Pan evaporation modeling using four different heuristic approaches

Wang, Lunche; Niu, Zigeng; Kisi, Özgür; Chang-an, LI; Yu, Deqing

doi:10.1016/j.compag.2017.05.036

Cited by 71 publications

(28 citation statements)

References 46 publications

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“…In addition, there is also the fact that NRPB aggregates predictive information into the models tested, explaining facts that cannot be elucidated only with the backscatter information. The insertion of the NRPB variable in machine learning models, like rf, helps the results to reach better statistical metrics in the validations, and consequently leads to greater generalization capacity in the models [48,49], since these kinds of models are recognized for having a high capacity for pattern recognition and complex solutions [6].…”

Section: Discussionmentioning

confidence: 99%

Crop NDVI Monitoring Based on Sentinel 1

et al. 2019

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Monitoring agricultural crops is necessary for decision-making in the field. However, it is known that in some regions and periods, cloud cover makes this activity difficult to carry out in a systematic way throughout the phenological cycle of crops. This circumstance opens up opportunities for techniques involving radar sensors, resulting in images that are free of cloud effects. In this context, the objective of this work was to obtain a normalized different vegetation index (NDVI) cloudless product (NDVInc) by modeling Sentinel 2 NDVI using different regression techniques and the Sentinel 1 radar backscatter as input. To do this, we used four pairs of Sentinel 2 and Sentinel 1 images on coincident days, aiming to achieve the greatest range of NDVI values for agricultural crops (soybean and maize). These coincident pairs were the only ones in which the percentage of clouds was not equal to 100% for 33 central pivot areas in western Bahia, Brazil. The dataset used for NDVInc modeling was divided into two subsets: training and validation. The training and validation datasets were from the period from 24 June 2017 to 19 July 2018 (four pairs of images). The best performing model was used in a temporal analysis from 02 October 2017 to 08 August 2018, totaling 55 Sentinel 2 images and 25 Sentinel 1 images. The selection of the best regression algorithm was based on two validation methodologies: K-fold cross-validation (k = 10) and holdout. We tested four modeling approaches with eight regression algorithms. The random forest was the algorithm that presented the best statistical metrics, regardless of the validation methodology and the approach used. Therefore, this model was applied to a time series of Sentinel 1 images in order to demonstrate the robustness and applicability of the model created. We observed that the data derived from Sentinel 1 allowed us to model, with great reliability, the NDVI of agricultural crops throughout the phenological cycle, making the methodology developed in this work a relevant solution for the monitoring of various regions, regardless of cloud cover.

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Section: Discussionmentioning

confidence: 99%

Crop NDVI Monitoring Based on Sentinel 1

et al. 2019

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“…The results showed that the AI models provided the most accuracy than the SS and MLR techniques. Wang, Niu, Kisi, Li, and Yu (2017) Remesan, Shamim, & Han, 2008;Singh, Malik, Kumar, & Kisi, 2018). Moghaddamnia, Ghafari Gousheh, Piri, Amin, and Han (2009) simulated evaporation using the ANN and ANFIS models while applying GT to select suitable input variables in different area of Iran.…”

Section: Introductionmentioning

confidence: 99%

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Malik

Kumar

Kim

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and 'M5Tree' were assessed to simulate the pan evaporation in monthly scale (EP m ) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe's Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management. ARTICLE HISTORY

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“…These authors have, for other regions, estimated evapotranspiration from the same meteorological data required by the Penman-Monteith, as well as from scarce data. Methods such as neural networks, regression trees, support vector machines and many others present, for the most part, a heuristic characteristic coupled with a high ability to generalize and model patterns (Wang et al, 2017b(Wang et al, , 2017c.…”

Section: Introductionmentioning

confidence: 99%

Heuristic methods applied in reference evapotranspiration modeling

et al. 2018

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The importance of the precise estimation of evapotranspiration is directly related to sustainable water usage. Since agriculture represents 70% of Brazil’s water consumption, adequate and efficient application of water may reduce the conflicts over the use of water among the multiple users. Considering the importance of accurate estimation of evapotranspiration, the objective of the present study was to model and compare the reference evapotranspiration from different heuristic methodologies. The standard Penman-Monteith method was used as reference for evapotranspiration, however, to evaluate the heuristic methodologies with scarce data, two widely known methods had their performances assessed in relation to Penman-Monteith. The methods used to estimate evapotranspiration from scarce data were Priestley-Taylor and Thornthwaite. The computational techniques Stepwise Regression (SWR), Random Forest (RF), Cubist (CB), Bayesian Regularized Neural Network (BRNN) and Support Vector Machines (SVM) were used to estimate evapotranspiration with scarce and full meteorological data. The results show the robustness of the heuristic methods in the prediction of the evapotranspiration. The performance criteria of machine learning methods for full weather data varied from 0.14 to 0.22 mm d-1 for mean absolute error (MAE), from 0.21 to 0.29 mm d-1 for root mean squared error (RMSE) and from 0.95 to 0.99 coefficient of determination (r²). The computational techniques proved superior performance to established methods in literature, even in scenarios of scarce variables. The BRNN presented the best performance overall.

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Pan evaporation modeling using four different heuristic approaches

Cited by 71 publications

References 46 publications

Crop NDVI Monitoring Based on Sentinel 1

Crop NDVI Monitoring Based on Sentinel 1

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Heuristic methods applied in reference evapotranspiration modeling

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