Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models

Lu, Xianghui; Yan, Jun X.; Wu, Lifeng; Fan, Junliang; Zhang, Fucang; Li, Zhijun

doi:10.1016/j.jhydrol.2018.09.055

Cited by 95 publications

(31 citation statements)

References 52 publications

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“…The development of indirect estimation methods based on the use of different meteorological variables such as sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures is often suggested for the estimation of evaporation, especially when working with empirical and semiempirical models (Ali Ghorbani et al, 2018;Lu et al, 2018). However, the major problem with using this form of evaporation estimation is the dynamic nature of the applied meteorological variables, owing to their nonlinearity, non-stationary, and stochastic features.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, a number of investigations into the implementation of machine learning (ML) models for evaporation estimation have been conducted across different regions (Abghari, Ahmadi, Besharat, & Rezaverdinejad, 2012;Baydaroǧlu & Koçak, 2014;Di et al, 2019;Fallah-Mehdipour, Bozorg Haddad, & Mariño, 2013;Fotovatikhah, Herrera, Shamshirband, Ardabili, & Piran, 2018;Lu et al, 2018;Majhi, Naidu, Mishra, & Satapathy, 2019;Moazenzadeh et al, 2018;Tabari, Marofi, & Sabziparvar, 2010). Several versions of ML models have been developed for evaporation modeling, including evolutionary computing, classical neural networks, kernel models, fuzzy logic, decision trees, deep learning, complementary wavelet-machine learning, and hybrid machine learning, among others (Danandeh Mehr et al, 2018;Fahimi, Yaseen, & El-shafie, 2016;Jing et al, 2019;Yaseen, Sulaiman, Deo, & Chau, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Al-Juboori

Beyaztaş

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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Evaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation-the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM)were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R 2 = .92), and with all variables as inputs at Station II (R 2 = .97). All the ML models performed well in predicting evaporation at the investigated locations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Al-Juboori

Beyaztaş

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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“…Applications of ML algorithms can provide accurate models of natural phenomena [22][23][24][25] and thus can be good candidates for QPE of rain radar data. Recently, random forest (RF), stochastic gradient boosted model (GBM), and extreme learning machine (ELM) have been actively employed as ML algorithms [26][27][28]. ese advanced ML algorithms, which have been tested recently, would increase our capacity to build QPE model.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018

Shin

Cha

et al. 2019

Advances in Meteorology

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Machine learning algorithms should be tested for use in quantitative precipitation estimation models of rain radar data in South Korea because such an application can provide a more accurate estimate of rainfall than the conventional ZR relationship-based model. The applicability of random forest, stochastic gradient boosted model, and extreme learning machine methods to quantitative precipitation estimation models was investigated using case studies with polarization radar data from Gwangdeoksan radar station. Various combinations of input variable sets were tested, and results showed that machine learning algorithms can be applied to build the quantitative precipitation estimation model of the polarization radar data in South Korea. The machine learning-based quantitative precipitation estimation models led to better performances than ZR relationship-based models, particularly for heavy rainfall events. The extreme learning machine is considered the best of the algorithms used based on evaluation criteria.

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“…The amount of water evaporated is a critical index in evaluating the water and energy balance in agriculture and hydrology, such as soil water prediction, water conservancy construction, and crop yield estimation [10,11]. However, the determination of soil evaporation is a rather difficult problem [12], in that soil atmosphere is a fairly complex system with soil moisture transport from the soil inside to the atmosphere through the bare ground or the plants covered. It relies on both meteorological factors (e.g., solar radiation, atmospheric temperature, atmospheric humidity, and wind speed) and the soil property (e.g., soil structure, water content, and organic matter content), not to mention the interactions of these factors.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, evaporation measurement methodology has always been the core of soil evaporation research. Many experimental methods have been proposed in recent decades [12,13], e.g., automatic weighing soil lysimeter, soil evaporating pan, soil column test, heat flux device, infrared thermometer, and Bowen ratio method. Besides, some indirect methods were also adopted in estimating soil evaporation, e.g., the Cooper method [14], the zero flux plane (ZFP) method [15], the modified Fox model [16], the aerodynamic model [17,18], and the Aydin model [19].…”

Section: Introductionmentioning

confidence: 99%

Evaporation-Induced Water and Solute Coupled Transport in Saline Loess Columns in Closed and Open Systems

Lan

et al. 2019

Geofluids

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Evaporation-induced water and solute coupled transport is a significant cause for soil salinization that leads to a series of engineering and environmental problems. In the artificial evaporation environment, including relative humidity, atmospheric temperature, atmospheric pressure, radiation intensity, and wind speed, evaporation of loess columns with sodium sulfate, temperature profile, and water and solute transport in closed and open systems were investigated. In the former case, a range of stability was noted in the lower part of the column where the heat, water, and solute coupled transport only exhibited a weak response to a specified evaporation environment. As the environment was more favorable for evaporation, or at a higher gradient of temperature, this range extended downwards, while above this range the heat, water, and salt profiles in the evaporation-affected domain changed dramatically, characterized by a basic law that solutes moved with water and were then retained with water desalinated. Evidences were found from the profiles that the water contents in the evaporation-affected range decreased but the salt contents increased, especially in the surface. In an open system, by contrast, there was little difference in temperature but greater in water and salt profiles. Three stages were found from the supply of external water during evaporation. In the initial stage, the higher gradient formed between the initial moisture state and the preset external water level led to a rapid supply of external water. As the evaporation proceeded, a relatively stable water profile was reached as the intensity of water supply approached to that of evaporation, accompanied by a continual migration of solutes towards the surface. Due to the accumulation of precipitated salts, water transport was slowed down, and the intensity of water supply decreased. The changes in soil suction may account for the above behavior.

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Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models

Cited by 95 publications

References 52 publications

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018

Evaporation-Induced Water and Solute Coupled Transport in Saline Loess Columns in Closed and Open Systems

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