Trend analysis and spatiotemporal prediction of precipitation, temperature, and evapotranspiration values using the ARIMA models: case of the Algerian Highlands

Bouznad, Imad Eddine; Guastaldi, Enrico; Zirulia, Andrea; Brancale, Mariantonietta; Barbagli, Alessio; Bengusmia, Djamal

doi:10.1007/s12517-020-06330-6

Cited by 25 publications

(13 citation statements)

References 44 publications

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“…Considering the network delay, we will preprocess the data, and the goal is to process the data into a standard time series [ 34 ]. After decomposing the time series [ 35 ], we will obtain the trend, season, and residual.…”

Section: Construction Of Prediction Modelmentioning

confidence: 99%

“…ARIMA model has high requirements for the stationarity of time series. Therefore, we test the stationarity of time series through differencing [ 21 ] and ACF (autocorrelation function) [ 35 ] and then use ADF (augmented Dickey fuller test) [ 37 ] to judge the stationarity further. If the time series is unstable, the time series trend is weakened by differencing.…”

Section: Construction Of Prediction Modelmentioning

confidence: 99%

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Design of PM2.5 monitoring and forecasting system for opencast coal mine road based on internet of things and ARIMA Mode

et al. 2022

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The dust produced by transportation roads is the primary source of PM2.5 pollution in opencast coal mines. However, China’s opencast coal mines lack an efficient and straightforward construction scheme of monitoring and management systems and a short-term prediction model to support dust control. In this study, by establishing a PM2.5 and other real-time environmental information to monitor, manage, visualize and predict the Internet of things monitoring and prediction system to solve these problems. This study solves these problems by establishing an Internet of things monitoring and prediction system, which can monitor PM2.5 and other real-time environmental information for monitoring, management, visualization, and prediction. We use Lua language to write interface protocol code in the APRUS adapter, which can simplify the construction of environmental monitoring system. The Internet of things platform has a custom visualization scheme, which is convenient for managers without programming experience to manage sensors and real-time data. We analyze real-time data using a time series model in Python, and RMSE and MAPE evaluate cross-validation results. The evaluation results show that the average RMSE of the ARIMA (4,1,0) and Double Exponential Smoothing models are 12.68 and 8.34, respectively. Both models have good generalization ability. The average MAPE of the fitting results are 10.5% and 1.7%, respectively, and the relative error is small. Because the ARIMA model has a more flexible prediction range and strong expansibility, and ARIMA model shows good adaptability in cross-validation, the ARIMA model is more suitable as the short-term prediction model of the prediction system. The prediction system can continuously predict PM2.5 dust through the ARIMA model. The monitoring and prediction system is very suitable for managers of opencast coal mines to prevent and control road dust.

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Section: Construction Of Prediction Modelmentioning

confidence: 99%

Section: Construction Of Prediction Modelmentioning

confidence: 99%

Design of PM2.5 monitoring and forecasting system for opencast coal mine road based on internet of things and ARIMA Mode

et al. 2022

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“…The review of the methods used to build prediction models shows a certain research gap, namely that all regression methods encounter difficulties in a situation when, on the basis of numerical data, the simultaneous occurrence of a trend and seasonality is found. Although there are methods that take into account the seasonality and cyclicality of phenomena, such as: ARIMA and SARIMA [37,38], their application is limited in many cases. This is due to the fact that these methods can be used to model stationary series, i.e., series in which there are only random fluctuations around the mean value, or non-stationary series that are reducible to the stationary form [39].…”

Section: Literature Surveymentioning

confidence: 99%

Prediction model of seasonality in the construction industry based on the accidentality phenomenon

Hoła

Topolski

Szer

et al. 2021

Archiv.Civ.Mech.Eng

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The construction industry is an economic sector that is characterized by seasonality. Seasonal factors affect the volume of production, which in turn affects the accident rate. The aim of the research presented in the article was to develop a model for predicting the number of people injured in occupational accidents in the construction industry. Based on the analysis of statistical data and previous studies, the occurrence of certain regularities of the accidentality phenomenon was found, namely the long-term trend over many years, as well as seasonality and cyclicality over the course of a year. The found regularities were the basis for the assumptions that were made for the construction of the model. A mathematical model was built in the non-linear regression dimension. The model was validated by comparing the results of prediction errors generated by the developed model with the results of prediction errors generated by other known models, such as ARIMA, SARIMA, linear and polynomial models, which take into account the seasonality of the phenomenon. The constructed model enables the number of people injured in accidents in the construction industry in selected months of future years to be predicted with high accuracy. The obtained results can be the basis for making appropriate decisions regarding preventive and prophylactic measures in the construction industry. Commonly known mathematical tools available in the STATISTICA package were used to solve the given task.

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“…SARIMA was applied to a coastal Tuscany watershed to study hydrological cycle changes at the local scales (area) [40]. Furthermore, SARIMA was applied for the spatiotemporal analysis in the Highlands region, Algeria, to predict the local scale of precipitation [41]. However, SARIMA does not work well on multiple seasonalities [42], the daily and yearly patterns in the same time series of a climate variable (e.g., surface temperature).…”

Section: Related Workmentioning

confidence: 99%

Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast

et al. 2021

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Many machine-learning applications and methods are emerging to solve problems associated with spatiotemporal climate forecasting; however, a prediction algorithm that considers only short-range sequential information may not be adequate to deal with periodic patterns such as seasonality. In this paper, we adopt a Periodic Convolutional Recurrent Network (Periodic-CRN) model to employ the periodicity component in our proposals of the periodic representation dictionary (PRD). Phase shifts and non-stationarity of periodicity are the key components in the model to support. Specifically, we propose a Soft Periodic-CRN (SP-CRN) with three proposals of utilizing periodicity components: nearby-time (PRD-1), periodic-depth (PRD-2), and periodic-depth differencing (PRD-3) representation to improve climate forecasting accuracy. We experimented on geopotential height at 300 hPa (ZH300) and sea surface temperature (SST) datasets of ERA-Interim. The results showed the superiority of PRD-1 plus or minus one month of a prior cycle to capture the phase shift. In addition, PRD-3 considered only the depth of one differencing periodic cycle (i.e., the previous year) can significantly improve the prediction accuracy of ZH300 and SST. The mixed method of PRD-1, and PRD-3 (SP-CRN-1+3) showed a competitive or slight improvement over their base models. By adding the metadata component to indicate the month with one-hot encoding to SP-CRN-1+3, the prediction result was a drastic improvement. The results showed that the proposed method could learn four years of periodicity from the data, which may relate to the El Niño–Southern Oscillation (ENSO) cycle.

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Trend analysis and spatiotemporal prediction of precipitation, temperature, and evapotranspiration values using the ARIMA models: case of the Algerian Highlands

Cited by 25 publications

References 44 publications

Design of PM2.5 monitoring and forecasting system for opencast coal mine road based on internet of things and ARIMA Mode

Design of PM2.5 monitoring and forecasting system for opencast coal mine road based on internet of things and ARIMA Mode

Prediction model of seasonality in the construction industry based on the accidentality phenomenon

Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast

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