scholarly journals Some Concerns when Using Data from the Cooperative Weather Station Networks: A Nebraska Case Study

2005 ◽  
Vol 22 (5) ◽  
pp. 592-602 ◽  
Author(s):  
Hong Wu ◽  
Kenneth G. Hubbard ◽  
Jinsheng You
Keyword(s):  
Mean Absolute Error ◽  
Absolute Error ◽  
Daily Temperature ◽  
Weather Data ◽  
Observation Error ◽  
Climate Data ◽  
Temperature And Precipitation ◽  
Using Data ◽  
Observation Times

Abstract In this study, daily temperature and precipitation amounts that are observed by the Cooperative Observer Program (COOP) were compared among geographically close stations. Hourly observations from nearby Automatic Weather Data Network (AWDN) stations were utilized to resolve the discrepancies between the observations during the same period. The statistics of maximum differences in temperature and precipitation between COOP stations were summarized. In addition, the quantitative measures of the deviations between COOP and AWDN stations were expressed by root-mean-square error, mean absolute error, and an index of agreement. The results indicated that significant discrepancies exist among the daily observations between some paired stations because of varying observation times, observation error, sensor error, and differences in microclimate exposure. The purpose of this note is to bring attention to the problem and offer guidance on the use of daily observations in the comparison and creation of weather maps. In addition, this study demonstrates approaches for identifying the sources of the discrepancies in daily temperature and precipitation observations. The findings will be useful in the quality assurance (QA) procedures of climate data.

scholarly journals A Comparison of Models for Estimating Solar Radiation from Sunshine Duration in Croatia

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Tihomir Betti ◽  
Ivana Zulim ◽  
Slavica Brkić ◽  
Blanka Tuka
Keyword(s):  
Mean Absolute Error ◽  
Sunshine Duration ◽  
Model Performance ◽  
Absolute Error ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Using Data ◽  
Statistical Indicators

The performance of seventeen sunshine-duration-based models has been assessed using data from seven meteorological stations in Croatia. Conventional statistical indicators are used as numerical indicators of the model performance: mean absolute percentage error (MAPE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). The ranking of the models was done using the combination of all these parameters, all having equal weights. The Rietveld model was found to perform the best overall, followed by Soler and Dogniaux-Lemoine monthly dependent models. For three best-performing models, new adjusted coefficients are calculated, and they are validated using separate dataset. Only the Dogniaux-Lemoine model performed better with adjusted coefficients, but across all analysed locations, the adjusted models showed improvement in reduced maximum percentage error.

Electricity Consumption Analysis Using Demographic Variables Case Study – Nakhonratchasima, Thailand

2013 ◽  
Vol 734-737 ◽  
pp. 1679-1682
Author(s):  
Sureeporn Meehom ◽  
Nopphadon Khodpun
Keyword(s):  
High Speed ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Electricity Consumption ◽  
Absolute Error ◽  
Demographic Variables ◽  
Resources Management ◽  
Squared Error ◽  
High Speed Diesel

Electricity energy is vital in social and economic for nation development. The electricity consumption analysis plays an important role for sustainable energy and electricity resources management in the future. In this paper, the influence of demographical variables on the annual electricity consumption in Nakhonratchasima has been investigated by multiple regression analysis. It is founded that the electricity consumption correlated with four demographic variables, which are the number of electricity consumers, the amount of high speed diesel usages, the number of industrial factory and the number of employed labor force. The historical electricity consumption and all variables for the period 20022010 have been analyzed in 8 models for electricity prediction in 2011. In conclusion, the effective model has been selected by comparison of adjusted R2, mean absolute error (MAE) and root mean squared error (RMSE) of the proposed models. Model 8 is acceptable in relation to electricity consumption analysis with adjusted-R2, RMSE and MAE equal to 0.9980, 0.7540% and 0.6095% respectively. The results indicate that the model using all four variables has strong ability to predict future annual electricity consumption with 4,195,837,877 kWh in 2011.

scholarly journals Predicting Fire Brigades Operational Breakdowns: A Real Case Study

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1383 ◽  
Author(s):  
Selene Cerna ◽  
Christophe Guyeux ◽  
Guillaume Royer ◽  
Céline Chevallier ◽  
Guillaume Plumerel
Keyword(s):  
Public Service ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Fire Departments ◽  
Squared Error ◽  
Learning Techniques ◽  
Over Time

Over the years, fire departments have been searching for methods to identify their operational disruptions and establish strategies that allow them to efficiently organize their resources. The present work develops a methodology for breakage calculation and another for predicting disruptions based on machine learning techniques. The main objective is to establish indicators to identify the failures due to the temporal state of the organization in the human and vehicular material. Likewise, by forecasting disruptions, to determine strategies for the deployment or acquisition of the necessary armament. This would allow improving operational resilience and increasing the efficiency of the firemen over time. The methodology was applied to the Departmental Fire and Rescue Doubs (SDIS25) in France. However, it is generic enough to be extended and adapted to other fire departments. Considering a historic of breakdowns of 2017 and 2018, the best predictions of public service breakdowns for the year 2019, presented a root mean squared error of 2.5602 and a mean absolute error of 2.0240 on average with the XGBoost technique.

scholarly journals Interdisciplinary Climate: The Case of the First 50 Years of British Observations in Australia

2012 ◽  
Vol 4 (2) ◽  
pp. 118-131 ◽  
Author(s):  
Kendal McGuffie ◽  
Ann Henderson-Sellers
Keyword(s):  
Interdisciplinary Research ◽  
New South ◽  
Weather Data ◽  
Climate Data ◽  
Climate Research ◽  
Historical Climate ◽  
South Wales ◽  
Instrumental Records ◽  
Single Instrument

Abstract This paper presents the case for improved interdisciplinarity in climate research in the context of assessing and discussing the caution required when utilizing some types of historical climate data. This is done by a case study examining the reliability of the instruments used for collecting weather data in Australia between 1788 and 1840, as well as the observers themselves, during the British settlement of New South Wales. This period is challenging because the instruments were not uniformly calibrated and were created, repaired, and used by a wide variety of people with skills that frequently remain undocumented. Continuing significant efforts to rescue such early instrumental records of climate are likely to be enhanced by more open, interdisciplinary research that encourages discussion of an apparent dichotomy of view about the quantitative value of early single-instrument data between historians of physics (including museum curators) and climate researchers.

scholarly journals Modelling of rainfall intensity in a watershed: A case study in Amprong watershed, Kedungkandang, Malang, East Java of Indonesia

2018 ◽  
Vol 38 (1) ◽  
pp. 75-84
Author(s):  
Lily Montarcih Limantara ◽  
Donny H. Harisuseno ◽  
Vita A.K. Dewi
Keyword(s):  
Return Period ◽  
Rainfall Intensity ◽  
Negative Impact ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Equation Model ◽  
The Other ◽  
Average Value ◽  
Rainfall Occurrence

AbstractAnalysis of rainfall intensity with specific probability is very important to control the negative impact of rainfall occurrence. Rainfall intensity (I), probability (p) and return period (T) are very important variables for the discharge analysis. There are several methods to estimate rainfall intensity, such as Talbot, Sherman, and Ishiguro. The aim of this research is to develop equation model which can predict rainfall intensity with specific duration and probability. The equation model is compared with the other methods. The result of rainfall intensity model with the value of correlation >0.94 and Nash–Sutcliffe coefficient >99 is quite good enough if compared with the observation result. For specific return period, the modelling result is less accurate which is most likely caused by election of duration. Advanced research in other location indicates that short duration gives the better result for rainfall intensity modelling, which is shown by the decreasing average value of mean absolute error (MAE) from 12.963 to 8.26.

scholarly journals Spatial Accuracy of Climate Networks: A Case Study in Nebraska

2014 ◽  
Vol 53 (8) ◽  
pp. 1932-1942 ◽  
Author(s):  
Andrea J. Coop ◽  
Kenneth G. Hubbard ◽  
Martha D. Shulski ◽  
Jinsheng You ◽  
David B. Marx
Keyword(s):  
Air Temperature ◽  
Weather Data ◽  
Reference Network ◽  
Daily Maximum ◽  
Spatial Accuracy ◽  
Climate Data ◽  
Historical Climatology ◽  
Mean Square Errors ◽  
Minimum Air Temperature

AbstractClimate data are increasingly scrutinized for accuracy because of the need for reliable input for climate-related decision making and assessments of climate change. Over the last 30 years, vast improvements to U.S. instrumentation, data collection, and station siting have created more accurate data. This study explores the spatial accuracy of daily maximum and minimum air temperature data in Nebraska networks, including the U.S. Historical Climatology Network (HCN), the Automated Weather Data Network (AWDN), and the more recent U.S. Climate Reference Network (CRN). The spatial structure of temperature variations at the earth’s surface is compared for timeframes 2005–09 for CRN and AWDN and 1985–2005 for AWDN and HCN. Individual root-mean-square errors between candidate station and surrounding stations were calculated and used to determine the spatial accuracy of the networks. This study demonstrated that in the 5-yr analysis CRN and AWDN were of high spatial accuracy. For the 21-yr analysis the AWDN proved to have higher spatial accuracy (smaller errors) than the HCN for both maximum and minimum air temperature and for all months. In addition, accuracy was generally higher in summer months and the subhumid area had higher accuracy than did the semiarid area. The findings of this study can be used for Nebraska as an estimate of the uncertainty associated with using a weather station’s data at a decision point some distance from the station.

scholarly journals Analysis and Forecasting Incidence, Intensive Care Unit Admissions, and Projected Mortality Attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France: Predictions for 4 Weeks Ahead

2021 ◽  
Vol 8 (6) ◽  
pp. 84
Author(s):  
Kathleen Carvalho ◽  
João Paulo Vicente ◽  
Mihajlo Jakovljevic ◽  
João Paulo Ramos Teixeira
Keyword(s):  
Mean Absolute Error ◽  
Absolute Error ◽  
Medical Studies ◽  
Virus Propagation ◽  
Icu Patients ◽  
The United Kingdom ◽  
The Mean ◽  
The Uk ◽  
Vaccination Campaigns

The use of artificial neural networks (ANNs) is a great contribution to medical studies since the application of forecasting concepts allows for the analysis of future diseases propagation. In this context, this paper presents a study of the new coronavirus SARS-COV-2 with a focus on verifying the virus propagation associated with mitigation procedures and massive vaccination campaigns. There were two proposed methodologies in making predictions 28 days ahead for the number of new cases, deaths, and ICU patients of five European countries: Portugal, France, Italy, the United Kingdom, and Germany. A case study of the results of massive immunization in Israel was also considered. The data input of cases, deaths, and daily ICU patients was normalized to reduce discrepant numbers due to the countries’ size and the cumulative vaccination values by the percentage of population immunized (with at least one dose of the vaccine). As a comparative criterion, the calculation of the mean absolute error (MAE) of all predictions presents the best methodology, targeting other possibilities of use for the method proposed. The best architecture achieved a general MAE for the 1-to-28-day ahead forecast, which is lower than 30 cases, 0.6 deaths, and 2.5 ICU patients per million people.

Prediction of cavitation damage on spillway using K-nearest neighbor modeling

2014 ◽  
Vol 71 (3) ◽  
pp. 347-352 ◽  
Author(s):  
E. Fadaei Kermani ◽  
G. A. Barani ◽  
M. Ghaeini-Hessaroeyeh
Keyword(s):  
Present Model ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
K Nearest Neighbor ◽  
Flow Rates ◽  
Cavitation Damage ◽  
Using Data ◽  
The Stability ◽  
Observed Damage

Cavitation is a common and destructive process on spillways that threatens the stability of the structure and causes damage. In this study, based on the nearest neighbor model, a method has been presented to predict cavitation damage on spillways. The model was tested using data from the Shahid Abbaspour dam spillway in Iran. The level of spillway cavitation damage was predicted for eight different flow rates, using the nearest neighbor model. Moreover, based on the cavitation index, five damage levels from no damage to major damage have been determined. Results showed that the present model predicted damage locations and levels close to observed damage during past floods. Finally, the efficiency and precision of the model was quantified by statistical coefficients. Appropriate values of the correlation coefficient, root mean square error, mean absolute error and coefficient of residual mass show the present model is suitable and efficient.

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