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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...

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...

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...

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...

MODWT-ANN hybrid models for daily precipitation estimates with time-delayed entries in Amazon region.

Environmental monitoring and assessment
Hydrological analyses based on precipitation records in the Amazon are essential due to their importance in climate regulation and regional and global atmospheric circulation. However, there are limitations related to data series with short periods a...

Causal discovery of drivers of surface ozone variability in Antarctica using a deep learning algorithm.

Environmental science. Processes & impacts
The discovery of causal structures behind a phenomenon under investigation has been at the heart of scientific inquiry since the beginning. Randomized control trials, the gold standard for causal analysis, may not always be feasible, such as in the d...

Rainfall prediction using multiple inclusive models and large climate indices.

Environmental science and pollution research international
Rainfall prediction is vital for the management of available water resources. Accordingly, this study used large lagged climate indices to predict rainfall in Iran's Sefidrood basin. A radial basis function neural network (RBFNN) and a multilayer per...

Mapping Local Climate Zones in the Urban Environment: The Optimal Combination of Data Source and Classifier.

Sensors (Basel, Switzerland)
The novel concept of local climate zones (LCZs) provides a consistent classification framework for studies of the urban thermal environment. However, the development of urban climate science is severely hampered by the lack of high-resolution data to...

Climate and genetic data enhancement using deep learning analytics to improve maize yield predictability.

Journal of experimental botany
Despite efforts to collect genomics and phenomics ('omics') and environmental data, spatiotemporal availability and access to digital resources still limit our ability to predict plants' response to changes in climate. Our goal is to quantify the imp...

Deep learning approach for prediction of exergy and emission parameters of commercial high by-pass turbofan engines.

Environmental science and pollution research international
Aviation emissions originated from the fuel burn have been hot topics by engineers and policy-makers due to their harmful effects on the environment and thereby human health as well as sustainability. In this study, it is tried that several emission ...