Estimating FAO Blaney-Criddle b-Factor Using Soft Computing Models

Thongkao, Suthira; Ditthakit, Pakorn; Pinthong, Sirimon; Salaeh, Nureehan; Elkhrachy, Ismail; Linh, Nguyễn Thị Thùy; Pham, Quoc Bao

doi:10.3390/atmos13101536

Cited by 11 publications

(6 citation statements)

References 30 publications

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“…Chiew et al [39] analyzed climate characteristics in 16 Australian locations and found that the FAO24 radiation, FAO24 Blaney-Criddle, and Penman-Monteith gave similar monthly ET, estimates. This result is reinforced by the results of research [40][41][42][43], which showed that the Blaney-Criddle method gave good calculation results of evapotranspiration values, which was close to the results found using the FAO Penman-Monteith method as a basic method. This finding is an important basis for researchers in determining evapotranspiration and crop water requirement when hydro-climatological data are incomplete at a location.…”

Section: Introductionsupporting

confidence: 68%

Long-Term Forecasting of Crop Water Requirement with BP-RVM Algorithm for Food Security and Harvest Risk Reduction

Syaharuddin¹,

Fatmawati²,

Suprajitno³

2023

IJSSE

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Cropping pattern planning is important to avoid crop failure. Meanwhile, cropping patterns are affected by climate change, which is constantly shifting and erratic. Mistakes in determining the planting schedule will affect the risk of crop failure. Hence, climate forecast using long-term hydro-climatological data must be conducted as cropping patterns are mapped for a multi-year period. Data was collected from the Meteorology, Climatology, and Geophysics Agency in Lombok Island. This paper discusses the combination of backpropagation and relevance vector machine with RBF kernel. We utilized BP-RVM architecture with three hidden layers to improve the performance of the network. This combination is utilized because of the BP algorithm's ability to simplify data pattern recognition and RVM to speed up and reduce the number of iterations for each data training-testing process. The evapotranspiration of each crop was then calculated using the FAO24 Blaney-Criddle method. Based on the forecasting, the average MAPE was below 20%, which indicates "good forecasting". The evapotranspiration values of CGPRT and horticultural crops were almost the same with an average of 2.79 mm/day and 2.78 mm/day. These values are lower than the evapotranspiration values of tobacco and rice. Finally, based on the calculation of each crop's water requirement throughout the year, it was recommended to start the first planting season at the end of October. The results of this study can be recommended to the government to apply the BP-RVM algorithm in forecasting hydro-climatological data and optimizing cropping patterns to avoid crop failure and maintain the stability of national food security.

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Section: Introductionsupporting

confidence: 68%

Long-Term Forecasting of Crop Water Requirement with BP-RVM Algorithm for Food Security and Harvest Risk Reduction

Syaharuddin¹,

Fatmawati²,

Suprajitno³

2023

IJSSE

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“…A number of trials were conducted using RT-based model, and the user-defined parameters (not presented here due to space limitations but will be available upon request) are consistent with the RT-based hyper-parameters reported in the previous data mining studies conducted for modeling of reference crop evapotranspiration [100] and global solar radiation [98]. Using the current dataset, the RT-based model was built, trained, and tested for 25,759 instances in 0.09 s, 25,759 instances in 8 s, and 11,039 instances in 3.49 s, respectively.…”

Section: Assessment Of the Prediction Accuracy For The Random Tree (R...mentioning

confidence: 99%

Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power

Bilal,

Yetilmezsoy,

Ouassaid

2024

Energies

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This computational study explores the potential of several soft-computing techniques for wind turbine (WT) output power (kW) estimation based on seven input variables of wind speed (m/s), wind direction (°), air temperature (°C), pitch angle (°), generator temperature (°C), rotating speed of the generator (rpm), and voltage of the network (V). In the present analysis, a nonlinear regression-based model (NRM), three decision tree-based methods (random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), and multilayer perceptron-based soft-computing approach (artificial neural network (ANN) model) were simultaneously implemented for the first time in the prediction of WT output power (WTOP). To identify the top-performing soft computing technique, the applied models’ predictive success was compared using over 30 distinct statistical goodness-of-fit parameters. The performance assessment indices corroborated the superiority of the RF-based model over other data-intelligent models in predicting WTOP. It was seen from the results that the proposed RF-based model obtained the narrowest uncertainty bands and the lowest quantities of increased uncertainty values across all sets. Although the determination coefficient values of all competitive decision tree-based models were satisfactory, the lower percentile deviations and higher overall accuracy score of the RF-based model indicated its superior performance and higher accuracy over other competitive approaches. The generator’s rotational speed was shown to be the most useful parameter for RF-based model prediction of WTOP, according to a sensitivity study. This study highlighted the significance and capability of the implemented soft-computing strategy for better management and reliable operation of wind farms in wind energy forecasting.

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“…The Food and Agricultural Organization of the United Nations (FAO) adopted the FAO Penman-Monteith method (FAO-PM) as the standard for ETo estimation (Allen, et al, 1998). Other models were also formulated and adopted to estimate ETo, such as the temperature-based FAO Balney-Criddle model (Thongkao, et al, 2022;Kwakye, 2023), the equation of Hargreaves (Hargreaves & Samani, 1985), and the radiation-based model developed by Benzarti and Riou (1982). During the last two decades, new artificial intelligencebased approaches (AI) were used in several fields of research (Kisi, 2006).…”

Section: Introductionmentioning

confidence: 99%

Reference Evapotranspiration Modeling Using Artificial Intelligence-Based Approaches: A Bibliometric Analysis

Skhiri,

Ferhi,

Bousselmi

et al. 2024

Am. J. Agric. Sci. Eng. Technol.

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A bibliometric analysis was performed over the period 1992–2023, to pinpoint important trends, emphasis, and geographic distribution of international reference evapotranspiration (ETo) modeling research using intelligence-based approaches. Data was mined from the databases of the online version of Web of Science. The data was analyzed using the Excel program, and the bibliometric mapping was performed using the VOSviewer software. A total of 1148 articles met the required criteria. The findings indicated that the number of articles had increased rapidly over the past five years and that English was the prevalent language (99.5%). Researchers in 99 countries have published in this field of research. China ranked first with 356 articles (31.0%), followed by the United States of America with 193 articles (16.8%). Egypt and Saudi Arabia are two of the top 15 countries in the world. These articles were published in 289 journals; the Journal of Hydrology was the most productive journal (95 articles, 8.3%), followed by Computers and Electronics in Agriculture (67, 5.8%). The most productive author is Kisi Ozgur from Turkey (68 articles, 4.2%). Taking into account all the institutions working on ETo modeling (1555 institutions), the Chinese Academy of Science was ranked first (80 articles, 7.0%), followed by the Egyptian Knowledge Bank (55 articles, 4.8%). Artificial neural networks and machine learning were the most commonly used intelligence-based approaches for ETo modeling. Reinforcement learning was the least popular technique for ETo modeling, which could be due to its complexity and data requirements.

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Estimating FAO Blaney-Criddle b-Factor Using Soft Computing Models

Cited by 11 publications

References 30 publications

Long-Term Forecasting of Crop Water Requirement with BP-RVM Algorithm for Food Security and Harvest Risk Reduction

Long-Term Forecasting of Crop Water Requirement with BP-RVM Algorithm for Food Security and Harvest Risk Reduction

Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power

Reference Evapotranspiration Modeling Using Artificial Intelligence-Based Approaches: A Bibliometric Analysis

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