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Daily scale evapotranspiration prediction over the coastal region of southwest Bangladesh: new development of artificial intelligence model

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Abstract

Among several complex hydrological process elements, Evapotranspiration (ET) is the most complex one. Estimation of ET is very challenging compared to other hydrological variables as it depends on complex interactions of several hydrometeorological variables. In the current research, the estimation of daily ET from maximum and minimum temperature was established. For this purpose, Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) and Multivariate Adaptive Regression Spline (MARS) were hybridized with two advanced metaheuristic optimization algorithms [i.e., Whale Optimization Algorithm (WOA) and Bat Algorithm (BA)]. Daily ET and temperature data estimated at 3 locations in the coastal region of southwest Bangladesh for the period 2005–2016 were used to develop and validate the models. The results showed a good performance of DENFIS-WOA model with minimum values of normalized root mean square error (NRMSE = 0.35–0.54) in estimating ET using only temperature in the complex climatic setup of southwest Bangladesh. DENFIS-BA also showed reasonable performance (NRMSE = 0.43–0.62), while the performance of MARS–WOA (NRMSE = 0.54–0.97) and MARS-BA (0.60–1.13) was found satisfactory in terms of most of the statistical indices. Obtained results were also evaluated using innovative visual presentations of model outputs, which revealed the better capability of only DENFIS-WOA in estimating mean, variability and distribution of ET for all the months and locations. The results indicate the potential of DENFIS-WOA to be used for reliable estimation of daily ET from the temperature in a tropical humid coastal region.

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Data availability

Data were obtained from the published research over the literature and can be provided as supplementary material once the manuscript accepted.

Abbreviations

AI:

Artificial intelligence

ANFIS:

Adaptive neural fuzzy inference systems

ANN:

Artificial neural network

BWDB:

Bangladesh Water Development Board

BA:

Bat algorithm

DENFIS:

Dynamic evolving neural-fuzzy inference system

ET:

Evapotranspiration

ELM:

Extreme learning machine

MARS:

Multivariate adaptive regression splines

MLR:

Multiple linear regression

ML:

Machine learning

NF:

Neuro-fuzzy

RF:

Random forest

PDF:

Probability distribution function

PM:

Penman–Monteith

SVM:

Support vector machine

SWB:

Southwest Bangladesh

WOA:

Whale optimization algorithm

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Acknowledgements

The authors acknowledge the excellent and valuable constructive comments reported by the respected reviewers and editors for improving the manuscript visualization. The authors acknowledge the support received by the Science and Technology Plan Project of Shaanxi Province NO.2020GY-041 in addition to the Doctoral Research Initiation Project NO. 209040080.

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Authors and Affiliations

  1. School of Computer Science, Baoji University of Arts and Sciences, Baoji, 721007, China

    Lu Ye

  2. Electrical Engineering Department, College of Engineering, University of Babylon, Hilla, Babil, Iraq

    Musaddak M. Abdul Zahra

  3. Computer Techniques Engineering Department, Al-Mustaqbal University College, Hilla, Babil, Iraq

    Musaddak M. Abdul Zahra

  4. Department of Civil Engineering, College of Engineering, University of Babylon, Hilla, Iraq

    Najah Kadhim Al-Bedyry

  5. New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Iraq

    Zaher Mundher Yaseen

  6. College of Creative Design, Asia University, Taichung City, Taiwan

    Zaher Mundher Yaseen

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  1. Lu Ye
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Contributions

LY: Conceptualization, methodology, writing up. MMAZ: Formal analysis, validation\evaluation and writing up. NKAB: Investigation, discussion, visualization, writing up. ZMY: Supervision, revision, editing, discussion and writing up.

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Correspondence to Zaher Mundher Yaseen.

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Ye, L., Zahra, M.M.A., Al-Bedyry, N.K. et al. Daily scale evapotranspiration prediction over the coastal region of southwest Bangladesh: new development of artificial intelligence model. Stoch Environ Res Risk Assess 36, 451–471 (2022). https://doi.org/10.1007/s00477-021-02055-4

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