AIMC Topic: Climate

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Estimating severe fever with thrombocytopenia syndrome transmission using machine learning methods in South Korea.

Scientific reports
Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infectious disease in China, Japan, and Korea. This study aimed to estimate the monthly SFTS occurrence and the monthly number of SFTS cases in the geographical area in Kore...

Spatial Prediction of COVID-19 in China Based on Machine Learning Algorithms and Geographically Weighted Regression.

Computational and mathematical methods in medicine
COVID-19 has swept through the world since December 2019 and caused a large number of patients and deaths. Spatial prediction on the spread of the epidemic is greatly important for disease control and management. In this study, we predicted the cumul...

Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future.

Journal of environmental management
The predicts current and future flood risk in the Kalvan watershed of northwestern Markazi Province, Iran. To do this, 512 flood and non-flood locations were identified and mapped. Twenty flood-risk factors were selected to model flood risk using sev...

Spatial prediction of human brucellosis (HB) using a GIS-based adaptive neuro-fuzzy inference system (ANFIS).

Acta tropica
OBJECTIVE: This study pursues three main objectives: 1) exploring the spatial distribution patterns of human brucellosis (HB); 2) identifying parameters affecting the disease spread; and 3) modeling and predicting the spatial distribution of HB cases...

Determining suitable machine learning classifier technique for prediction of malaria incidents attributed to climate of Odisha.

International journal of environmental health research
This study investigated the influence of climate factors on malaria incidence in the Sundargarh district, Odisha, India. The WEKA machine learning tool was used with two classifier techniques, Multi-Layer Perceptron (MLP) and J48, with three test opt...

Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt.

Scientific reports
This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions...

The Prediction of Hepatitis E through Ensemble Learning.

International journal of environmental research and public health
According to the World Health Organization, about 20 million people are infected with Hepatitis E every year. In 2015, there were 44,000 deaths due to HEV infection worldwide. Food, water and climate are key factors that affect the outbreak of Hepati...

The Influence of Climate Parameters on Maintenance of Wind Farms-A Galician Case Study.

Sensors (Basel, Switzerland)
There are different monitoring procedures in wind farms with two main objectives: (i) to improve energy production by the capability of the national electrical network and (ii) to reduce the stooped hours due to preventive and or corrective maintenan...

Cherry Tomato Production in Intelligent Greenhouses-Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality.

Sensors (Basel, Switzerland)
Greenhouses and indoor farming systems play an important role in providing fresh and nutritious food for the growing global population. Farms are becoming larger and greenhouse growers need to make complex decisions to maximize production and minimiz...

Improving area of occupancy estimates for parapatric species using distribution models and support vector machines.

Ecological applications : a publication of the Ecological Society of America
As geographic range estimates for the IUCN Red List guide conservation actions, accuracy and ecological realism are crucial. IUCN's extent of occurrence (EOO) is the general region including the species' range, while area of occupancy (AOO) is the su...