Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions

Rezaeianzadeh, Mehdi; Zand‐Parsa, Shahrokh; Abghari, Hirad; Zolghadr, Masih; Singh, Vijay P.

doi:10.1007/s00704-012-0595-0

Cited by 31 publications

(10 citation statements)

References 28 publications

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“…Although it is generally accepted that ANNs cannot extrapolate beyond the range of the training data [38], utilization of sigmoidal-type and linear transfer functions in hidden and output layers, respectively, has been recommended for extrapolation purposes [39,40]. For instance, Cigizoglu [41], and Rezaeian-Zadeh et al [42], respectively showed the extrapolation ability of ANNs in predicting daily streamflows and hourly air temperatures. Hence, in the current study, logistic sigmoid and linear transfer functions were utilized in hidden and output layers, respectively.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

“…Although it is generally accepted that the ANNs cannot extrapolate beyond the range of the training data [38], utilization of sigmoidal-type, and linear transfer functions in hidden and output layers, respectively, has been recommended for extrapolation purposes [39,40]. For instance, Rezaeian-Zadeh et al [42], successfully tested the generalization ability of ANNs to predict hourly air temperatures by using the same type of transfer functions. The other successful example of the extrapolation ability of ANNs (compared to multi non-linear regression) was reported by Cigizoglu [41], on daily river flow data.…”

Section: Swat-ann Model Performancementioning

confidence: 99%

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An Integrated Approach for Modeling Wetland Water Level: Application to a Headwater Wetland in Coastal Alabama, USA

Rezaeianzadeh¹,

Kalin

Hantush

2018

Water

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Headwater wetlands provide many benefits such as water quality improvement, water storage, and providing habitat. These wetlands are characterized by water levels near the surface and respond rapidly to rainfall events. Driven by both groundwater and surface water inputs, water levels (WLs) can be above or below the ground at any given time depending on the season and climatic conditions. Therefore, WL predictions in headwater wetlands is a complex problem. In this study a hybrid modeling approach was developed for improved WL predictions in wetlands, by coupling a watershed model with artificial neural networks (ANNs). In this approach, baseflow and stormflow estimates from the watershed draining to a wetland are first estimated using an uncalibrated Soil and Water Assessment Tool (SWAT). These estimates are then combined with meteorological variables and are utilized as inputs to an ANN model for predicting daily WLs in wetlands. The hybrid model was used to successfully predict WLs in a headwater wetland in coastal Alabama, USA. The model was then used to predict the WLs at the study wetland from 1951 to 2005 to explore the possible teleconnections between the El Niño Southern Oscillation (ENSO) and WLs. Results show that both precipitation and the variations in WLs are partially affected by ENSO in the study area. A correlation analysis between seasonal precipitation and the Nino 3.4 Index suggests that winters are wetter during El Niño in Coastal Alabama. Analysis also revealed a significant negative correlation between WLs and the Nino 3.4 Index during the El Niño phase for spring. The findings of this study and the developed methodology/tools are useful to predict long-term WLs in wetlands and construct more accurate restoration plans under a variable climate.

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Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Section: Swat-ann Model Performancementioning

confidence: 99%

An Integrated Approach for Modeling Wetland Water Level: Application to a Headwater Wetland in Coastal Alabama, USA

Rezaeianzadeh¹,

Kalin

Hantush

2018

Water

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“…Cogan et al (2000), Trajkovic et al (2003), Tapiador et al (2004), Capacci and Conway (2005), Ustaoglu et al (2008), Dahamsheh and Aksoy (2009) and Voyant et al (2014) used ANN techniques for modelling climatic data such as air temperature, wind velocity, global radiation and precipitation. Rezaeian-Zadeh et al (2012). Almonacid et al (2013) and Okoh et al (2015) developed ANN models for estimating hourly air temperature and statistically significant results were obtained.…”

Section: Introductionmentioning

confidence: 99%

Estimating missing hourly climatic data using artificial neural network for energy balance based ET mapping applications

Köksal

Cemek

Çetin

et al. 2017

Meteorological Applications

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Remote sensing based evapotranspiration (ET) mapping has become an important tool for water resources management at a regional scale. Accurate hourly climatic data and alfalfa‐reference ET (ETr) are crucial inputs for successfully implementing remote sensing based ET models such as Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) and Surface Energy Balance Algorithm for Land (SEBAL). In Turkey, hourly climatic data may not be available at all locations, either due to cost constraints or due to equipment malfunctioning. In this study, the artificial neural network (ANN) technique was used to estimate missing and unmeasured hourly climatic data and ETr for the agriculturally important semi‐humid Bafra plains located in northern Turkey. Modelled and actual climatic data were used to derive ET maps from two Landsat 5 Thematic Mapper images acquired on 2 September 2009 and 4 August 2010. The METRIC algorithm was used to generate ET maps. Accuracy assessment of the METRIC‐derived ET maps indicated that climatic data and ETr estimated through ANN could be used for accurately mapping ET, where hourly climatic data are missing or not measured.

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“…Each group of input nodes which has identical information as the input is indicated by one hidden node, and the transformation related to any node within the hidden layer is named a Gaussian function [35]. More detailed information about RBNN theory can be obtained from Haykin [36].…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods

et al. 2017

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This paper investigates the effectiveness of four different soft computing methods, namely radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM) and M5 model tree (M5Tree), for predicting the ultimate strength and strain of concrete cylinders confined with fiber-reinforced polymer (FRP) sheets. The models were compared according to the root mean square error (RMSE), mean absolute relative error (MARE) and determination coefficient (R 2 ) criteria. Similar accuracy was obtained by RBNN and ANFIS-FCM, and they provided better estimates in modeling ultimate strength of confined concrete. The ANFIS-SC, however, performed slightly better than the RBNN and ANFIS-FCM in estimating ultimate strain of confined concrete, and M5Tree provided the worst strength and strain estimates. Finally, the effects of strain ratio and the confinement stiffness ratio on strength and strain were investigated, and the confinement stiffness ratio was shown to be more effective.

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Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions

Cited by 31 publications

References 28 publications

An Integrated Approach for Modeling Wetland Water Level: Application to a Headwater Wetland in Coastal Alabama, USA

An Integrated Approach for Modeling Wetland Water Level: Application to a Headwater Wetland in Coastal Alabama, USA

Estimating missing hourly climatic data using artificial neural network for energy balance based ET mapping applications

Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods

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