Comparative Study of M5 Model Tree and Artificial Neural Network in Estimating Reference Evapotranspiration Using MODIS Products

Alipour, Armin; Yarahmadi, Jalal; Mahdavi, Maryam

doi:10.1155/2014/839205

Cited by 26 publications

(14 citation statements)

References 38 publications

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“…In general, the principle of the tree-based model consists of “divide-and-conquer” approach for constructing a relationship between independent and dependent variables or input and output parameters. They can also be used for qualitative and quantitative data assessment [ 51 ]. All model trees can effectively learn and succeed in tasks with very high dimensionality.…”

Section: Methodsmentioning

confidence: 99%

Reference Evapotranspiration Modeling Using New Heuristic Methods

Chen

Yuan

Kisi

et al. 2020

Entropy

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The study investigates the potential of two new machine learning methods, least-square support vector regression with a gravitational search algorithm (LSSVR-GSA) and the dynamic evolving neural-fuzzy inference system (DENFIS), for modeling reference evapotranspiration (ETo) using limited data. The results of the new methods are compared with the M5 model tree (M5RT) approach. Previous values of temperature data and extraterrestrial radiation information obtained from three stations, in China, are used as inputs to the models. The estimation exactness of the models is measured by three statistics: root mean square error, mean absolute error, and determination coefficient. According to the results, the temperature or extraterrestrial radiation-based LSSVR-GSA models perform superiorly to the DENFIS and M5RT models in terms of estimating monthly ETo. However, in some cases, a slight difference was found between the LSSVR-GSA and DENFIS methods. The results indicate that better prediction accuracy may be obtained using only extraterrestrial radiation information for all three methods. The prediction accuracy of the models is not generally improved by including periodicity information in the inputs. Using optimum air temperature and extraterrestrial radiation inputs together generally does not increase the accuracy of the applied methods in the estimation of monthly ETo.

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Section: Methodsmentioning

confidence: 99%

Reference Evapotranspiration Modeling Using New Heuristic Methods

Chen

Yuan

Kisi

et al. 2020

Entropy

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“…And, if automatic processing processes have been successfully used for a long time, then machine analysis systems for data containing complex patterns have become popular in geophysics only recently with the development of machine learning methods. At present, the accuracy of data analysis using artificial intelligence methods is not inferior to classical methods [20,21], while significantly exceeding them in terms of speed and usability. Among the numerous machine learning methods for recognizing anomalies, convolutional neural networks (CNNs) have found the greatest application.…”

Section: Creation Of a Model Of Automated Monitoring And Short-term Fmentioning

confidence: 99%

Principles of organizing earthquake forecasting based on multiparameter sensor-WEB monitoring data

et al. 2020

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The paper describes an approach that allows, basing on the data of multiparameter monitoring of atmospheric and ionospheric parameters and using ground-based and satellite measurements, to select from the data stream a time interval indicating the beginning of the final stage of earthquake preparation, and finally using intelligent data processing to carry out a short-term forecast for a time interval of 2 weeks to 1 day before the main shock. Based on the physical model of the lithosphere-atmospheric-ionospheric coupling, the precursors are selected, the ensemble of which is observed only during the precursory periods, and their identification is based on morphological features determined by the physical mechanism of their generation, and not on amplitude selection based on statistical data processing. Basing on the developed maquette of the automatic processing service, the possibility of real-time monitoring of the situation in a seismically active region will be demonstrated using the territory of the Kamchatka region and the Kuril Islands.

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“…The stopping criteria is either the number of remaining instances to reach a certain number or a very small change in class value. The successful competition of M5P against other regression trees or other conventional ML learners has been stated in recent literature [30,43,44]. Its exploitation under the wrapper scheme of Logitboost could lead to a robust classifier that operates on the field of AL, both for choosing informative u i instances and for providing remarkable classification performance.…”

Section: Methodsmentioning

confidence: 99%

Investigation of Combining Logitboost(M5P) under Active Learning Classification Tasks

2020

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Active learning is the category of partially supervised algorithms that is differentiated by its strategy to combine both the predictive ability of a base learner and the human knowledge so as to exploit adequately the existence of unlabeled data. Its ambition is to compose powerful learning algorithms which otherwise would be based only on insufficient labelled samples. Since the latter kind of information could raise important monetization costs and time obstacles, the human contribution should be seriously restricted compared with the former. For this reason, we investigate the use of the Logitboost wrapper classifier, a popular variant of ensemble algorithms which adopts the technique of boosting along with a regression base learner based on Model trees into 3 different active learning query strategies. We study its efficiency against 10 separate learners under a well-described active learning framework over 91 datasets which have been split to binary and multi-class problems. We also included one typical Logitboost variant with a separate internal regressor for discriminating the benefits of adopting a more accurate regression tree than one-node trees, while we examined the efficacy of one hyperparameter of the proposed algorithm. Since the application of the boosting technique may provide overall less biased predictions, we assume that the proposed algorithm, named as Logitboost(M5P), could provide both accurate and robust decisions under active learning scenarios that would be beneficial on real-life weakly supervised classification tasks. Its smoother weighting stage over the misclassified cases during training as well as the accurate behavior of M5P are the main factors that lead towards this performance. Proper statistical comparisons over the metric of classification accuracy verify our assumptions, while adoption of M5P instead of weak decision trees was proven to be more competitive for the majority of the examined problems. We present our results through appropriate summarization approaches and explanatory visualizations, commenting our results per case.

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Comparative Study of M5 Model Tree and Artificial Neural Network in Estimating Reference Evapotranspiration Using MODIS Products

Cited by 26 publications

References 38 publications

Reference Evapotranspiration Modeling Using New Heuristic Methods

Reference Evapotranspiration Modeling Using New Heuristic Methods

Principles of organizing earthquake forecasting based on multiparameter sensor-WEB monitoring data

Investigation of Combining Logitboost(M5P) under Active Learning Classification Tasks

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