A two-step combined algorithm based on NARX neural network and the subsequent prediction of the residues improves prediction accuracy of the greenhouse gases concentrations

Deep belief network-based AR model for nonlinear time series forecasting

(2019) Wenquan Xu et al.   APPLIED SOFT COMPUTING

Dynamic effect analysis of meteorological conditions on air pollution: A case study from Beijing

(2019) Yongli Zhang   SCIENCE OF THE TOTAL ENVIRONMENT

Time series analysis of wintertime O3 and NOx formation using vector autoregressions

(2019) David A. Olson et al.   ATMOSPHERIC ENVIRONMENT

High variation topsoil pollution forecasting in the Russian Subarctic: Using artificial neural networks combined with residual kriging

(2018) D.A. Tarasov et al.   APPLIED GEOCHEMISTRY

Growth of industrial CO 2 emissions in Shanghai city: Evidence from a dynamic vector autoregression analysis

(2018) Boqiang Lin et al.   ENERGY

A modified Elman neural network with a new learning rate scheme

(2018) Guanghua Ren et al.   NEUROCOMPUTING

CO2 Emissions in China’s Yangtze River Economic Zone: A Dynamic Vector Autoregression Approach

(2018) Lei Wen et al.   POLISH JOURNAL OF ENVIRONMENTAL STUDIES

Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals

(2018) A.P. Sergeev et al.   CATENA

Entropy based statistical inference for methane emissions released from wetland

(2015) R. Sabolová et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau

(2014) Fuqiang Dai et al.   ECOLOGICAL INDICATORS

A hybrid model for PM 2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network

(2014) Qingping Zhou et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Air quality prediction using optimal neural networks with stochastic variables

(2013) Ana Russo et al.   ATMOSPHERIC ENVIRONMENT

Microergodicity effects on ebullition of methane modelled by Mixed Poisson process with Pareto mixing variable

(2013) P. Jordanova et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Forecasting PM10 in metropolitan areas: Efficacy of neural networks

(2012) H.J.S. Fernando et al.   ENVIRONMENTAL POLLUTION

The prediction of meteorological variables using artificial neural network

(2012) Ahmet Erdil et al.   NEURAL COMPUTING & APPLICATIONS

How bias-correction can improve air quality forecasts over Portugal

(2011) C. Borrego et al.   ATMOSPHERIC ENVIRONMENT

Processes and impacts of Arctic amplification: A research synthesis

(2011) Mark C. Serreze et al.   GLOBAL AND PLANETARY CHANGE

A refined index of model performance

(2011) Cort J. Willmott et al.   INTERNATIONAL JOURNAL OF CLIMATOLOGY

Using Statistical Regressions to Identify Factors Influencing PM2.5Concentrations: The Pittsburgh Supersite as a Case Study

(2010) Nanjun Chu et al.   AEROSOL SCIENCE AND TECHNOLOGY

An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

(2010) W. Geoffrey Cobourn   ATMOSPHERIC ENVIRONMENT

Defining and applying prediction performance metrics on a recurrent NARX time series model

(2010) Ryad Zemouri et al.   NEUROCOMPUTING

Chaotic time series prediction with residual analysis method using hybrid Elman–NARX neural networks

(2010) Muhammad Ardalani-Farsa et al.   NEUROCOMPUTING

Forecasting peak air pollution levels using NARX models

(2009) Enrico Pisoni et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Air pollution forecasting in Ankara, Turkey using air pollution index and its relation to assimilative capacity of the atmosphere

(2009) D. Deniz Genc et al.   ENVIRONMENTAL MONITORING AND ASSESSMENT

Long-term time series prediction with the NARX network: An empirical evaluation

(2008) José Maria P. Menezes et al.   NEUROCOMPUTING