–10692. https://doi.org/10.1007/s00521-019-04601-2

    Article  Google Scholar 

  43. Ayadi W, Elhamzi W, Charfi I, Atri M (2021) Deep CNN for brain tumor classification. Neural Process Lett. https://doi.org/10.1007/s11063-020-10398-2

    Article  Google Scholar 

  44. Karim F, Majumdar S, Darabi H (2019) Insights into lstm fully convolutional networks for time series classification. IEEE Access 7:67718–67725. https://doi.org/10.1109/ACCESS.2019.2916828

    Article  Google Scholar 

  45. Hu C, Wu Q, Li H et al (2018) Deep learning with a long short-term memory networks approach for rainfall-runoff simulation. Water (Switzerland) 10:1–16. https://doi.org/10.3390/w10111543

    Article  Google Scholar 

  46. Hewage P, Trovati M, Pereira E, Behera A (2021) Deep learning—based effective fine—grained weather forecasting model. Pattern Anal Appl 24:343–366. https://doi.org/10.1007/s10044-020-00898-1

    Article  Google Scholar 

  47. Majhi B, Naidu D, Prasad A et al (2020) Improved prediction of daily pan evaporation using Deep-LSTM model. Neural Comput Appl 32:7823–7838. https://doi.org/10.1007/s00521-019-04127-7

    Article  Google Scholar 

  48. Afzaal H, Farooque AA, Abbas F et al (2020) Computation of evapotranspiration with artificial intelligence for precision water resource management. Appl Sci 10:1621. https://doi.org/10.3390/app10051621

    Article  Google Scholar 

  49. Sainath TN, Vinyals O, Senior A, Sak H (2015) Convolutional, long short-term memory, fully connected deep neural networks. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 4580–4584

  50. Xue N, Triguero I, Figueredo GP, Landa-Silva D (2019) Evolving deep CNN-LSTMs for inventory time series prediction. In: 2019 IEEE congress on evolutionary computation, CEC 2019—proceedings. IEEE, Wellington, New Zealand, pp 1517–1524

  51. Shi X, Chen Z, Wang H, et al (2015) Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Adv Neural Inf Process Syst 2015-Janua:802–810

  52. Çiçek E, Gören S (2021) Smartphone power management based on ConvLSTM model. Neural Comput Applic 33:8017–8029. https://doi.org/10.1007/s00521-020-05544-9

    Article  Google Scholar 

  53. Pascanu R, Gulcehre C, Cho K, Bengio Y (2013) How to construct deep recurrent neural networks. In: 2nd International Conference in learning representations ICLR 2014-conference track proceedings, pp 1–13

  54. Shen G, Chen C, Pan Q et al (2018) Research on traffic speed prediction by temporal clustering analysis and convolutional neural network with deformable kernels (May, 2018). IEEE Access 6:51756–51765. https://doi.org/10.1109/ACCESS.2018.2868735

    Article  Google Scholar 

  55. Singh P, Sehgal P (2021) G. V Black dental caries classification and preparation technique using optimal CNN-LSTM classifier, pp 5255–5272

  56. Kim TY, Cho SB (2019) Predicting residential energy consumption using CNN-LSTM neural networks. Energy 182:72–81. https://doi.org/10.1016/j.energy.2019.05.230

    Article  Google Scholar 

  57. Khorram A, Khalooei M, Rezghi M (2021) End-to-end CNN + LSTM deep learning approach for bearing fault diagnosis. Appl Intell 51:736–751. https://doi.org/10.1007/s10489-020-01859-1

    Article  Google Scholar 

  58. Koca A, Oztop HF, Varol Y, Koca GO (2011) Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey. Expert Syst Appl 38:8756–8762. https://doi.org/10.1016/j.eswa.2011.01.085

    Article  Google Scholar 

  59. Sanjar K, Bekhzod O, Kim J et al (2020) Missing data imputation for geolocation-based price prediction using KNN–MCF METHOD. ISPRS Int J Geo-Inform 9:227. https://doi.org/10.3390/ijgi9040227

    Article  Google Scholar 

  60. Holt B, Benfer RA (2000) Estimating missing data: an iterative regression approach. J Hum Evol 39:289–296. https://doi.org/10.1006/jhev.2000.0418

    Article  Google Scholar 

  61. Tutz G, Ramzan S (2015) Improved methods for the imputation of missing data by nearest neighbor methods. Comput Stat Data Anal 90:84–99. https://doi.org/10.1016/j.csda.2015.04.009

    Article  MathSciNet  MATH  Google Scholar 

  62. Liew AWC, Law NF, Yan H (2011) Missing value imputation for gene expression data: computational techniques to recover missing data from available information. Brief Bioinform 12:498–513. https://doi.org/10.1093/bib/bbq080

    Article  Google Scholar 

  63. Beretta L, Santaniello A (2016) Nearest neighbor imputation algorithms: a critical evaluation. BMC Med Inform Decis Mak 16:74. https://doi.org/10.1186/s12911-016-0318-z

    Article  Google Scholar 

  64. Shalabi L Al, Shaaban Z (2006) Normalization as a preprocessing engine for data mining and the approach of preference matrix

  65. Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305

    MathSciNet  MATH  Google Scholar 

  66. Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30:79–82. https://doi.org/10.3354/cr030079

    Article  Google Scholar 

  67. Willmott CJ (1981) On the validation of models. Phys Geogr 2:184–194. https://doi.org/10.1080/02723646.1981.10642213

    Article  Google Scholar 

  68. Patil AP, Deka PC (2016) An extreme learning machine approach for modeling evapotranspiration using extrinsic inputs An extreme learning machine approach for modeling evapotranspiration using extrinsic inputs. Comput Electron Agric 121:385–392. https://doi.org/10.1016/j.compag.2016.01.016

    Article  Google Scholar 

  69. Osroosh Y, Troy Peters R, Campbell CS, Zhang Q (2015) Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold. Comput Electron Agric 118:193–203. https://doi.org/10.1016/j.compag.2015.09.006

    Article  Google Scholar 

  70. Alazba MAMAA (2019) GEP and MLR approaches for the prediction of reference evapotranspiration. Neural Comput Appl 31:5843–5855. https://doi.org/10.1007/s00521-018-3410-8

    Article  Google Scholar 

  71. Tikhamarine Y, Malik A, Souag-Gamane D, Kisi O (2020) Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration. Environ Sci Pollut Res 27:30001–30019. https://doi.org/10.1007/s11356-020-08792-3

    Article  Google Scholar 

  72. Traore S, Luo Y, Fipps G (2017) Gene-Expression programming for short-term forecasting of daily reference evapotranspiration using public weather forecast information. Water Resour Manag 31:4891–4908. https://doi.org/10.1007/s11269-017-1784-5

    Article  Google Scholar 

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Acknowledgments

The authors express their gratitude to the India Meteorological Department of Pune (IMD), India, to access the Meteorological data of Ludhiana and Amritsar stations.

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  1. Department of Computer Science, Thapar Institute of Engineering and Technology, Patiala, India

    Gitika Sharma, Ashima Singh & Sushma Jain

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  1. Gitika Sharma
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Sharma, G., Singh, A. & Jain, S. A hybrid deep neural network approach to estimate reference evapotranspiration using limited climate data. Neural Comput & Applic 34, 4013–4032 (2022). https://doi.org/10.1007/s00521-021-06661-9

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