AIMC Topic: Meteorology

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Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

Environmental science and pollution research international Jul 6, 2016
In this paper, we predict air pollutant concentration using a feedforward artificial neural network inspired by the mechanism of the human brain as a useful alternative to traditional statistical modeling techniques. The neural network is trained bas...
Humans Neural Networks, Computer Algorithms Environmental Monitoring Models, Statistical Air Pollutants Air Pollution Linear Models Hong Kong Meteorology
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Using self-organizing maps to infill missing data in hydro-meteorological time series from the Logone catchment, Lake Chad basin.

Environmental monitoring and assessment Jun 9, 2016
Hydro-meteorological data is an important asset that can enhance management of water resources. But existing data often contains gaps, leading to uncertainties and so compromising their use. Although many methods exist for infilling data gaps in hydr...
Neural Networks, Computer Artificial Intelligence Algorithms Lakes Environmental Monitoring Rivers Meteorology Chad
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Artificial neural network models for prediction of daily fine particulate matter concentrations in Algiers.

Environmental science and pollution research international Apr 4, 2016
Neural network (NN) models were evaluated for the prediction of suspended particulates with aerodynamic diameter less than 10-μm (PM10) concentrations. The model evaluation work considered the sequential hourly concentration time series of PM10, whic...
Neural Networks, Computer Forecasting Models, Theoretical Environmental Monitoring Particle Size Air Pollutants Particulate Matter Algeria Meteorology
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Comparison of hybrid machine learning models to predict short-term meteorological drought in Guanzhong region, China.

Water science and technology : a journal of the International Association on Water Pollution Research Jun 1, 2023
Reliable drought prediction plays a significant role in drought management. Applying machine learning models in drought prediction is getting popular in recent years, but applying the stand-alone models to capture the feature information is not suffi...
Machine Learning Algorithms China Droughts Meteorology
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How machine learning could help to improve climate forecasts.

Nature Aug 23, 2017
Machine Learning Algorithms Forecasting Models, Theoretical Weather Climate Global Warming Meteorology
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Daily runoff prediction using the linear and non-linear models.

Water science and technology : a journal of the International Association on Water Pollution Research Aug 1, 2017
Runoff prediction, as a nonlinear and complex process, is essential for designing canals, water management and planning, flood control and predicting soil erosion. There are a number of techniques for runoff prediction based on the hydro-meteorologic...
Neural Networks, Computer Support Vector Machine Nonlinear Dynamics Meteorology
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