Neural computing modeling of the reference crop evapotranspiration

Adeloye, Adebayo J.; Rustum, Rabee; Kariyama, Ibrahim Denka

doi:10.1016/j.envsoft.2011.10.012

Cited by 67 publications

(18 citation statements)

References 20 publications

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“…In recent years, ANNs have intensively been applied in forest and agriculture hydrology, e.g., in estimating evapotranspiration (Kumar et al 2002, Adeloye et al 2012, Huo et al 2012, trunk sap flow (Liu et al 2009) and transpiration (Zee 2001a, 2001b, Vrugt et al 2002, Garcia-Santos 2007, Meijun et al 2007). In forest management, ANNs have also been applied to estimate tree volume (Gorgens et al 2009, Silva et al 2009, Diamantopoulou & Milios 2010, Özçelik et al 2010, Yu & Jia-Yin 2012, growth modeling (Castro et al 2013), tree height (Binoti et al 2013a), and to describe diameter distribution (Leite et al 2011, Binoti et al 2013b).…”

Section: Introductionmentioning

confidence: 99%

Coupling daily transpiration modelling with forest management in a semiarid pine plantation

Fernandes¹,

Campo²,

García-Bartual³

et al. 2016

iForest

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Estimating forest transpiration is of great importance for Adaptive Forest Management (AFM) in the scope of climate change prediction. AFM in the Mediterranean region usually generates a mosaic of different canopy covers within the same forest. Several models and methods are available to estimate forest transpiration, but most require a homogeneous forest cover, or an individual calibration/validation process for each cover stand. Hence, a model capable of reproducing accurately the transpiration of the whole canopy-cover mosaic is necessary. In this paper, the use of Artificial Neural Network (ANN) is proposed as a flexible tool for estimating forest transpiration using the forest cover as an input variable. To that end, sap flow, soil water content and other environmental variables were experimentally collected under five Aleppo pine stands of different canopy covers for two years. These sets of inputs were then used for the ANN training. Stand transpiration was accurately estimated using climate data, soil water content and forest cover through the ANN approach (correlation coefficient R = 0.95; Nash-Sutcliffe coefficient E = 0.90; root-meansquare error RMSE = 0.078 mm day -1 ). Finally, the input value for soil water content (when not available) was computed using the process-based model Gotilwa+. Then, this computed soil water content was used as input in the proposed ANN. This combination predicted the forest transpiration with values of R = 0.90, E = 0.63, and RMSE = 0.068 mm day -1 . Artificial Neural Network proved to be a useful and flexible tool to predict the transpiration dynamics of an Aleppo pine stand regardless of the heterogeneity of the forest cover produced by adaptive forest management.

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Section: Introductionmentioning

confidence: 99%

Coupling daily transpiration modelling with forest management in a semiarid pine plantation

Fernandes¹,

Campo²,

García-Bartual³

et al. 2016

iForest

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“…The variables used in the analyses are summarised in Table 1, and those identified as being significantly related to the measures of stability, by either LMM or KSOM, are shown in Table 2. The performance of KSOM in modelling measured values in terms of the correlation coefficient (R) is seen in Table 3 from which it is clear that the KSOM model could model the majority of the parameters as their R values are over 70% (very strong for uncertain environmental data, Rustum & Adeloye, 2007;Rustum et al, 2008;Adeloye et al, 2012). The KSOM model failed to successfully model the measured values for thixotropic resistance.…”

Section: Resultsmentioning

confidence: 99%

Probing soil physical and biological resilience data from a broad sampling of arable farms in Scotland

Griffiths

Hallett

Daniell

et al. 2015

Soil Use and Management

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Physical and biological soil stabilities (i.e. resistance and resilience) were measured on a range of arable farms across eastern Scotland under a range of management practices, with the objective of using a geographically restricted set of soils under similar land use to detect any underlying associations between soil stability, management factors and soil properties. Data were analysed using a combination of a stepwise fixed effects model selection within a linear mixed-model framework (LMM) and neural network analysis using a Kohonen self-organising map (KSOM). In general, physical and biological measures of stability were associated with both physical and biological soil properties, particularly bulk density, water retention characteristics, soil carbon and bacterial community structure. A strength of KSOM is its ability to fit more flexible models than the linear relationships of LMM. However, a weakness is that it does not have the ability of LMM to model the sampling design, which is likely to lead to overstating statistical significance. Consequently, KSOM identified more significant associations between soil properties and stability than LMM, while the latter identified significant associations at the between-farm level. The high-level land management decisions of farm type (conventional, organic, integrated), crop type or underlying soil type were not associated with stability at this regional scale, thus indicating that the effects of different management practices between farms were overridden by the soil properties on each farm. Management decisions on improving soil stability therefore need to be taken at the individual field scale.

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“…The learning process takes place in between BMU and its neighboring neurons at each training iteration 't' with an aim to reduce the distance between weights and input. Kohonen self organizing map [40].…”

Section: Training the Ksommentioning

confidence: 99%

Nonlinear Evapotranspiration Modeling Using Artificial Neural Networks

Adamala¹

2019

Advanced Evapotranspiration Methods and Applications

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Reference evapotranspiration (ET o) is an important and one of the most difficult components of the hydrologic cycle to quantify accurately. Estimation/measurement of ET o is not simple as there are number of climatic parameters that can affect the process. There exists copious conventional (direct and indirect) and non conventional/soft computing (artificial neural networks, ANNs) methods for estimating ET o. Direct methods have the limitations of measurement errors, expensive, impracticality of acquiring point measurements for spatially variable locations, whereas the indirect methods have the limitations of unavailability of all necessary climate data and lack of generalizability (needs local calibration). In contrast to conventional methods, soft computing models can estimate ET o accurately with minimum climate data which have advantages over limitations of conventional ET o methods. This chapter reviews the application of ANN methods in estimating ET o accurately for 15 locations in India using six climatic variables as input. The performance of ANN models were compared with the multiple linear regression (MLR) models in terms of root mean squared error, coefficient of determination and ratio of average output and target ET o values. The results suggested that the ANN models performed better as compared to MLR for all locations.

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Neural computing modeling of the reference crop evapotranspiration

Cited by 67 publications

References 20 publications

Coupling daily transpiration modelling with forest management in a semiarid pine plantation

Coupling daily transpiration modelling with forest management in a semiarid pine plantation

Probing soil physical and biological resilience data from a broad sampling of arable farms in Scotland

Nonlinear Evapotranspiration Modeling Using Artificial Neural Networks

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