A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System

Kişi, Özgür; Mansouri, Iman; Hu, Jong Wan

doi:10.1155/2017/5356324

Cited by 13 publications

(17 citation statements)

References 25 publications

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“…e model is made up of five layers. e process of each layer is presented in [20]. Kisis et al [19] summarized that the input and output neurons can be fuzzified by a fuzzy quantization approach.…”

Section: Adaptive Neurofuzzy Interference System (Anfis)mentioning

confidence: 99%

“…e DENFIS model is also an integrated model like the ANFIS model [19,48]. In DANFIS, the FIS parameters are calculated using NN back-propagation algorithms by applying evolving fuzzy neural networks (EFuNN) in the model [20]. erefore, the major difference between ANFIS and DENFIS is the application of EFuNN [20].…”

Section: Dynamic Evolving Neurofuzzy Inference System (Denfis) Algorimentioning

confidence: 99%

“…More recently, integrated soft computing models were found to be better to detect different environmental changes [19,20]. Advanced regression and integrated NN models have shown good performance as prediction models for nonlinear performances changes of LST, water level changes, monthly temperature changes, and evaporation [11,[19][20][21].For instance, the wavelet neural network (WNN) was developed to forecast water level changes, while adaptive neurofuzzy inference system (ANFIS) is used to forecast both water level changes and LST changes [21,22]. e ANFIS provides better performances for the dynamic behaviors [23,24].…”

Section: Introductionmentioning

confidence: 99%

“…ey established that the ANFIS with grid partition is the optimal model for change prediction. In addition, Kisi et al [20] developed a dynamic evolving neurofuzzy inference system (DENFIS), which is a fuzzy integrated model, to predict the evaporation of water surface and found that the performance of DENFIS is superior to that of ANFIS as a prediction model for water evaporation. Wang et al [25,26] utilized the soft computing to model the solar radiation in China, and they found that the Multilayer Perceptron (MLP) and radial basis NN models are more accurate in detecting solar radiation at different climatic zones in China; furthermore, the ANFIS and M5 model tree are found to be better in predicting solar radiation at some stations in China.…”

Section: Introductionmentioning

confidence: 99%

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Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Mustafa

Liu

et al. 2020

Advances in Civil Engineering

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The soft computing models used for predicting land surface temperature (LST) changes are very useful to evaluate and forecast the rapidly changing climate of the world. In this study, four soft computing techniques, namely, multivariate adaptive regression splines (MARS), wavelet neural network (WNN), adaptive neurofuzzy inference system (ANFIS), and dynamic evolving neurofuzzy inference system (DENFIS), are applied and compared to find the best model that can be used to predict the LST changes of Beijing area. The topographic change is considered in this study to accurately predict LST; furthermore, Landsat 4/5 TM and Landsat 8OLI_TIRS images for four years (1995, 2004, 2010, and 2015) are used to study the LST changes of the research area. The four models are assessed using statistical analysis, coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) in the training and testing stages, and MARS is used to estimate the important variables that should be considered in the design models. The results show that the LST for the studied area increases by 0.28°C/year due to the urban changes in the study area. In addition, the topographic changes and previously recorded temperature changes have a significant influence on the LST prediction of the study area. Moreover, the results of the models show that the MARS, ANFIS, and DENFIS models can be used to predict the LST of the study area. The ANFIS model showed the highest performances in the training (R2 = 0.99, RMSE = 0.78°C, MAE = 0.55°C) and testing (R2 = 0.99, RMSE = 0.36°C, MAE = 0.16°C) stages; therefore, the ANFIS model can be used to predict the LST changes in the Beijing area. The predicted LST shows that the change in climate and urban area will affect the LST changes of the Beijing area in the future.

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Section: Adaptive Neurofuzzy Interference System (Anfis)mentioning

confidence: 99%

Section: Dynamic Evolving Neurofuzzy Inference System (Denfis) Algorimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Mustafa

Liu

et al. 2020

Advances in Civil Engineering

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“…ANFIS es la conjunción y posee las ventajas de las redes neuronales artificiales y la lógica difusa (Adelkhani et al, 2013;Kisi et al, 2017). Las redes neuronales artificiales se componen de elementos unitarios (neuronas) que operan conjuntamente y están inspiradas en el sistema biológico.…”

Section: Introductionunclassified

Sistema Adaptativo de Inferencias Difusas para la Caracterización de la Calidad Sensorial de Aceitunas Negras Naturales

Álvarez

Modesti

2018

Inf. tecnol.

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ResumenEl objetivo del trabajo es aplicar la lógica difusa en la evaluación sensorial de aceitunas negras naturales, mediante un modelo que vincule la valoración de jueces sobre el color, textura y sabor, con la calidad sensorial del alimento. Se realizaron pruebas de diagnóstico por atributos y valoración general con 30 jueces. El modelo se desarrolló mediante un sistema adaptativo de inferencias difusas, ANFIS. Como entrada se tomó la calificación de los jueces sobre cada atributo y como salida, el valor asignado en la aceptación general. Se definió la ponderación de los atributos evaluados sobre el perfil general de la muestra, determinando tres funciones de membresía de campana generalizada. La red creada demostró desempeño aceptable, aún con un reducido número de neuronas empleados para su desarrollo. Palabras clave: sistema adaptativo; inferencias difusas, ANFIS; calidad sensorial; aceitunas negras AbstractThe objective of the work was the application of diffuse logic in the sensorial evaluation of natural black olives, through the development of a model that links the valuation made by consumer judges in relation to color, texture and flavor attributes, with the food general acceptance. Sensory evaluation was developed with 30 consumer judges. The model was generated using Adaptive Neuro-Fuzzy Inference System, ANFIS. The qualification of the judges for each attribute was considered as input data. The values assigned by them in the general acceptance tests were considered as the output matrix. The weighting of each of the attributes evaluated on the general profile of the sample was defined, which was delimited by means of three generalized bell-type membership functions. The ANFIS network created showed an acceptable performance, even the reduced number of neurons used for its development.

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Modeling of pan evaporation based on the development of machine learning methods

Al-Mukhtar

2021

Theor Appl Climatol

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For effective planning and management of water resources and implementation of the related strategies, it is important to ensure proper estimation of evaporation losses, especially in regions that are prone to drought. Changes in climatic factors, such as changes in temperature, wind speed, sunshine hours, humidity, and solar radiation can have a significant impact on the evaporation process. As such, evaporation is a highly non-linear, non-stationary process, and can be difficult to be modeled based on climatic factors, especially in different agro-climatic conditions. The aim of this study, therefore, is to investigate the feasibility of several machines learning (ML) models (conditional random forest regression, Multivariate Adaptive Regression Splines, Bagged Multivariate Adaptive Regression Splines, Model Tree M5, K-nearest neighbor, and the weighted Knearest neighbor) for modeling the monthly pan evaporation estimation. This study proposes the development of newly explored ML models for modeling evaporation losses in three different locations over the Iraq region based on the available climatic data in such areas. The evaluation of the performance of the proposed model based on various evaluation criteria showed the capability of the proposed weighted K-nearest neighbor model in modeling the monthly evaporation losses in the studies areas with better accuracy when compared with the other existing models used as a benchmark in this study.

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A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System

Cited by 13 publications

References 25 publications

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Sistema Adaptativo de Inferencias Difusas para la Caracterización de la Calidad Sensorial de Aceitunas Negras Naturales

Modeling of pan evaporation based on the development of machine learning methods

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