Related references
Note: Only part of the references are listed.Forecasting of Extreme Storm Tide Events Using NARX Neural Network-Based Models
Fabio Di Nunno et al.
ATMOSPHERE (2021)
Prediction of spring flows using nonlinear autoregressive exogenous (NARX) neural network models
Fabio Di Nunno et al.
ENVIRONMENTAL MONITORING AND ASSESSMENT (2021)
Artificial intelligence based approaches to evaluate actual evapotranspiration in wetlands
Francesco Granata et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2020)
Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data
Sevim Seda Yamac et al.
AGRICULTURAL WATER MANAGEMENT (2020)
Temporal convolution-network-based models for modeling maize evapotranspiration under mulched drip irrigation
Zhijun Chen et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)
Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration
Yazid Tikhamarine et al.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2020)
Comparative Study of Time Series Models, Support Vector Machines, and GMDH in Forecasting Long-Term Evapotranspiration Rates in Northern Iran
Afshin Ashrafzadeh et al.
JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING (2020)
Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression integrated with whale optimization algorithm
Yazid Tikhamarine et al.
ENVIRONMENTAL MONITORING AND ASSESSMENT (2020)
Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods
Zhijun Chen et al.
JOURNAL OF HYDROLOGY (2020)
Multi-step ahead forecasting of daily reference evapotranspiration using deep learning
Lucas Borges Ferreira et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)
Evapotranspiration evaluation models based on machine learning algorithms-A comparative study
Francesco Granata
AGRICULTURAL WATER MANAGEMENT (2019)
Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches
Yazid Tikhamarine et al.
HYDROLOGICAL SCIENCES JOURNAL (2019)
The viability of co-active fuzzy inference system model for monthly reference evapotranspiration estimation: case study of Uttarakhand State
Anurag Malik et al.
HYDROLOGY RESEARCH (2019)
Physics-Constrained Machine Learning of Evapotranspiration
Wen Li Zhao et al.
GEOPHYSICAL RESEARCH LETTERS (2019)
Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems
Xianming Dou et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)
Modelling reference evapotranspiration using a new wavelet conjunction heuristic method: Wavelet extreme learning machine vs wavelet neural networks
Ozgur Kisi et al.
AGRICULTURAL AND FOREST METEOROLOGY (2018)
Quantifying soybean evapotranspiration using an eddy covariance approach
Saseendran S. Anapalli et al.
AGRICULTURAL WATER MANAGEMENT (2018)
Evaluating Diffferent Machine Learning Methods for Upscaling Evapotranspiration from Flux Towers to the Regional Scale
Tongren Xu et al.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES (2018)
Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data
Yu Feng et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2017)
Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts
Yufeng Luo et al.
WATER RESOURCES MANAGEMENT (2015)
Fraction of canopy intercepted radiation relates differently with crop coefficient depending on the season and the fruit tree species
Jordi Marsal et al.
AGRICULTURAL AND FOREST METEOROLOGY (2014)
Deep Learning: Methods and Applications
Li Deng et al.
FOUNDATIONS AND TRENDS IN SIGNAL PROCESSING (2013)
Implementing the dual crop coefficient approach in interactive software. 1. Background and computational strategy
Ricardo D. Rosa et al.
AGRICULTURAL WATER MANAGEMENT (2012)
Forecasting daily potential evapotranspiration using machine learning and limited climatic data
Alfonso F. Torres et al.
AGRICULTURAL WATER MANAGEMENT (2011)
Forecasting Weekly Evapotranspiration with ARIMA and Artificial Neural Network Models
Gorka Landeras et al.
JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING (2009)
Utility of Penman-Monteith, Priestley-Taylor, reference evapotranspiration, and pan evaporation methods to estimate pasture evapotranspiration
DM Sumner et al.
JOURNAL OF HYDROLOGY (2005)
Forecasting of reference evapotranspiration by artificial neural networks
S Trajkovic et al.
JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING (2003)
Seasonal and interannual variation in evapotranspiration, energy balance and surface conductance in a northern temperate grassland
LA Wever et al.
AGRICULTURAL AND FOREST METEOROLOGY (2002)
Learning to forget: Continual prediction with LSTM
FA Gers et al.
NEURAL COMPUTATION (2000)
Share Paper
