New alternatives for reference evapotranspiration estimation in West Africa using limited weather data and ancillary data supply strategies.

Landeras, Gorka; Bekoe, E. Asante; Ampofo, Joseph A.; Logah, F. Y.; Diop, Mbaye; Cissé, M.F.; Shiri, Jalal

doi:10.1007/s00704-017-2120-y

Cited by 41 publications

(13 citation statements)

References 34 publications

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“…When lacking meteorological data or a complete meteorological station, many researchers and decision makers have used methods, which are limited in input or neighbor stations (Althoff et al, 2019; Feng et al, 2017; Ferreira et al, 2019; Hargreaves & Allen, 2003; Landeras et al, 2018; Zhu et al, 2019). Yet the performance of those methods is, in many cases, and even after calibration, lower than the performance of the gridded ETo developed here.…”

Section: Resultsmentioning

confidence: 99%

ETo‐Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000–2018)

Althoff

Dias

Filgueiras

et al. 2020

Water Resources Research

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The reference evapotranspiration (ETo) has long been used as a climate parameter for many studies in climatology and hydrology. However, many regions suffer from shortage of both meteorological monitoring stations and historical information on ETo. Thus, the objective of this study was to develop a daily gridded reference evapotranspiration data set for Brazil that matches the period and grid cells of the Global Precipitation Measurement (GPM) data. ETo was calculated using data from 849 weather stations over the period from 1 June 2000 to 31 December 2018. The features used to model ETo were the GPM daily data set, WorldClim averages monthly, and two engineered features. Among the machine learning algorithms assessed, the Cubist presented the best performance‐computation cost trade‐off in a subset of the entire data and, therefore, was selected to model ETo daily. The developed data set presented root mean square error of 0.65 mm day−1, or 16% lower than previous ETo data set developed for Brazil using interpolation techniques. The GPM and engineered features showed higher importance for the models trained during the wet season, while the WorldClim maximum temperature averages monthly were more important during the dry and cold season. The new gridded reference evapotranspiration data set for Brazil (ETo‐Brazil) was made freely available to the community.

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Section: Resultsmentioning

confidence: 99%

ETo‐Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000–2018)

Althoff

Dias

Filgueiras

et al. 2020

Water Resources Research

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“…Furthermore, this procedure makes it capable of expressing several relationships between input and output variables. This unique feature makes GEP stronger than other methods [65,66].…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

“…Finally, we selected genetic operators. GeneXpro default operators were also chosen following previous research [65].…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

Estimating Soil Available Phosphorus Content through Coupled Wavelet–Data-Driven Models

et al. 2020

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Soil phosphorus (P) is a vital but limited element which is usually leached from the soil via the drainage process. Soil phosphorus as a soluble substance can be delivered through agricultural fields by runoff or soil loss. It is one of the most essential nutrients that affect the sustainability of crops as well as the energy transfer for living organisms. Therefore, an accurate simulation of soil phosphorus, which is considered as a point source pollutant in elevated contents, must be performed. Considering a crucial issue for a sustainable soil and water management, an effective soil phosphorus assessment in the current research was conducted with the aim of examining the capability of five different wavelet-based data-driven models: gene expression programming (GEP), neural networks (NN), random forest (RF), multivariate adaptive regression spline (MARS), and support vector machine (SVM) in modeling soil phosphorus (P). In order to achieve this goal, several parameters, including soil pH, organic carbon (OC), clay content, and soil P data, were collected from different regions of the Neyshabur plain, Khorasan-e-Razavi Province (Northeast Iran). First, a discrete wavelet transform (DWT) was applied to the pH, OC, and clay as the inputs and their subcomponents were utilized in the applied data-driven techniques. Statistical Gamma test was also used for identifying which effective soil parameter is able to influence soil P. The applied methods were assessed through 10-fold cross-validation scenarios. Our results demonstrated that the wavelet–GEP (WGEP) model outperformed the other models with respect to various validations, such as correlation coefficient (R), scatter index (SI), and Nash–Sutcliffe coefficient (NS) criteria. The GEP model improved the accuracy of the MARS, RF, SVM, and NN models with respect to SI-NS (By comparing the SI values of the GEP model with other models namely MARS, RF, SVM, and NN, the outputs of GEP showed more accuracy by 35%, 30%, 40%, 50%, respectively. Similarly, the results of the GEP outperformed the other models by 3.1%, 2.3%, 4.3%, and 7.6%, comparing their NS values.) by 35%-3.1%, 30%-2.3%, 40%-4.3%, and 50%-7.6%, respectively.

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“…The performance of AI-based models such as ANN and GEP and ancillary/external approaches were investigated by Landeras et al (2018) for the estimation of ET o at four stations in Ghana. They used 7 years' data (2006)(2007)(2008)(2009) for the training and 4 years' data (2009)(2010)(2011)(2012) for the testing of the models.…”

Section: State Of the Art: Evolutionary Computing (Ec) Models For Et mentioning

confidence: 99%

Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions

Wang

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Shahid

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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Evapotranspiration is one of the most important components of the hydrological cycle as it accounts for more than two-thirds of the global precipitation losses. Indeed, the accurate prediction of reference evapotranspiration (ET o) is highly significant for many watershed activities, including agriculture, water management, crop production and several other applications. Therefore, reliable estimation of ET o is a major concern in hydrology. ET o can be estimated using different approaches, including field measurement, empirical formulation and mathematical equations. Most recently, advanced machine learning models have been developed for the estimation of ET o. Among several machine learning models, evolutionary computing (EC) has demonstrated a remarkable progression in the modeling of ET o. The current research is devoted to providing a new milestone in the implementation of the EC algorithm for the modeling of ET o. A comprehensive review is conducted to recognize the feasibility of EC models and their potential in simulating ET o in a wide range of environments. Evaluation and assessment of the models are also presented based on the review. Finally, several possible future research directions are proposed for the investigations of ET o using EC.

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New alternatives for reference evapotranspiration estimation in West Africa using limited weather data and ancillary data supply strategies.

Cited by 41 publications

References 34 publications

ETo‐Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000–2018)

ETo‐Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000–2018)

Estimating Soil Available Phosphorus Content through Coupled Wavelet–Data-Driven Models

Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions

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