scholarly journals MODELING AND PREDICTING THE MONO RIVER OVERFLOW UPSTREAM OF THE NANGBETO DAM IN WEST AFRICA USING MULTIPLICATIVE DETERMINIST MODEL

2021 ◽  
Vol 9 (12) ◽  
pp. 632-642
Author(s):  
Taofic Bacharou ◽  
◽  
Vincent Prodjinonto ◽  
Come Agossa Linsoussi ◽  
◽  
...  
Keyword(s):  
West Africa ◽  
Water Retention ◽  
Deterministic Model ◽  
Arima Model ◽  
Multiplicative Model ◽  
Planning Tool ◽  
R Software ◽  
Operational Model ◽  
River Flows ◽  
Seasonal Trends

The variation and non-control of the overflow of the Mono River adversely affects the performance of the Nangbetohydropower plant to the point thatitcan no longermeet the increasinglyincreaseddemand for electricity. This studypresents the development of an operational model for forecastingdaily river flows for the plants water retention. The overflow of the Mono River at the upstreamhydroelectric dam from 1991 to 2019 wasanalyzed and modeled by the deterministicprocesswith R software in order to makepredictions. First, the flow serieswasanalyzed by the ARIMA model (18, 1, 2) then by a multiplicative model afterremoving the seasonal trends fromtheseseries by the movingaveragemethod. The calculatederror of the results of said model revealsthat the deterministic model integrates the input generationprocesseswith an error of the order of . Finally, an annual flow forecasting program has been developed as a planning tool for the operation of the dam, in order to meet production needs and to plan water releases.

scholarly journals How might climate change affect river flows across West Africa?

2021 ◽  
Vol 169 (3-4) ◽  
Author(s):  
Ponnambalam Rameshwaran ◽  
Victoria A. Bell ◽  
Helen N. Davies ◽  
Alison L. Kay
Keyword(s):  
Climate Change ◽  
West Africa ◽  
Regional Scale ◽  
Weather Data ◽  
Eastern Region ◽  
Water Shortages ◽  
River Flows ◽  
Considerable Uncertainty ◽  
Modelling Framework ◽  
The Impact

AbstractWest Africa and its semi-arid Sahelian region are one of the world’s most vulnerable regions to climate change with a history of extreme climate variability. There is still considerable uncertainty as to how projected climate change will affect precipitation at local and regional scales and the consequent impact on river flows and water resources across West Africa. Here, we aim to address this uncertainty by configuring a regional-scale hydrological model to West Africa. The model (hydrological modelling framework for West Africa—HMF-WA) simulates spatially consistent river flows on a 0.1° × 0.1° grid (approximately 10 km × 10 km) continuously across the whole domain and includes estimates of anthropogenic water use, wetland inundation, and local hydrological features such as endorheic regions. Regional-scale hydrological simulations driven by observed weather data are assessed against observed flows before undertaking an analysis of the impact of projected future climate scenarios from the CMIP5 on river flows up to the end of the twenty-first century. The results indicate that projected future changes in river flows are highly spatially variable across West Africa, particularly across the Sahelian region where the predicted changes are more pronounced. The study shows that median peak flows are projected to decrease by 23% in the west (e.g. Senegal) and increase by 80% in the eastern region (e.g. Chad) by the 2050s. The projected reductions in river flows in western Sahel lead to future droughts and water shortages more likely, while in the eastern Sahel, projected increases lead to future frequent floods.

scholarly journals Comparative Performance of the ARIMA, ARIMAX and SES Model for Estimating Reported Cases of Diabetes Mellitus in Anambra State, Nigeria

2021 ◽  
Vol 6 (1) ◽  
pp. 63-68
Author(s):  
C. V. Obi ◽  
C. N. Okoli
Keyword(s):  
Diabetes Mellitus ◽  
Arima Model ◽  
Secondary Data ◽  
Information Criterion ◽  
R Software ◽  
Comparative Performance ◽  
Medical Practitioners ◽  
Anambra State ◽  
Related Risk ◽  
Best Fit

This study examined the performance of the ARIMA, ARIMAX and the Single Exponential Smoothing (SES) model for the estimation of diabetes cases in Anambra State with the following specific objectives: to fit the model to the data, to determine the best fit model for estimating diabetes mellitus cases and forecast for expected cases for period of five years. The secondary data used for the study is sourced from records of Anambra state Ministry of Health. The Akaike information criterion is adopted for assessing the performance of the models. The R-software is employed for the analysis of data. The results obtained showed that the data satisfied normality and stationarity requirements. The finding of the study showed that ARIMA model has least value of AIC of 1177.92, following the ARIMAX model with value of AIC=1542.25 and SEM recorded highest value of 1595.67. The findings further revealed that the ARIMA has the least values across the measures of accuracy. More so, five years predictions of the cases of diabetes mellitus were obtained using the models under study. From the results of the findings, ARIMA model proved to be best alternative for estimating reported cases of diabetes mellitus in Anambra state. Based on the findings, we recommend there is need for medical practitioners /health planners to create awareness and inform patients about the possible related risk factors of death through early diagnosis and intervention.

Peramalan terhadap Forex dengan Metode ARIMA Studi Kasus GBP/USD

2019 ◽  
Vol 11 (1) ◽  
pp. 6-10
Author(s):  
Michael Saputra Suryono ◽  
Raymond Oetama
Keyword(s):  
Knowledge Discovery ◽  
Foreign Exchange ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
R Software ◽  
Absolute Percentage Error ◽  
Knowledge Discovery In Database ◽  
The Mean

Forex or Foreign Exchange is trading a country's currency with another country's currency. The purpose of this study is basically to test the accuracy of ARIMA on the GBP/USD currency pair. In addition, this research is expected to provide the benefits of knowledge about forecasting using ARIMA. This study resulted in forecasting the GBP/USD currency pair within 1 month, per 6 months from January 2018 to June 2018 using the ARIMA method and R software. Data to be used are data taken from January 2013 to June 2018. For the the process will follow the process of the KDD (Knowledge Discovery in Database). The results obtained by the ARIMA model (3,2,1) as the best model to be applied for 1 month per 6 months on the GBP/USD currency pair because it has the lowest AIC value and the mean absolute percentage error is 3.16%.

Prediction of River Flows by Multiplicative Model

1977 ◽  
Vol 10 (7) ◽  
pp. 23-30
Author(s):  
Motoyasu Nagata ◽  
Tohru Katayama ◽  
Yoshikazu Sawaragi
Keyword(s):  
Multiplicative Model ◽  
River Flows

scholarly journals Rainfall and Water Resources Variability in Sub-Saharan Africa during the Twentieth Century

2009 ◽  
Vol 10 (1) ◽  
pp. 41-59 ◽  
Author(s):  
Declan Conway ◽  
Aurelie Persechino ◽  
Sandra Ardoin-Bardin ◽  
Hamisai Hamandawana ◽  
Claudine Dieulin ◽  
...  
Keyword(s):  
Water Resources ◽  
West Africa ◽  
Southern Africa ◽  
River Flow ◽  
Data Availability ◽  
Sub Saharan Africa ◽  
Spatial And Temporal Variability ◽  
Rainfall Runoff ◽  
River Flows ◽  
Sub Saharan

Abstract River basin rainfall series and extensive river flow records are used to characterize and improve understanding of spatial and temporal variability in sub-Saharan African water resources during the last century. Nine major international river basins were chosen for examination primarily for their extensive, good quality flow records. A range of statistical descriptors highlight the substantial variability in rainfall and river flows [e.g., differences in rainfall (flows) of up to −14% (−51%) between 1931–60 and 1961–90 in West Africa], the marked regional differences, and the modest intraregional differences. On decadal time scales, sub-Saharan Africa exhibits drying across the Sahel after the early 1970s, relative stability punctuated by extreme wet years in East Africa, and periodic behavior underlying high interannual variability in southern Africa. Central Africa shows very modest decadal variability, with some similarities to the Sahel in the adjoining basins. No consistent signals in rainfall and river flows emerge across the whole of the region. An analysis of rainfall–runoff relationships reveals varying behavior including strong but nonstationary relationships (particularly in West Africa); many basins with marked variations (temporal and spatial) in strength; weak, almost random behavior (particularly in southern Africa); and very few strong, temporally stable relationships. Twenty-year running correlations between rainfall and river flow tend to be higher during periods of greater rainfall station density; however, there are situations in which weak (strong) relationships exist even with reasonable (poor) station coverage. The authors conclude for sub-Saharan Africa that robust identification and attribution of hydrological change is severely limited by data availability, conflicting behavior across basins/regions, low signal-to-noise ratios, sometimes weak rainfall–runoff relationships, and limited quantification of the magnitude and effects of land use change.

scholarly journals Forecasting the spread of COVID-19 in Nigeria using Box-Jenkins Modeling Procedure

2020 ◽  
Author(s):  
Rauf Rauf Ibrahim ◽  
Hannah Oluwakemi Oladipo
Keyword(s):  
Task Force ◽  
Moving Average ◽  
Arima Model ◽  
Time Frame ◽  
R Software ◽  
Test Parameter ◽  
Autoregressive Integrated Moving Average ◽  
Stationarity Test ◽  
Parameter Test ◽  
Insight Into

AbstractObjectiveThis study is focused on the analysis of the spread of Covid-19 in Nigeria, applying statistical models and available data from the NCDC. We present an insight into the spread of Covid-19 in Nigeria in order to establish a suitable prediction model, which can be applied as a decision-supportive tool for assigning health interventions and mitigating the spread of the Covid-19 infection.MethodologyDaily spread data from February 27 to April 26, 2020, were collected to construct the autoregressive integrated moving average (ARIMA) model using the R software. Stability analysis and stationarity test, parameter test, and model diagnostic were also carried out. Finally, the fitting, selection and prediction accuracy of the ARIMA model was evaluated using the AICc model selection criteria.ResultsThe ARIMA (1,1,0) model was finally selected among ARIMA models based upon the parameter test and Box–Ljung test. A ten-day forecast was also made from the model, which shows a steep upward trend of the spread of the COVID-19 in Nigeria within the selected time frame.ConclusionFederal Government of Nigeria through the presidential task force can apply the forecasted trend of much more spread to make more informed decisions on the additional measures in place to curb the spread of the virus. Application of the model can also assist in studying the effectiveness of the lockdown on the on the spread of Covid-19 in Nigeria.

scholarly journals Modeling and analysis of a time series of equine infectious anemia cases in the state of Tocantins, Brazil, between 2007 and 2019

2020 ◽  
Vol 41 (6supl2) ◽  
pp. 3145-3154
Author(s):  
Alessandro José Ferreira dos Santos ◽  
◽  
Jardel Martins Ferreira ◽  
Francisco Baptista ◽  
Marco Augusto Giannoccaro da Silva ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
The State ◽  
Intermittent Fever ◽  
R Software ◽  
Predictive Capacity ◽  
Modeling And Analysis ◽  
Viral Infectious Disease ◽  
Equine Infectious Anemia

Equine infectious anemia (EIA) is a viral infectious disease that affects Equidae and is clinically characterized by intermittent fever, anemia, depression, emaciation, and edema. To evaluate disease dynamics in the state of Tocantins, Brazil, a time series of EIA cases in the period 2007–2019 was analyzed to describe the pattern of occurrence and define the autoregressive integrated by moving average (ARIMA) model best suited to make predictions of cases of this disease for the period 2020–2021. The modeling and statistical analysis of the time series were performed using R software. The ARIMA model (2,1,1) was evaluated by Holdout cross-validation, in which data from the periods 2007–2017 and 2018–2019 were used as training and test data, respectively. The analyses showed that EIA was endemic and non-seasonal in Tocantins. The ARIMA model (2,1,1) showed good predictive capacity adjusted for this time series. However, the prediction of 276 cases of EIA in Tocantins for the period 2020–2021 may vary depending on the demand for diagnostic tests for Equidae transportation and herd sanitation in farms considered infection foci. The ARIMA model helps predict the number of EIA cases in Tocantins and improves planning for disease control by the Official Veterinary Service.

scholarly journals The Impact of the Lockdown Measure on the Confirmed Cases of the Novel Coronavirus (COVID-19) in Nigeria

2020 ◽  
pp. 110-119
Author(s):  
Charles Okechukwu Aronu ◽  
Nkechi Udochukwu Otty ◽  
Jacob Chinedum Ehiwario ◽  
Patrick Nnaemeka Okafor
Keyword(s):  
West Africa ◽  
Moving Average ◽  
Arima Model ◽  
Secondary Data ◽  
The Novel ◽  
Test Analysis ◽  
Significant Difference ◽  
Daily Report ◽  
Novel Coronavirus ◽  
The Impact

Aims: This study examined the impact of the lockdown measure on the confirmed cases of the Novel Coronavirus (COVID-19) in Nigeria.  The objectives of the study include to identifying an appropriate autoregressive integrated moving average (ARIMA) model that is adequate for estimating the reported cases of COVID-19 in Nigeria and to ascertain whether the ease of lockdown has a significant impact on the reported cases of COVID-19 in Nigeria.  Place and Duration of Study: The source of the data used for this study was the secondary data obtained from the daily report of the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) from 1st February 2020 to 30th June 2020. Methodology: The statistical tools used for data analysis are the ARIMA time series model and the Chow test analysis. Results: Nigeria ranked 1st in West Africa sub-region with a total of 25, 133 confirmed COVID-19 cases, followed by Ghana with 17, 351 confirmed cases while Gambia recorded the least number of confirmed cases with 47 cases of COVID19. The ARIMA (0, 1, 1) was identified as the best model for forecasting the confirmed COVID-19 cases in Nigeria within the observed period. It was found that there exists a significant difference in the number of confirmed cases of COVID-19 during the lockdown period and the post lockdown period. Conclusion: The study revealed that Nigeria has the most confirmed cases of COVID-19 in West Africa region. Also, the ease of the lockdown was found to increase the number of confirmed virus cases in Nigeria.

scholarly journals A long range dependent model with nonlinear innovations for simulating daily river flows

2004 ◽  
Vol 4 (2) ◽  
pp. 277-283 ◽  
Author(s):  
P. Elek ◽  
L. Márkus
Keyword(s):  
Long Memory ◽  
Arima Model ◽  
Smooth Transition ◽  
River Flows ◽  
Discharge Data ◽  
High Quantiles ◽  
Fractional Arima ◽  
Proposed Model ◽  
Significant Difference ◽  
Non Gaussian

Abstract. We present the analysis aimed at the estimation of flood risks of Tisza River in Hungary on the basis of daily river discharge data registered in the last 100 years. The deseasonalised series has skewed and leptokurtic distribution and various methods suggest that it possesses substantial long memory. This motivates the attempt to fit a fractional ARIMA model with non-Gaussian innovations as a first step. Synthetic streamflow series can then be generated from the bootstrapped innovations. However, there remains a significant difference between the empirical and the synthetic density functions as well as the quantiles. This brings attention to the fact that the innovations are not independent, both their squares and absolute values are autocorrelated. Furthermore, the innovations display non-seasonal periods of high and low variances. This behaviour is characteristic to generalised autoregressive conditional heteroscedastic (GARCH) models. However, when innovations are simulated as GARCH processes, the quantiles and extremes of the discharge series are heavily overestimated. Therefore we suggest to fit a smooth transition GARCH-process to the innovations. In a standard GARCH model the dependence of the variance on the lagged innovation is quadratic whereas in our proposed model it is a bounded function. While preserving long memory and eliminating the correlation from both the generating noise and from its square, the new model is superior to the previously mentioned ones in approximating the probability density, the high quantiles and the extremal behaviour of the empirical river flows.

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