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 …
Machine learning applications for precision agriculture: A comprehensive review
Agriculture plays a vital role in the economic growth of any country. With the increase of
population, frequent changes in climatic conditions and limited resources, it becomes a …
population, frequent changes in climatic conditions and limited resources, it becomes a …
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 …
Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques
IMK Ho, KY Cheong, A Weldon - Plos one, 2021 - journals.plos.org
Despite the wide adoption of emergency remote learning (ERL) in higher education during
the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting …
the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting …
Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques
This study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning
(EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were …
(EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were …
Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR …
Agriculture, meteorological, and hydrological drought is a natural hazard which affects
ecosystems in the central India of Maharashtra state. Due to limited historical data for …
ecosystems in the central India of Maharashtra state. Due to limited historical data for …
A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem
Hydrological models play a crucial role in water planning and decision making. Machine
Learning-based models showed several drawbacks for frequent high and a wide range of …
Learning-based models showed several drawbacks for frequent high and a wide range of …
[HTML][HTML] Utilizing time series data from 1961 to 2019 recorded around the world and machine learning to create a Global Temperature Change Prediction Model
SM Malakouti - Case Studies in Chemical and Environmental …, 2023 - Elsevier
Since 1880, the Earth's temperature has increased at a pace of 0.14° Fahrenheit (0.08°
Celsius) every decade; however, the rate of warming since 1981 is more than double that, at …
Celsius) every decade; however, the rate of warming since 1981 is more than double that, at …
Smart farming prediction models for precision agriculture: a comprehensive survey
Considering the variability of the farming resources such as soil, fertilizer and weather
conditions including crops. Proper utilization of these resources for high yield is paramount …
conditions including crops. Proper utilization of these resources for high yield is paramount …
Flash-flood hazard susceptibility map** in Kangsabati River Basin, India
Flood-susceptibility map** is an important component of flood risk management to control
the effects of natural hazards and prevention of injury. We used a remote-sensing and …
the effects of natural hazards and prevention of injury. We used a remote-sensing and …