Forecasting Temperature Records in PEI, Canada

Khedhiri, Sami

doi:10.1007/s12076-014-0135-x

Cited by 7 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…King (2019) predicted that seasonal temperatures here would continue to nonlinearly increase during 2020–45. Khedhiri (2016) used PEI data of 1913–2013 to project mean monthly minimum and maximum temperatures and predicted that temperature would continue to rise.…”

Section: Resultsmentioning

confidence: 99%

“…Two sample t ‐test was applied for finding significant difference in rainfall intensities over time, having two data groups. Kedhiri (2016) used f‐test and t ‐test to find statistical significance of temperature over time in PEI. Minitab 18 (State College, Pennsylvania State University, PA: Minitab, Inc.) was used in the calculation of ANOVA and multiple means comparisons tests.…”

Section: Methodsmentioning

confidence: 99%

“…King (2019) predicted that seasonal temperatures here would continue to nonlinearly increase during 2020-45. Khedhiri (2016) used PEI data of 1913-2013 to project mean monthly minimum and maximum temperatures and predicted that temperature would continue to rise. Precipitation has increased on the eastern side, whereas as we move westward it decreased particularly snowfall, and especially in Summerside.…”

Section: Mean Monthlymentioning

confidence: 99%

See 2 more Smart Citations

Climate change impacts on precipitation and temperature in Prince Edward Island, Canada

et al. 2021

View full text Add to dashboard Cite

To understand climate change impacts on Prince Edward Island (PEI), Canada, historical daily precipitation and temperature of the island was investigated between the periods: 1931–60 (1940s), 1961–90 (1970s), and 1991–2020 (2000s) in its eastern, central, and western parts. Observed climatic data were utilized, augmented by some validated modeled data of Pacific Climate Impact Consortium (PCIC) for missing years. Statistically significant warming of the island was found ranging from 1.14°C in the east to 0.75°C in the west from the 1970s to 2000s. The warming trend during the period was distributed throughout the year including winters. In the east, mean monthly temperature significantly increased in all the months except for January, March, and June. Significant increase in temperature was found solely during August (+0.80°C) in central, and for August (+0.64°C), September (+0.99°C), and October (+0.73°C) in western parts. Proportionate increase in annual minimum temperature was greater than the maximum, particularly in eastern (+1.57°C) and central (+0.75°C) parts and thus indicated moderated cold there. Over the same 30‐year period, annual precipitation increased 6 percent in the east but decreased 5 and 8 percent in the central and the western PEI, respectively. The changes in precipitation were not statistically significant, except snowfall reduction (−20%) in the west, which was a statistically significant change. Interannual precipitation variations during wet and dry years having 20 and 80 percent probabilities of exceedance, respectively, ranged 350–470 mm/year during 1991–2020. Rainfall intensities, measured by hourly data, increased from 1.15 to 2.24 mm/hr, on average in central and western parts, respectively, in 2004–17 compared to 1970s. Impacts of the rising temperatures, decreasing precipitation, and uneven and intense rainfalls patterns on water resources and rainfed agriculture need further investigations. Climate change adaptations be included in existing water policies to mitigate the impacts.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Mean Monthlymentioning

confidence: 99%

See 1 more Smart Citation

Climate change impacts on precipitation and temperature in Prince Edward Island, Canada

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In another vein, [29] used different ARIMA models and linear trends models to forecast the temperature and precipitation in the Afyonkarahisar area in Turkey and found that there would be an increase in temperature till the year 2025. According to pieces of literatures, [30] using statistical properties studied the historical effect of temperature in Canada for the period 1913-2013 whereby determine a seasonal ARIMA model to forecast forthcoming temperature records. In their research [31] implemented the use of the SERIMA model as proposed by different researchers on the frequency analysis and forecasting monthly rainfall in Umuahia.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Forecasting Selected Meteorological Parameters for the Environmental Awareness in Sub-Sahel West Africa Stations

Aweda

Akinpelu

Samson

et al. 2022

J. Nig. Soc. Phys. Sci.

View full text Add to dashboard Cite

The monthly air temperature, rainfall, air pressure, and wind speed direction for the environmental time series recorded between January 1, 1980 and December 31, 2020 in six African stations from different climatic zones were modeled and forecasted. In the forecasting, Augmented Dickey Fuller test, ARIMA models, Auto correlation Function (ACF) and Partial Autocorrelation Function (PACF) were used. Result showed that in most of the fitted models, the Moving Average terms both seasonal and non- seasonal were also significant (p<0.05) indicating that the previous day value of the stochastic term also had a significant effect on the present value of meteorological parameters in the environment. It was observed that in all the fitted models except for wind direction in Conakry and rainfall in Abidjan have all their Autoregressive term of order 1 significant (p<0.05) which implies that previous day value of these meteorological parameter had a significant effect on the present day value of the parameters. Therefore, the forecast model indicates that maximum temperature are expected in February, March, April, and June while minimum temperatures in January, August, December. Although, the selected models cannot forecast the precise air temperature, this can also provide information that can be of help to create tactics for appropriate preparation of farming which can be used as tools for effective environmental preparation and policymaking.

show abstract

“…The basic postulation created for the application of the various models for the assumption of those time series, which could result in the improvement of the conventional statistical method, includes linear time series and statistical distribution [1,2,17,20]. The ARIMA model is known as the SARIMA model, which is successful in the variation of those models [8], according to seasonal time series forecasting that is based on [8,10,11,13,[21][22][23]. Due to its implications as the degree of heat and cold in the environment, the air temperature has long been recognized as a crucial component in the analysis of meteorological data [7,8].…”

Section: Introductionmentioning

confidence: 99%

Temperature Forecasting for Iwo City, Nigeria Using Statistical Models and Selected Machine Learning Algorithms

Aweda,

Adeola,

Adeniji

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Time series modeling and forecasting using a machine learning algorithm approach for Iwo City, a western city in Nigeria, is a technique used to forecast the future occurrence of diverse data for the study. The purpose of this study is to use time series models and some machine learning approaches to predict the temperature in the future for climate change. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) Web service provided the information used in this study. The dataset underwent training and testing in an 80/20 per cent manner. Using seasonal auto-regressive integrated moving average (SARIMA) and twenty-four (24) different Machine Learning algorithms of regression performance of the temperature, future predictions of Iwo station, were made using training data that spans the years 1980 to 2012 and test data that spans the years 2013 to 2021. The results reveal that the standard deviation value ranges from 2.4×10-05 to 0.031, while the optimum model for the SARIMA (1, 0, 0) and Akaike’s Information Criterion (AIC) spans from 961.090 to 1341.831. Furthermore, the results of the statistical analysis show that the maximum average temperature recorded in April was 303.68K, the minimum temperature recorded in January was 293.23K with a maximum standard deviation of 1.84 in February, and the minimum standard deviation for the temperature was 0.42 in September. For the machine learning algorithm, Exponential GPR shows the highest R2 of 0.19 while the least Ensemble Boosted Trees (R2= -46.24). In terms of the forecasting performance of these machine learning algorithms based on RMSE, the best forecasting model was Medium Neural Network (RMSE = 1.8112). The result also reveals that Fine Gaussian gave the least MAE = 1.19, while the least MSE = 2.7534 meaning that these machine learning algorithms outperformed other models. As a result, the report recommends that Nigerian meteorological management establishes additional research centers to collect data and conduct research.

show abstract

Forecasting Temperature Records in PEI, Canada

Cited by 7 publications

References 17 publications

Climate change impacts on precipitation and temperature in Prince Edward Island, Canada

Climate change impacts on precipitation and temperature in Prince Edward Island, Canada

Modeling and Forecasting Selected Meteorological Parameters for the Environmental Awareness in Sub-Sahel West Africa Stations

Temperature Forecasting for Iwo City, Nigeria Using Statistical Models and Selected Machine Learning Algorithms

Contact Info

Product

Resources

About