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Meteorology

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BiLSTM-I: A Deep Learning-Based Long Interval Gap-Filling Method for Meteorological Observation Data.

International journal of environmental research and public health
Complete and high-resolution temperature observation data are important input parameters for agrometeorological disaster monitoring and ecosystem modelling. Due to the limitation of field meteorological observation conditions, observation data are co...

Drought Assessment Based on Data Fusion and Deep Learning.

Computational intelligence and neuroscience
Drought is a major factor affecting the sustainable development of society and the economy. Research on drought assessment is of great significance for formulating drought emergency policies and drought risk early warning and enhancing the ability to...

Using multiple linear regression and BP neural network to predict critical meteorological conditions of expressway bridge pavement icing.

PloS one
Icy bridge deck in winter has tremendous consequences for expressway traffic safety, which is closely related to the bridge pavement temperature. In this paper, the critical meteorological conditions of icy bridge deck were predicted by multiple line...

A novel combined model for prediction of daily precipitation data using instantaneous frequency feature and bidirectional long short time memory networks.

Environmental science and pollution research international
Meteorological events constantly affect human life, especially the occurrence of excessive precipitation in a short time causes important events such as floods. However, in case of insufficient precipitation for a long time, drought occurs. In recent...

Improving generalisation capability of artificial intelligence-based solar radiation estimator models using a bio-inspired optimisation algorithm and multi-model approach.

Environmental science and pollution research international
One way of reducing environmental pollution is to reduce our dependence on fossil fuels by replacing them with solar radiation (Rs), which is one of the main sources of clean and renewable energy. In this study, daily Rs values at seven meteorologica...

Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data.

Environmental monitoring and assessment
Accurate prediction of the reference evapotranspiration (ET) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinatio...

ECMWF short-term prediction accuracy improvement by deep learning.

Scientific reports
This paper aims to describe and evaluate the proposed calibration model based on a neural network for post-processing of two essential meteorological parameters, namely near-surface air temperature (2 m) and 24 h accumulated precipitation. The main i...

Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods.

Sensors (Basel, Switzerland)
Forecasting road flow has strong importance for both allowing authorities to guarantee safety conditions and traffic efficiency, as well as for road users to be able to plan their trips according to space and road occupation. In a summer resort, such...

Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter.

Environmental pollution (Barking, Essex : 1987)
From hourly ozone observations obtained from three regions⸻Houston, Dallas, and West Texas⸻we investigated the contributions of meteorology to changes in surface daily maximum 8-h average (MDA8) ozone from 2000 to 2019. We applied a deep convolutiona...

Prediction of daily mean and one-hour maximum PM concentrations and applications in Central Mexico using satellite-based machine-learning models.

Journal of exposure science & environmental epidemiology
BACKGROUND: Machine-learning algorithms are becoming popular techniques to predict ambient air PM concentrations at high spatial resolutions (1 × 1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to pred...