Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables

Antonopoulos, Vassilis Z.; Antonopoulos, Athanasios V.

doi:10.1016/j.compag.2016.11.011

Cited by 166 publications

(86 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…ANNs have robust learning and generalization ability, after simulating the learning and decision-making process of human beings. Therefore, ANNs can describe the linear or non-linear relationships precisely even with limited input variables [27,28] and identify complex patterns in dataset without adequate understanding of the interaction among variables [24]. Besides this, because the learning mechanism in ANNs is non-parametric, the structure and distribution of data are not limitations [29].…”

Section: Introductionmentioning

confidence: 99%

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Chen

Huang

2018

Water

View full text Add to dashboard Cite

Freshwater reservoirs are considered as the source of atmospheric greenhouse gas (GHG), but more than 96% of global reservoirs have never been monitored. Compared to the difficulty and high cost of field measurements, statistical models are a better choice to simulate the carbon emissions from reservoirs. In this study, two types of Artificial Neural Networks (ANNs), Back Propagation Neural Network (BPNN) and Generalized Regression Neural Network (GRNN), were used to predict carbon dioxide (CO 2 ) flux emissions from reservoirs based on the published data. Input variables, which were latitude, age, the potential net primary productivity, and mean depth, were selected by Spearman correlation analysis, and then the rationality of these inputs was proved by sensitivity analysis. Besides this, a Multiple Non-Linear Regression (MNLR) and a Multiple Linear Regression (MLR) were used for comparison with ANNs. The performance of models was assessed by statistical metrics both in training and testing phases. The results indicated that ANNs gave more accurate results than regression models and GRNN provided the best performance. With the help of this GRNN, the total CO 2 emitted by global reservoirs was estimated and possible CO 2 flux emissions from a planned reservoir was assessed, which illustrated the potential application of GRNN.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Chen

Huang

2018

Water

View full text Add to dashboard Cite

show abstract

“…On a vegetated surface, evapotranspiration consists in the transfer of water from the soil-plant system to the atmosphere. Adequate estimation of evapotranspiration is fundamental for a project and management of irrigation, hydrological studies, among others (Antonopoulos & Antonopoulos, 2017).…”

Section: Introductionmentioning

confidence: 99%

Performance of different methods for reference evapotranspiration estimation in Jaíba, Brazil

Silva

Dias

Ferreira

et al. 2018

Rev. bras. eng. agríc. ambient.

View full text Add to dashboard Cite

A B S T R A C TFAO-Penman-Monteith (FAO-PM) is considered the standard method for the estimation of reference evapotranspiration (ET 0 ) but requires various meteorological data, which are often not available. The objective of this work was to evaluate the performance of the FAO-PM method with limited meteorological data and other methods as alternatives to estimate ET 0 in Jaíba-MG. The study used daily meteorological data from 2007 to 2016 of the National Institute of Meteorology's station. Daily ET 0 values were randomized, and 70% of these were used to determine the calibration parameters of the ET 0 for the equations of each method under study. The remaining data were used to test the calibration against the standard method. Performance evaluation was based on Willmott's index of agreement, confidence coefficient and root-mean-square error. When one meteorological variable was missing, either solar radiation, relative air humidity or wind speed, or in the simultaneous absence of wind speed and relative air humidity, the FAO-PM method showed the best performances and, therefore, was recommended for Jaíba. The FAO-PM method with two missing variables, one of them being solar radiation, showed intermediate performance.Methods that used only air temperature data are not recommended for the region.

show abstract

“…Extraterrestrial radiation, air temperature, and humidity were used in [7] to estimate potential evapotranspiration and to assess future climate change effects on the vegetation.…”

Section: Introductionmentioning

confidence: 99%

Linear Optimization Model for Efficient Use of Irrigation Water

Difallah

Benahmed

Draoui

et al. 2017

International Journal of Agronomy

View full text Add to dashboard Cite

The implementation of innovative and efficient irrigation techniques is among the greatest challenges facing agriculture. In this regard, a linear programming model is presented in order to optimize water use. The idea behind this model is to assess the effectiveness or ineffectiveness of precipitation to determine the amount of irrigation water required to optimize water use. To achieve this idea, the "knapsack" problem decisional form was used, and the combination of the linear programming and the above-mentioned form proved satisfactory. Field experiments were conducted in Algeria. Based on calculated budgets a model using linear programming was developed. A comparison between the model results and the field findings suggests that the model could reduce water consumption by 28.5%.

show abstract

Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables

Cited by 166 publications

References 36 publications

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Performance of different methods for reference evapotranspiration estimation in Jaíba, Brazil

Linear Optimization Model for Efficient Use of Irrigation Water

Contact Info

Product

Resources

About