Machine learning in agriculture: A comprehensive updated review
The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …
artificial intelligent systems for the sake of making value from the ever-increasing data …
An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research …
Despite the massive diversity in the modeling requirements for practical hydrological
applications, there remains a need to develop more reliable and intelligent expert systems …
applications, there remains a need to develop more reliable and intelligent expert systems …
Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization
Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes.
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …
Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions
Accurate estimation of reference evapotranspiration (ET 0) is critical for water resource
management and irrigation scheduling. This study evaluated the potential of a new machine …
management and irrigation scheduling. This study evaluated the potential of a new machine …
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 …
A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping
Flash flood is a typical natural hazard that occurs within a short time with high flow velocities
and is difficult to predict. In this study, we propose and validate a new soft computing …
and is difficult to predict. In this study, we propose and validate a new soft computing …
Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data
Accurate estimation of reference evapotranspiration (ETo) is required in many fields, eg
irrigation scheduling design, agricultural water management, crop growth modeling and …
irrigation scheduling design, agricultural water management, crop growth modeling and …
Evaluation of stacking and blending ensemble learning methods for estimating daily reference evapotranspiration
Precise reference evapotranspiration (ETo) estimation and prediction are the first steps to
realize efficient agricultural water resources management. As machine learning methods are …
realize efficient agricultural water resources management. As machine learning methods are …
Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling
Y Feng, N Cui, D Gong, Q Zhang, L Zhao - Agricultural Water Management, 2017 - Elsevier
Accurate estimation of reference evapotranspiration (ET 0) is of importance for regional
water resource management. The present study proposed two artificial intelligence models …
water resource management. The present study proposed two artificial intelligence models …