AIMC Topic: Climate
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Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network.
BMC bioinformatics
Dec 24, 2019
BACKGROUND: The occurrence of cotton pests and diseases has always been an important factor affecting the total cotton production. Cotton has a great dependence on environmental factors during its growth, especially climate change. In recent years, m...
Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach.
Computational and mathematical methods in medicine
Nov 15, 2019
Emergency departments (EDs) play a vital role in the whole healthcare system as they are the first point of care in hospitals for urgent and critically ill patients. Therefore, effective management of hospital's ED is crucial in improving the quality...
Prediction mapping of human leptospirosis using ANN, GWR, SVM and GLM approaches.
BMC infectious diseases
Nov 13, 2019
BACKGROUND: Recent reports of the National Ministry of Health and Treatment of Iran (NMHT) show that Gilan has a higher annual incidence rate of leptospirosis than other provinces across the country. Despite several efforts of the government and NMHT...
Spatiotemporal dengue fever hotspots associated with climatic factors in Taiwan including outbreak predictions based on machine-learning.
Geospatial health
Nov 6, 2019
Early warning systems (EWS) have been proposed as a measure for controlling and preventing dengue fever outbreaks in countries where this infection is endemic. A vaccine is not available and has yet to reach the market due to the economic burden of d...
Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches.
PloS one
Oct 10, 2019
In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), ...
Artificial intelligence reveals environmental constraints on colour diversity in insects.
Nature communications
Oct 7, 2019
Explaining colour variation among animals at broad geographic scales remains challenging. Here we demonstrate how deep learning-a form of artificial intelligence-can reveal subtle but robust patterns of colour feature variation along an ecological gr...
Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration.
PloS one
May 31, 2019
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)
Apr 16, 2019
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
Mar 7, 2019
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)
Oct 13, 2018
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 ...
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