The pre-monsoon season heavily influences the precipitation amount in Pakistan. When hydrometeorological parameters interact with aerosols from multiple sources, a radiative climatic response is ...observed. In this study, aerosol optical depth (AOD) space-time dynamics were analyzed in relation to meteorological factors and surface parameters during the pre-monsoon season in the years 2002–2019 over Pakistan. Level-3 (L3) monthly datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectroradiometer (MISR) were used. Tropical Rainfall Measuring Mission (TRMM) derived monthly precipitation, Atmospheric Infrared Sounder (AIRS) derived air temperature, after moist relative humidity (RH) from Modern-Era Retrospective analysis for Research and Applications, Version-2 (MERRA-2), near-surface wind speed, and soil moisture data derived from Global Land Data Assimilation System (GLDAS) were also used on a monthly time scale. For AOD trend analysis, Mann-Kendall (MK) trend test was applied. Moreover, Autoregressive Integrated Moving Average with Explanatory variable (ARIMAX) technique was applied to observe the actual and predicted AOD trend, as well as test the multicollinearity of AOD with covariates. The periodicities of AOD were analyzed using continuous wavelet transformation (CWT) and the cross relationships of AOD with prevailing covariates on a time-frequency scale were analyzed by wavelet coherence analysis. A high variation of aerosols was observed in the spatiotemporal domain. The MK test showed a decreasing trend in AOD which was most significant in Baluchistan and Punjab, and the overall trend differs between MODIS and MISR datasets. ARIMAX model shows the correlation of AOD with varying meteorological and soil parameters. Wavelet analysis provides the abundance of periodicities in the 2–8 months periodic cycles. The coherency nature of the AOD time series along with other covariates manifests leading and lagging effects in the periodicities. Through this, a notable difference was concluded in space-time patterns between MODIS and MISR datasets. These findings may prove useful for short-term and long-term studies including oscillating features of AOD and covariates.
•The periodicities of AOD were analyzed using continuous wavelet transformation.•A high variation of aerosols was observed in the spatio-temporal domain.•The MK test shows a decreasing trend that expands under eastern regions of Pakistan.•Wavelet analysis provides the abundance of 2–4 and 4-8-months cycle.
In this paper, we used the Baidu feed index to quantify the valuable information from the search engine, and proposed a China's crude oil futures price forecasting model with feed index. The ...empirical analysis confirms that the feed index, which characterizes the investor attention of participants in China's crude oil futures market, provides valuable information for internet concerns and investor sentiment, and has significant impact on China's crude oil futures price forecasting. We found that the time lag between the search query results and the crude oil futures price changes, which mostly lies within the shorter time range. The forecasting model with the search engine data would produce forecasts with superior performance.
•The neural network is outperformed by the ARIMAX model in foreseeing peaks.•The neural net models the pollution dependency on non-linear factors realistically.•The forecast of extreme pollution is ...best performed by ensemble modelling (R=0.92).•Lowering by 20μg/m3 the alarm limit improves the model ability to predict exceedances (from 62 to 82%).
This paper illustrates the early results of ongoing research developing novel methods to analyse and simulate the relationship between trasport-related air pollutant concentrations and easily accessible explanatory variables. The final scope is to integrate the new models in traditional traffic management support systems for a sustainable mobility of road vehicles in urban areas.
This first stage concerns the relationship between the hourly mean concentration of nitrogen dioxide (NO2) and explanatory factors reflecting the NO2 mean level one hour back, along with traffic and weather conditions. Particular attention is given to the prediction of pollution peaks, defined as exceedances of normative concentration limits. Two model frameworks are explored: the Artificial Neural Network approach and the ARIMAX model. Furthermore, the benefit of a synergic use of both models for air quality forecasting is investigated.
The analysis of findings points out that the prediction of extreme concentrations is best performed by integrating the two models into an ensemble. The neural network is outperformed by the ARIMAX model in foreseeing peaks, but gives a more realistic representation of the concentration's dependency upon wind characteristics. So, the Neural Network can be exploited to highlight the involved functional forms and improve the ARIMAX model specification. In the end, the study shows that the ability to forecast exceedances of legal pollution limits can be enhanced by requiring traffic management actions when the predicted concentration exceeds a lower threshold than the normative one.
Hong Kong International Airport (HKIA) is one of the main gateways to Mainland China and the major aviation hub in Asia. An accurate airport traffic demand forecast allows for short and long-term ...planning and decision making regarding airport facilities and flight networks. This paper employs the Box–Jenkins Seasonal ARIMA (SARIMA) model and the ARIMAX model to forecast airport passenger traffic for Hong Kong, and projecting its future growth trend to 2015. Both models predict a steady growth in future airport passenger traffic at Hong Kong. In addition, scenario analysis suggests that Hong Kong airport's future passenger traffic will continue to grow in different magnitudes.
•The SARIMA and ARIMAX models were employed for forecasting air passenger throughput of Hong Kong until 2015.•Future air passenger traffic of Hong Kong is projected to grow under different scenarios (an average of 0.5–0.8% per month).•Both forecasting models are highly accurate with smaller forecasting errors.•Future air passenger numbers travelling to Hong Kong from seven markets are likely to increase.•Negative growth in air passenger numbers is predicted for Africa, Mainland China, and Taiwan.
This research shows the connection between climate change and its impact on the production of sugarcane in India. For this research, time series data on sugarcane production and monsoon rains in ...India were examined over a period of 60 years from 1960 to 2019. The six different models of time series, mean forecasting model, naive model, simple exponential smoothing model, Holt’s model, autoregressive integrated moving average (ARIMA) model, and autoregressive integrated moving average with explanatory variable (ARIMAX) model were employed to investigate the sustainability of empirical findings. The best model was selected by comparing the lowest forecast errors of root mean square error (RMSE), mean absolute error (MAE), and mean absolute scaled error (MASE), which were 2351.98, 1750.39, and 0.7966383, respectively, for the ARIMA model, which was smaller than the values obtained by all other time series models. Therefore, the ARIMA (2,1,1) model was considered the most fruitful model among all other models. Compared to the ARIMAX model, the ARIMA (2,1,1) model was found to be the best fit due to its lower Akaike’s information criterion (AIC) value which was 1097.616. Therefore, ARIMA (2,1,1) model can be applied to forecast sugarcane production in the next decade, and the production has been assessed to be around 34,221.43 million tonnes.
•We modeled monthly direct runoff coefficients to forecast a holdout dataset.•Initially, we used univariate ARIMA, multivariate ARIMAX, and ANN models.•We found the applied traditional model ...performances insufficient.•We developed a new Hybrid approach by using time series decomposition and ANN.•We found that the new generated model is superior, suitable for complicated data.
In this study, monthly runoff coefficients of seven southern large basins are calculated and modeled to forecast a holdout dataset by using univariate autoregressive integrated moving average (ARIMA), multivariate ARIMA (ARIMAX), and Artificial neural network (ANN) models. The applied traditional model performances are found insufficient, since the characteristic behaviors of the time series of direct runoff coefficients are very complicated. Therefore, a new Hybrid approach is adopted by using time series decomposition procedure and ANN. ARIMA, ARIMAX, ANN, and Hybrid models are compared with each other. The results indicate that the new generated Hybrid approach can be generalized to boost the prediction capability of ANNs in complicated time series data. It is seen that the new model captures the physical behavior of the direct runoff coefficient time series. The semi-random spikes of the direct runoff coefficient series are approximated sufficiently.
In recent 2 years, the incidence of influenza showed a slight upward trend in Guangxi; therefore, some joint actions should be done to help preventing and controlling this disease. The factors ...analysis of affecting influenza and early prediction of influenza incidence may help policy-making so as to take effective measures to prevent and control influenza. In this study, we used the cross correlation function (CCF) to analyze the effect of climate indicators on influenza incidence, ARIMA and ARIMAX (autoregressive integrated moving average model with exogenous input variables) model methods to do predictive analysis of influenza incidence. The results of CCF analysis showed that climate indicators (PM2.5, PM10, SO
2
, CO, NO2, O
3
, average temperature, maximum temperature, minimum temperature, average relative humidity, and sunshine duration) had significant effects on the incidence of influenza. People need to take good precautions in the days of severe air pollution and keep warm in cold weather to prevent influenza. We found that the ARIMAX (1,0,1)(0,0,1)
12
with NO
2
model has good predictive performance, which can be used to predict the influenza incidence in Guangxi, and the predicted incidence may be useful in developing early warning systems and providing important evidence for influenza control policy-making and public health intervention.
The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and ...AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.
Port environmental problems have gradually become the primary concern of port authorities. The future trend of port carbon emissions is crucial to port authorities and managers in formulating ...regulations and optimizing operation schedules. Owing to the limitations of current prediction methods and the complex social–environmental impact, the estimation results of port carbon emissions have insufficient accuracy to support port development in the future. In this work, the stochastic impacts by regression on population, affluence, and technology (STIRPAT)–long short-term memory (LSTM)–autoregressive integrated moving average with explanatory variable (ARIMAX) integrated model is proposed for the estimation of the carbon emission of Port of Los Angeles to improve the reliability of emission prediction. Macroeconomic indicators that affect port throughput are selected using the principal component analysis—multiple linear regression model. The chosen indicators are then combined with long-term historical port throughput data as the input of the multivariate autoregressive integrated moving average (ARIMAX) model to predict port throughput. Indicators related to port carbon emissions are verified by the STIRPAT model. The LSTM–ARIMAX integrated model is then applied to estimate the emission tendency, which can be useful in developing corresponding carbon reduction strategies and further understanding port emissions. Results show that the proposed method can significantly improve the estimation accuracy for port emission by 11% compared with existing techniques. Energy conservation strategies are also put forward to assist port authorities in achieving the peak clipping of port carbon emission.
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