scholarly journals Application of Sarima Models in Modelling and Forecasting Monthly Rainfall in Nigeria

2021 ◽  
pp. 30-43
Author(s):  
Aliyu Sani Aliyu ◽  
Abubakar Muhammad Auwal ◽  
M. O. Adenomon
Keyword(s):  
World Bank ◽  
Rainfall Amount ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
P Value ◽  
Sarima Model ◽  
Nigerian Government ◽  
Unpredictable Variation ◽  
Sarima Models ◽  
Modelling And Forecasting

Application of SARIMA model in modelling and forecasting monthly rainfall in Nigeria was considered in this study. The study utilizes the Nigerian monthly rainfall data between 1980-2015 obtained from World Bank Climate Portal. The Box-Jenkin’s methodology was adopted.  SARIMA (2,0,1) (2,1,1) [12] was the best model among others that fit the Nigerian rainfall data (1980-2015) with maximum p-value from Box-Pierce Residuals Test. The study forecasts Nigeria’s monthly rainfall from 2018 through 2042. It was discovered that the month of April is the period of onset of rainfall in Nigeria and November is the period of retreat. Based on the findings, Nigeria will experience approximately equal amount of rainfall between 2018 to 2021 and will experience a slight increase in rainfall amount in 2022 to about 1137.078 (mm). There will be a decline of rainfall at 2023 to about 1061 (mm). Rainfall values will raise again to about 1142.756 (mm) in 2024 and continue to fluctuate with decrease in variation between 2024 to 2042, then remain steady to 2046 at approximately 1110.0 (mm). Nigerian Government should provide a more mechanized and drier season farming methods to ease the outage of rainfall in future that may be caused due to natural (or unpredictable) variation.

scholarly journals Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

2017 ◽  
Vol 35 (1) ◽  
pp. 229-236 ◽  
Author(s):  
Kassahun Birhanu Tadesse ◽  
Megersa Olumana Dinka
Keyword(s):  
River Flow ◽  
River System ◽  
Forecast Performance ◽  
Sarima Model ◽  
River Flow Forecasting ◽  
Flow Information ◽  
Computational Intelligent ◽  
Development And Management ◽  
Sarima Models ◽  
Modelling And Forecasting

AbstractKnowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann–Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI) and Hannan–Quinn (HQ) criteria, SARIMA (3, 0, 2) x (3, 1, 3)12 model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.

scholarly journals SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 602
Author(s):  
Luisa Martínez-Acosta ◽  
Juan Pablo Medrano-Barboza ◽  
Álvaro López-Ramos ◽  
John Freddy Remolina López ◽  
Álvaro Alberto López-Lambraño
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Autocorrelation Function ◽  
Information Criterion ◽  
Monthly Rainfall ◽  
Statistical Characteristics ◽  
Rainfall Time Series ◽  
River Watershed ◽  
Statistical Measures ◽  
Sarima Models

Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed.

scholarly journals PENGARUH FAKTR USIA DAN GRAVIDA IBU TERHADAP PERDARAHAN ANTEPARTUM DI RSUD CILACAP PERIODE TAHUN 2016-2018

2020 ◽  
Vol 13 (1) ◽  
pp. 56-63
Author(s):  
Sohimah ◽  
Yogi Andhi Lestari ◽  
Arief Hendrawan
Keyword(s):  
World Bank ◽  
Early Pregnancy ◽  
Pregnancy Loss ◽  
Case Control ◽  
P Value ◽  
Early Pregnancy Loss ◽  
Chi Square ◽  
Key Word

Berdasarkan Laporan World Bank Tahun 2017, dalam sehari ada empat Ibu di Indonesia yang meninggal akibat melahirkan. Angka ini menempatkan Indonesia sebagai Negara dengan angka kematian tertinggi kedua di Asia Tenggara setelah Laos dengan AKI 357 per 100 ribu (WHO,2017). Penyebab kematian Ibu terdiri dari penyebab langsung dan tidak langsung. Penyebab langsung kematian ibu disebabkan karena perdarahan sampai saat ini masih memegang peranan penting sebagai penyeba utama kematian maternal.  Perdarahan dapat terjadi disetiap usia kehamilan, pada kehamilan muda ssering dikaitkan dengan abortus, misscariiage, early pregnancy loss.  Perdarahan yang terjadi pada umur kehamilan yang lebih tua terutama setelah melewati trimester III disebut perdarahan antepartum. Survey pendahuluan yang dilakukan pada tanggal 4 Januari 2019 dan didukung data pada Profil Dinas Kesehatan Kabupaten Cilacap, Kematian ibu selama tahun 2016 sebanyak 25 kasus, 2017 sebanyak 20 kasus dan 22 kasus selama Tahun 2018.   Penyebab kematian ibu sebagian besar terjadi pada saat persalinan dan segera setelah persalinan yaitu perdarahan (30,37%), eklampsia (32,97%), infeksi (4,34%), Gangguan sistem peredaran darah 8%, Gangguan metabolism 4,34 %, dan lain-lain 0,87 % . Tujuan dari penelitian ini adalah untuk Mengetahui Pengaruh Usia dan Gravida Ibu terhadap kejadian perdarahan antepartum di RSUD Cilacap Tahun 2016 – 2018.  Desain penelitian ini adalah deskriptif analitik dengan metode pendekatan case control yang bertujuan mengetahui analisis Pengaruh fektor usia dan Gravida ibus terhadap kejadian perdarahan antepartum di RSUD Cilacap. Tekhnik pengambilan sampel pada penelitian ini adalah dengan total sampling dengan kriteria inklusi rekam medik lengkap. Uji statistik yang digunakan adalah Chi-Square.     Hasil Penelitian: Berdasarkan hasil analisis  Faktor usia ibu berpengaruh terhadap kejadian perdarahan antepartum dengan p value 0.001.  Faktor gravida berpengaruh terhadap kejadian perdarahan antepartum dengan p value 0.000. Faktor usia merupakan faktor yang paling berisiko terhadap kejadian perdarahan antepartum, dengan OR:  2,098.     Kesimpulan:  Usia ibu yang berisiko berpengaruh 2.098 kali lebih besar terhadap perdarahan antepartum dibanding dengan usia yang tidak  berisiko   Key Word :             Gravida, Perdarahan Antepartum, Usia Ibu

scholarly journals Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management

2021 ◽  
Vol 11 (20) ◽  
pp. 9566
Author(s):  
Tommaso Caloiero ◽  
Gaetano Pellicone ◽  
Giuseppe Modica ◽  
Ilaria Guagliardi
Keyword(s):  
New Zealand ◽  
Spatial Interpolation ◽  
Cross Validation ◽  
Landscape Management ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
The Other ◽  
Geostatistical Methods ◽  
The Cross ◽  
Almost All

Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.

scholarly journals U.K. Rainfall Data: A Long-Term Persistence Approach

2012 ◽  
Vol 51 (10) ◽  
pp. 1904-1913 ◽  
Author(s):  
Luis A. Gil-Alana
Keyword(s):  
Strong Dependence ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Modeling Approaches ◽  
Long Run ◽  
The One

AbstractThis paper looks at the analysis of U.K. monthly rainfall data from a long-term persistence viewpoint. Different modeling approaches are considered, taking into account the strong dependence and the seasonality in the data. The results indicate that the most appropriate model is the one that presents cyclical long-run dependence with the order of integration being positive though small, and the cycles having a periodicity of about a year.

scholarly journals Comparison of Different Rainfall Erosion Estimation Methods for the Island of Crete

Proceedings ◽  
2020 ◽  
Vol 30 (1) ◽  
pp. 67 ◽  
Author(s):  
Dimitrios D. Alexakis ◽  
Manolis Grillakis
Keyword(s):  
Temporal Resolution ◽  
Total Duration ◽  
Power Estimation ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
High Temporal Resolution ◽  
Rainfall Erosivity ◽  
Daily Data

Interactions between soil and rainfall plays a vital role in ecological, hydrological and biogeochemical cycles of land. Among those interactions, the phenomenon of rainfall induced soil erosion is crucial to the soil functions, as it affects the soil structure and organic matter content that subsequently affects soil ability to hold moisture and nutrients. The erosive power of a specific rainfall event is regulated by its intensity and total duration. Various methodologies have been developed and tested to estimate the rainfall erosivity in different hydroclimatic regions and using different rainfall measuring timescales. Studies have shown that high temporal resolution measurements provide a more robust erosivity estimation. Nonetheless the sparsity and scarcity of such high temporal resolution data make the accurate estimation of rainfall erosivity difficult. Here, we compare different erosion power estimation methods based on different rainfall timescales for the island of Crete. Sub-daily (30-min) rainfall data based estimation is used as the basis for the assessment of a daily data based estimation methodology and two different methods that use monthly rainfall data. Modified Fournier Index (MFI) is incorporated in the study through different literature approaches and a regression equation is developed between rainfall erosivity power and MFI index for Crete. Results indicate that the use of daily data in the rainfall erosive power estimation is a good approximation of the sub-daily estimation, while formulas based on monthly rainfall data tend to exhibit larger deviations.

Evaluation of methods of spatial interpolation for monthly rainfall data over the state of Rio de Janeiro, Brazil

2017 ◽  
Vol 134 (3-4) ◽  
pp. 955-965 ◽  
Author(s):  
Gustavo Bastos Lyra ◽  
Tamíres Partelli Correia ◽  
José Francisco de Oliveira-Júnior ◽  
Marcelo Zeri
Keyword(s):  
Rio De Janeiro ◽  
Spatial Interpolation ◽  
The State ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Evaluation Of Methods

Modelling Catchment Rainfall Using Sum of Correlated Gamma Variables

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Zakaria, R. ◽  
Howlett, P. G. ◽  
Piantadosi, J. ◽  
Boland, J. W. ◽  
Moslim, N. H.
Keyword(s):  
Spatial Analysis ◽  
Random Variables ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Weighted Sum ◽  
Statistical Parameters ◽  
Dependent Random Variables ◽  
Rainfall Model

One of the major difficulties in simulating rainfall is the need to accurately represent rainfall accumulations. An accurate simulation of monthly rainfall should also provide an accurate simulation of yearly rainfall by summing the monthly totals. A major problem in this regard is that rainfall distributions for successive months may not be independent. Thus the rainfall accumulation problem must be represented as the summation of dependent random variables. This study is aimed to show if the statistical parameters for several stations within a particular catchment is known, then a weighted sum is used to determine a rainfall model for the entire local catchment. A spatial analysis for the sum of rainfall volumes from four selected meteorological stations within the same region using the monthly rainfall data is conducted. The sum of n correlated gamma variables is used to model the sum of monthly rainfall totals from four stations when there is significant correlation between the stations.

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