AIMC Topic: Climate

Clear Filters Showing 71 to 80 of 92 articles

Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration.

PloS one
Accurately predicting reference evapotranspiration (ET0) with limited climatic data is crucial for irrigation scheduling design and agricultural water management. This study evaluated eight machine learning models in four categories, i.e. neuron-base...

Remote Control of Greenhouse Vegetable Production with Artificial Intelligence-Greenhouse Climate, Irrigation, and Crop Production.

Sensors (Basel, Switzerland)
The global population is increasing rapidly, together with the demand for healthy fresh food. The greenhouse industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI...

Using artificial neural networks to predict future dryland responses to human and climate disturbances.

Scientific reports
Land degradation and sediment remobilisation in dryland environments is considered to be a significant global environmental problem. Given the potential for currently stabilised dune systems to reactivate under climate change and increased anthropoge...

Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks.

Environmental pollution (Barking, Essex : 1987)
Urban population exposure to tropospheric ozone is a serious health concern in Europe countries. Although there are insufficient evidence to derive a level below which ozone has no effect on mortality WHO (World Health Organization) uses SOMO35 (sum ...

A bioavailable strontium isoscape for Western Europe: A machine learning approach.

PloS one
Strontium isotope ratios (87Sr/86Sr) are gaining considerable interest as a geolocation tool and are now widely applied in archaeology, ecology, and forensic research. However, their application for provenance requires the development of baseline mod...

Machine learning modeling of plant phenology based on coupling satellite and gridded meteorological dataset.

International journal of biometeorology
Changes in the timing of plant phenological phases are important proxies in contemporary climate research. However, most of the commonly used traditional phenological observations do not give any coherent spatial information. While consistent spatial...

The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison.

Journal of biomedical informatics
INTRODUCTION: Accurate and timely prediction for endemic infectious diseases is vital for public health agencies to plan and carry out any control methods at an early stage of disease outbreaks. Climatic variables has been identified as important pre...

Eyes on our planet.

Current biology : CB
Combining several satellite-based tracking technologies with big data methods and machine learning, fisheries experts can now efficiently monitor the entirety of the oceans and ensure that legal limits and protected areas are respected. Observing our...

Developing a dengue forecast model using machine learning: A case study in China.

PLoS neglected tropical diseases
BACKGROUND: In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine lea...

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

PloS one
Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effecti...