Remote sensing and machine learning for crop water stress determination in various crops: a critical review
The remote sensing (RS) technique is less cost-and labour-intensive than ground-based
surveys for diverse applications in agriculture. Machine learning (ML), a branch of artificial …
surveys for diverse applications in agriculture. Machine learning (ML), a branch of artificial …
Recent advances in evapotranspiration estimation using artificial intelligence approaches with a focus on hybridization techniques—a review
Difficulties are faced when formulating hydrological processes, including that of
evapotranspiration (ET). Conventional empirical methods for formulating these possess …
evapotranspiration (ET). Conventional empirical methods for formulating these possess …
Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates …
Accurate estimation of reference evapotranspiration (ET 0) is of great importance for the
regional water resources planning and irrigation scheduling design. The FAO-56 Penman …
regional water resources planning and irrigation scheduling design. The FAO-56 Penman …
Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods
Z Chen, Z Zhu, H Jiang, S Sun - Journal of Hydrology, 2020 - Elsevier
To evaluate the performance of deep learning methods (DL) for reference
evapotranspiration estimation and to assess the applicability of the developed DL models …
evapotranspiration estimation and to assess the applicability of the developed DL models …
Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling
Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles.
However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed …
However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed …
Physics‐constrained machine learning of evapotranspiration
Estimating ecosystem evapotranspiration (ET) is important to understanding the global water
cycle and to study land‐atmosphere interactions. We developed a physics constrained …
cycle and to study land‐atmosphere interactions. We developed a physics constrained …
A novel hybrid system based on multi-objective optimization for wind speed forecasting
C Wu, J Wang, X Chen, P Du, W Yang - Renewable energy, 2020 - Elsevier
Wind power has demonstrated high-efficiency utilization in electricity system, accordingly,
accurate and stable forecasting of wind speed is of vital significance in power grid security …
accurate and stable forecasting of wind speed is of vital significance in power grid security …
Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data
Accurate estimation of reference evapotranspiration (ET 0) is essential to agricultural water
management. The present study developed two artificial intelligence models for daily ET 0 …
management. The present study developed two artificial intelligence models for daily ET 0 …
Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)
J Yin, Z Deng, AVM Ines, J Wu, E Rasu - Agricultural Water Management, 2020 - Elsevier
As the standard method to compute reference evapotranspiration (ET 0), Penman-Monteith
(PM) method requires eight meteorological input variables, which makes it difficult to apply …
(PM) method requires eight meteorological input variables, which makes it difficult to apply …
Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling
D Chakraborty, H Başağaoğlu, J Winterle - Expert Systems with …, 2021 - Elsevier
Due to their enhanced predictive capabilities, noninterpretable machine learning (ML)
models (eg deep learning) have recently gained a growing interest in analyzing and …
models (eg deep learning) have recently gained a growing interest in analyzing and …