A new approach for neutron moisture meter calibration: artificial neural network

Köksal, Eyüp Seli̇m; Cemek, Bilal; Artık, Cengiz; Temizel, Kadir Ersin; Taşan, Mehmet

doi:10.1007/s00271-010-0246-0

Cited by 22 publications

(9 citation statements)

References 8 publications

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“…Numerous studies have been conducted regarding the indirect measurement methods for measuring soil water content (eg, Tülün, 2005, Evett et al, 2006, Köksal et al, 2011, Kirnak and Akpinar, 2016.…”

Section: Discussionmentioning

confidence: 99%

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Toprak Su İçeriğinin Belirlenmesinde PR2’nin Performansının Değerlendirilmesi

Kaman

Özbek

2021

Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi

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As it is known, drought is one of the most important issues of recent years. On the other hand, agriculture is the sector that uses the most water. The impact of drought can be reduced by reducing the water used in agriculture. Therefore, it is very important to determine the application time and amount of irrigation water for a successful agricultural irrigation management. One of the most important ways to achieve this success is to accurately determine and monitor the water content in the plant root zone. Soil water content can be determined in two ways; direct and indirect methods. The direct measurement method is tedious, laborious and time consuming. Instead, a large number of indirect measurement methods are developed. One of these indirect measurement methods is the PR2 Profile Probe method. In this study, the possibilities of using PR2 method under different soil water content conditions were investigated. First, the calibration of the PR2 method was made within 40 cm of soil depth. Then, the water content in the soil was monitored by the PR2 method under three different soil water content (100%, 75% and 50%) conditions. R2 values for calibration relationships varied between 0.7947 and 0.9305. In the study, it was concluded that soil water content could be monitored with PR2 Profile Probe.

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Section: Discussionmentioning

confidence: 99%

“…Numerous studies have been carried out on the indirect measurement method of soil water content (eg, Tülün, 2005, Evett et al, 2006, Köksal et al, 2011, Kirnak and Akpinar, 2016. Sometimes different and sometimes parallel results emerged depending on other factors such as the method, soil properties, etc.…”

Section: Introductionmentioning

confidence: 99%

Toprak Su İçeriğinin Belirlenmesinde PR2’nin Performansının Değerlendirilmesi

Kaman

Özbek

2021

Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi

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“…A Levenberg Marquadt (LM) back propagation algorithm was used for training the network. Root mean square error (RMSE) and the co-efficient of determinations (r 2 ) were calculated to evaluate the performance of the ANN models (Koksal et al, 2011). Figure 2 illustrates the implementation of one of the ANN structures (4-10-10-1) for estimating hourly Ta, RH and ETr for the days of interest, i.e.…”

Section: Satellite Datamentioning

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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“…Nonetheless, ANNs have attracted considerable attention because of their ability to solve complex problems in various fields of engineering, including agriculture mechanical, environmental, civil, petroleum, and aerospace, among others 12‐22 . Furthermore, ANNs are commonly being used in many energy engineering problems and recently, numerous studies have been performed in ANNs modeling of various energy systems, such as solar energy, 23 building thermal load, 24 power generation systems, 25 etc.…”

Section: Introductionmentioning

confidence: 99%

The use of artificial neural networks to estimate optimum insulation thickness, energy savings, and carbon dioxide emissions

Küçüktopcu

Cemek

2020

Env Prog and Sustain Energy

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This study examined artificial neural networks' (ANNs) applicability in modeling optimum insulation thickness (OIT), annual total net savings (ATS), and reduction of carbon dioxide emissions (RCO2) that result from insulating buildings. Data from insulation markets, economic parameters, fuel prices, and heating degree days (HDDs) were introduced into the model as input variables. To complete the most thorough analysis, three learning algorithms, Levenberg Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) were employed. Five statistical indexes were utilized to evaluate models' performances: determination coefficient (R2), root mean square error (RMSE), standard error of prediction (SEP), RMSE observations' standard deviation ratio (RSR), and average absolute percent relative error (AAPRE). Moreover, visualization techniques were used to assess the similarity between the OIT, ATS, and RCO2 values calculated and predicted. The results obtained clearly show that the LM model outperformed the BR and SCG models in all estimations. Thereafter, the developed ANNs model was validated for different cities. Overall, this model will provide an effective and straightforward guide for people working in the field to improve thermal insulation design, analysis, and implementation worldwide.

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A new approach for neutron moisture meter calibration: artificial neural network

Cited by 22 publications

References 8 publications

Toprak Su İçeriğinin Belirlenmesinde PR2’nin Performansının Değerlendirilmesi

Toprak Su İçeriğinin Belirlenmesinde PR2’nin Performansının Değerlendirilmesi

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

The use of artificial neural networks to estimate optimum insulation thickness, energy savings, and carbon dioxide emissions

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