Assessing temporal data partitioning scenarios for estimating reference evapotranspiration with machine learning techniques in arid regions

Kazemi, Mohammad Hossein; Shiri, Jalal; Martí, Pau; Majnooni-Heris, Abolfazl

doi:10.1016/j.jhydrol.2020.125252

Cited by 16 publications

(1 citation statement)

References 37 publications

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“…Yin et al [57] evaluated ET in the eddy covariance flux observations at 14 Chinese flux tower sites during the period 2003-2017, and each site has at least 3 years of reliable data. Hossein Kazemi et al [58] only used the daily meteorological records of seven weather stations in Iran for 10 years (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). Therefore, our data are enough to train a machine learning model.…”

Section: Accuracy Of Ann-pm Model Under Dry Climatesmentioning

confidence: 99%

Using Artificial Neural Network Algorithm and Remote Sensing Vegetation Index Improves the Accuracy of the Penman-Monteith Equation to Estimate Cropland Evapotranspiration

Liu

Zhang

et al. 2021

Applied Sciences

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Accurate estimation of evapotranspiration (ET) can provide useful information for water management and sustainable agricultural development. However, most of the existing studies used physical models, which are not accurate enough due to our limited ability to represent the ET process accurately or rarely focused on cropland. In this study, we trained two models of estimating croplands ET. The first is Medlyn–Penman–Monteith (Medlyn–PM) model. It uses artificial neural network (ANN)-derived gross primary production along with Medlyn’s stomatal conductance to compute surface conductance (Gs), and the computed Gs is used to estimate ET using the PM equation. The second model, termed ANN-PM, directly uses ANN to construct Gs and simulate ET using the PM equation. The results showed that the two models can reasonably reproduce ET with ANN-PM showing a better performance, as indicated by the lower error and higher determination coefficients. The results also showed that the performances of ANN-PM without the facilitation of any remote sensing (RS) factors degraded significantly compared to the versions that used RS factors. We also evidenced that ANN-PM can reasonably characterize the time-series changes of ET at sites having a dry climate. The ANN-PM method can reasonably estimate the ET of croplands under different environmental conditions.

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Section: Accuracy Of Ann-pm Model Under Dry Climatesmentioning

confidence: 99%

Using Artificial Neural Network Algorithm and Remote Sensing Vegetation Index Improves the Accuracy of the Penman-Monteith Equation to Estimate Cropland Evapotranspiration

Liu

Zhang

et al. 2021

Applied Sciences

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

Lü

Zahra

Al-Bedyry

et al. 2021

Stoch Environ Res Risk Assess

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Scrutinizing different predictive modeling validation methodologies and data-partitioning strategies: new insights using groundwater modeling case study

Lal,

Sharan,

Sharma

et al. 2024

Environ Monit Assess

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Groundwater salinity is a critical factor affecting water quality and ecosystem health, with implications for various sectors including agriculture, industry, and public health. Hence, the reliability and accuracy of groundwater salinity predictive models are paramount for effective decision-making in managing groundwater resources. This pioneering study presents the validation of a predictive model aimed at forecasting groundwater salinity levels using three different validation methods and various data partitioning strategies. This study tests three different data validation methodologies with different data-partitioning strategies while developing a group method of data handling (GMDH)-based model for predicting groundwater salinity concentrations in a coastal aquifer system. The three different methods are the hold-out strategy (last and random selection), k-fold cross-validation, and the leave-one-out method. In addition, various combinations of data-partitioning strategies are also used while using these three validation methodologies. The prediction model’s validation results are assessed using various statistical indices such as root mean square error (RMSE), means squared error (MSE), and coefficient of determination (R2). The results indicate that for monitoring wells 1, 2, and 3, the hold-out (random) with 40% data partitioning strategy gave the most accurate predictive model in terms of RMSE statistical indices. Also, the results suggested that the GMDH-based models behave differently with different validation methodologies and data-partitioning strategies giving better salinity predictive capabilities. In general, the results justify that various model validation methodologies and data-partitioning strategies yield different results due to their inherent differences in how they partition the data, assess model performance, and handle sources of bias and variance. Therefore, it is important to use them in conjunction to obtain a comprehensive understanding of the groundwater salinity prediction model's behavior and performance.

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Assessing temporal data partitioning scenarios for estimating reference evapotranspiration with machine learning techniques in arid regions

Cited by 16 publications

References 37 publications

Using Artificial Neural Network Algorithm and Remote Sensing Vegetation Index Improves the Accuracy of the Penman-Monteith Equation to Estimate Cropland Evapotranspiration

Using Artificial Neural Network Algorithm and Remote Sensing Vegetation Index Improves the Accuracy of the Penman-Monteith Equation to Estimate Cropland Evapotranspiration

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

Scrutinizing different predictive modeling validation methodologies and data-partitioning strategies: new insights using groundwater modeling case study

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