Prediction of actual evapotranspiration by artificial neural network models using data from a Bowen ratio energy balance station

Walls, Spencer; Binns, Andrew D.; Levison, Jana; MacRitchie, Scott

doi:10.1007/s00521-020-04800-2

Cited by 29 publications

(6 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the development of ET 0 model with fewer climatic inputs (e.g., temperature data) is mainly requisite. For this purpose, different types of machine learning algorithms were developed in ET 0 modeling, for example, support vector machine (SVM) (Kisi and Imen 2009;Kushwaha et al 2021;Mehdizadeh et al 2017;Ferreiraand and Cunha 2020), least square support vector machine (Kisi 2013;Guo et al 2011), genetic programming (Traore et al 2016;Valipour et al 2019;Mattar 2018), extreme learning machine (ELM) (Shamshirband and Kamsin 2016;Abdullah et al 2015), tree-based models (Raza et al 2021a, b) such as M5 model tree (Fan et al 2018a, b;Granata 2019), random forest (Feng et al 2017a, b;Fang et al 2018;Saggi and Jain 2019), and extreme gradient boosting (XGBoost) (Ferreira and Cunha 2020;Fan et al 2018a, b;Han et al 2019), artificial neural networks (ANNs) (Torres et al 2011;Tang et al 2018;Walls et al 2020), and adaptive neuro-fuzzy inference system (ANFIS) (Nourani et al 2019;Tabari et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration

Elbeltagi

Raza

et al. 2022

Appl Water Sci

View full text Add to dashboard Cite

For developing countries, scarcity of climatic data is the biggest challenge, and model development with limited meteorological input is of critical importance. In this study, five data intelligent and hybrid metaheuristic machine learning algorithms, namely additive regression (AR), AR-bagging, AR-random subspace (AR-RSS), AR-M5P, and AR-REPTree, were applied to predict monthly mean daily reference evapotranspiration (ET0). For this purpose, climatic data of two meteorological stations located in the semi-arid region of Pakistan were used from the period 1987 to 2016. The climatic dataset includes maximum and minimum temperature (Tmax, Tmin), average relative humidity (RHavg), average wind speed (Ux), and sunshine hours (n). Sensitivity analysis through regression methods was applied to determine effective input climatic parameters for ET0 modeling. The results of performed regression analysis on all input parameters proved that Tmin, RHAvg, Ux, and n were identified as the most influential input parameters at the studied station. From the results, it was revealed that all the selected models predicted ET0 at both stations with greater precision. The AR-REPTree model was located furthest and the AR-M5P model was located nearest to the observed point based on the performing indices at both the selected meteorological stations. The study concluded that under the aforementioned methodological framework, the AR-M5P model can yield higher accuracy in predicting ET0 values, as compared to other selected algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%

Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration

Elbeltagi

Raza

et al. 2022

Appl Water Sci

View full text Add to dashboard Cite

show abstract

“…The results showed that the SVM model had better performance than the other models. Walls et al [30] used different ANN structures for modeling the ETo and reported that the ANN model has good accuracy for predicting ETo. Kaya et al [31] modeled ETo using multilayer perceptron, support vector regression and multilinear regression models.…”

Section: Introductionmentioning

confidence: 99%

A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods

et al. 2022

View full text Add to dashboard Cite

In the present study, a new methodology for reference evapotranspiration (ETo) prediction and uncertainty analysis under climate change and COVID-19 post-pandemic recovery scenarios for the period 2021–2050 at nine stations in the two basins of Lake Urmia and Sefidrood is presented. For this purpose, firstly ETo data were estimated using meteorological data and the FAO Penman–Monteith (FAO-56 PM) method. Then, ETo modeling by six machine learning techniques including multiple linear regression (MLR), multiple non-linear regression (MNLR), multivariate adaptive regression splines (MARS), model tree M5 (M5), random forest (RF) and least-squares boost (LSBoost) was carried out. The technique for order of preference by similarity to ideal solution (TOPSIS) method was used under seven scenarios to rank models with evaluation and time criteria in the next step. After proving the acceptable performance of the LSBoost model, the downscaling of temperature (T) and precipitation (P) by the delta change factor (CF) method under three models ACCESS-ESM1-5, CanESM5 and MRI-ESM2-0 (scenarios SSP245-cov-fossil (SCF), SSP245-cov-modgreen (SCM) and SSP245-cov-strgreen (SCS)) was performed. The results showed that the monthly changes in the average T increases at all stations for all scenarios. Also, the average monthly change ratio of P increases in most stations and scenarios. In the next step, ETo forecasting under climate change for periods (2021–2050) was performed using the best model. Prediction results showed that ETo increases in all scenarios and stations in a pessimistic and optimistic state. In addition, the Monte Carlo method (MCM) showed that the lowest uncertainty is related to the Mianeh station in the MRI-ESM2-0 model and SCS scenario.

show abstract

“…More importantly, optimization algorithms such as stochastic gradient descent (SGD), root mean square propagation (RMSprop), adaptive grad (AdaGrad), adaptive delta (AdaDelta), adaptive moment estimation (Adam), adaptive maximum (Adamax), and Nesterov‐accelerated adaptive moment estimation (Nadam) are absent in the typhoon rainfall forecasting model based on the DL model (Huang et al., 2018; Lin & Chen, 2005; Lin & Wu, 2009; Wei & Chou, 2020). These optimization algorithms have successfully been applied in rainfall forecasting (Barrera‐Animas et al., 2021; Fadilah et al., 2021; Manoj & Ananth, 2020; Prasetya & Djamal, 2019; Sari et al., 2020; Zhang et al., 2018), spatial prediction of landslides (Bui et al., 2019), wind speed and wind direction forecasting (Puspita Sari et al., 2020; Saputri et al., 2020), evapotranspiration forecasting (Walls et al., 2020), run‐off forecasting (Nath et al., 2021), air quality index prediction (H. He & Luo, 2020), river stage, flash flood susceptibility and streamflow forecasting (Hitokoto et al., 2017; Rahimzad et al., 2021; Tien Bui et al., 2020), water demand forecasting (Koo et al., 2021), temperature and global solar radiation prediction (Del & Starchenko, 2021; Ghimire et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Effects of Learning Rates and Optimization Algorithms on Forecasting Accuracy of Hourly Typhoon Rainfall: Experiments With Convolutional Neural Network

Uddin

Sattar

et al. 2022

Earth and Space Science

View full text Add to dashboard Cite

show abstract

Prediction of actual evapotranspiration by artificial neural network models using data from a Bowen ratio energy balance station

Cited by 29 publications

References 50 publications

Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration

Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration

A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods

Effects of Learning Rates and Optimization Algorithms on Forecasting Accuracy of Hourly Typhoon Rainfall: Experiments With Convolutional Neural Network

Contact Info

Product

Resources

About