scholarly journals Forecasting East Belitung Regency Rainfall Data by Reviewing Heteroscedasticity

2021 ◽  
Vol 926 (1) ◽  
pp. 012018
Author(s):  
E Kustiawan ◽  
Adriyansyah
Keyword(s):  
Time Series ◽  
Seasonal Pattern ◽  
Rainfall Data ◽  
Rainfall Forecasting ◽  
Irrigation Planning ◽  
Flood Prediction ◽  
Important Thing ◽  
Erosion Prediction ◽  
Wet Climate ◽  
The Many

Abstract East Belitung Regency is one of the regencies located on Belitung Island. East Belitung Regency has a tropical and wet climate with a fairly high variation of rainfall. Rainfall forecasting is an important thing to model because of the many uses of rainfall forecasting results such as irrigation planning, flood prediction, erosion prediction and others. This study aims to predict rainfall for the next 5 years by using a time series model by reviewing the heteroscedasticity of the data. From the results of the analysis of rainfall in East Belitung Regency with a seasonal pattern. The best model used is ARIMA (0, l, l)(2, l, l)12 with insignificant heteroscedasticity.

scholarly journals PERAMALAN PERSEDIAAN INFUS MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) PADA RUMAH SAKIT UMUM PUSAT SANGLAH

2018 ◽  
Vol 7 (2) ◽  
pp. 129
Author(s):  
I PUTU YUDI PRABHADIKA ◽  
NI KETUT TARI TASTRAWATI ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Time Series ◽  
General Hospital ◽  
Moving Average ◽  
Glucose Infusion ◽  
Series Method ◽  
Autoregressive Integrated Moving Average ◽  
Important Thing ◽  
Forecasting Method ◽  
Time Series Method ◽  
The Many

Infusion supplies are an important thing that must be considered by the hospital in meeting the needs of patients. This study aims to predict the need for infusion of 0.9% 500 ml of NaCl and 5% 500 ml glucose infusion at Sanglah General Hospital (RSUP) Sanglah so that the hospital can estimate the many infusions needed for the next six months. The forecasting method used in this research is the autoregressive integrated moving average (ARIMA) time series method. The results of this study indicate the need for infusion at Sanglah Hospital as many as 154,831 units for infusion of 0.9% NaCl 500 ml and 8,249 units for 5% 500 ml Glucose infusion.

Accuracy Validation of Flood Forecasting Method Based on Time Series Analysis Using Observed Water Level and Rainfall Data

2020 ◽  
Author(s):  
Naoki Koyama ◽  
Tadashi Yamada
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Water Level ◽  
First Grade ◽  
Flood Forecasting ◽  
Rainfall Data ◽  
Flood Prediction ◽  
Series Analysis ◽  
Level Data ◽  
Water Level Data

<p>The aim of this paper is to verify the accuracy of the real-time flood prediction model, using the time-series analysis. Forecast information of water level is important information that encourages residents to evacuate. Generally, flood forecasting is conducted by using runoff analysis. However, in developing countries, there are not enough hydrological data in a basin. Therefore, this study assumes where poor hydrologic data basin and evaluates it through reproducibility and prediction by using time series analysis which statistical model with the water level data and rainfall data. The model is applied to the one catchment of the upper Tone River basin, one of the first grade river in Japan. This method is possible to reproduce hydrograph, if the observation stations exist several points in the basin. And using the estimated parameters from past flood events, we can apply this method to predict the water level until the flood concentration time which the reference point and observation station. And until this time, the peak water level can be predicted with the accuracy of several 10cm. Prediction can be performed using only water level data, but by adding rainfall data, prediction can be performed for a longer time.</p>

nterrogative style in the commandments of fathers to children

2021 ◽  
Vol 29 ◽  
Author(s):  
Prof.A.Dr. Nahidha Sattar Hanadi Nizar Abdul-Amir
Keyword(s):  
Arabic Language ◽  
Important Thing ◽  
The Many ◽  
The Mind ◽  
To Receive

In the Arabic language، the question is a method intended to seek knowledge of a specific thing. The most important thing that distinguishes the Arabic language from other languages is the diversity of its methods and its validity for various sciences and arts. God Almighty has honored it by making it the language of the Noble Qur’an، which He revealed to all people، and the many methods and diversity in the language make speech more accurate in approach، fuller in phrase، and systematized path and most sincere outlet. Therefore، the question is one of the pillars of the methods of the construction language، as it is not based on the building only، but the meaning is based on it as well. The most important characteristic that distinguishes the interrogative method from other methods; The high and influential ability in the mind and the soul of the addressee and its appeal to consideration، meditation and contemplation، and the reason for this is that the question is originally issued by a soul wishing to obtain a request for understanding and knowledge; The question arises to alert the mind and provoke feelings، and makes the soul ready to receive the thoughts and images that come out of the person who is casting.

scholarly journals Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

2014 ◽  
Vol 18 (1) ◽  
pp. 353-365 ◽  
Author(s):  
U. Haberlandt ◽  
I. Radtke
Keyword(s):  
Time Series ◽  
Frequency Analysis ◽  
Model Calibration ◽  
Hydrological Model ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Rainfall Data ◽  
Flood Frequency Analysis ◽  
Peak Flows ◽  
Hourly Rainfall

Abstract. Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the application for derived flood frequency analysis.

scholarly journals Architecture of Artificial Neural Network in Identification of Internal Dynamics and Prediction of Dynamic System Rainfall Data Time Series

2012 ◽  
pp. 34-42
Author(s):  
Sanjeev Karmakar ◽  
Manoj Kumar Kowar ◽  
Pulak Guhathakurta
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Dynamic System ◽  
Long Range ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Global Minima ◽  
Internal Dynamics ◽  
Artificial Neural

The objective of this study is to expand and evaluate the back-propagation artificial neural network (BPANN) and to apply in the identification of internal dynamics of very high dynamic system such long-range total rainfall data time series. This objective is considered via comprehensive review of literature (1978-2011). It is found that, detail of discussion concerning the architecture of ANN for the same is rarely visible in the literature; however various applications of ANN are available. The detail architecture of BPANN with its parameters, i.e., learning rate, number of hidden layers, number of neurons in hidden layers, number of input vectors in input layer, initial and optimized weights etc., designed learning algorithm, observations of local and global minima, and results have been discussed. It is observed that obtaining global minima is almost complicated and always a temporal nervousness. However, achievement of global minima for the period of the training has been discussed. It is found that, the application of the BPANN on identification for internal dynamics and prediction for the long-range total annual rainfall has produced good results. The results are explained through the strong association between rainfall predictors i.e., climate parameter (independent parameter) and total annual rainfall (dependent parameter) are presented in this paper as well.

scholarly journals Study on Flood Prediction System Based on Radar Rainfall Data

2008 ◽  
Vol 41 (11) ◽  
pp. 1153-1162 ◽  
Author(s):  
Won-Il Kim ◽  
Kyoung-Doo Oh ◽  
Won-Sik Ahn ◽  
Byong-Ho Jun
Keyword(s):  
Rainfall Data ◽  
Prediction System ◽  
Radar Rainfall ◽  
Flood Prediction

scholarly journals Bayesian Time Series Modelling of the Italian Daily Rainfall Data using Mixed Distribution

2019 ◽  
Vol 8 (4) ◽  
pp. 2279-2288
Keyword(s):  
Time Series ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Seasonal Effects ◽  
Model Parameters ◽  
Daily Rainfall Data ◽  
Mixed Distribution ◽  
Time Series Modelling ◽  
Bayesian Time Series ◽  
The Ideal

A combination of continuous and discrete elements is referred to as a mixed distribution. For example, daily rainfall data consist of zero and positive values. We aim to develop a Bayesian time series model that captures the evolution of the daily rainfall data in Italy, focussing on directly linking the amount and occurrence of rainfall. Two gamma (G1 and G2) distributions with different parameterisations and lognormal distribution were investigated to identify the ideal distribution representing the amount process. Truncated Fourier series was used to incorporate the seasonal effects which captures the variability in daily rainfall amounts throughout the year. A first-order Markov chain was used to model rainfall occurrence conditional on the presence or absence of rainfall on the previous day. We also built a hierarchical prior structure to represent our subjective beliefs and capture the initial uncertainties of the unknown model parameters for both amount and occurrence processes. The daily rainfall data from Urbino rain gauge station in Italy were then used to demonstrate the applicability of our proposed methods. Residual analysis and posterior predictive checking method were utilised to assess the adequacy of model fit. In conclusion, we clearly found that our proposed method satisfactorily and accurately fits the Italian daily rainfall data. The gamma distribution was found to be the ideal probability density function to represent the amount of daily rainfall.

Guessing the missing half of a geophysical field with blunt extension of discrete Universal Multifractal cascades

2021 ◽  
Author(s):  
Auguste Gires ◽  
Ioulia Tchiguirinskaia ◽  
Daniel Schertzer
Keyword(s):  
Time Series ◽  
Financial Support ◽  
Space Time ◽  
Rainfall Data ◽  
Second Step ◽  
Geophysical Field ◽  
Geophysical Fields ◽  
Wide Range ◽  
The Common ◽  
Partial Financial Support

<p>Universal Multifractals have been widely used to characterize and simulate geophysical fields extremely variable over a wide range of scales such as rainfall. Despite strong limitations, notably its non-stationnarity, discrete cascades are often used to simulate such fields. Recently, blunt cascades have been introduced in 1D and 2D to cope with this issue while remaining in the simple framework of discrete cascades. It basically consists in geometrically interpolating over moving windows the multiplicative increments at each cascade steps.</p><p> </p><p>In this paper, we first suggest an extension of this blunt cascades to space-time processes. Multifractal expected behaviour is theoretically established and numerically confirmed. In a second step, a methodology to address the common issue of guessing the missing half of a field is developed using this framework. It basically consists in reconstructing the increments of the known portion of the field, and then stochastically simulating the ones for the new portion, while ensuring the blunting the increments on the portion joining the two parts of the fields. The approach is tested with time series, maps and in a space-time framework. Initial tests with rainfall data are presented.</p><p> </p><p>Authors acknowledge the RW-Turb project (supported by the French National Research Agency - ANR-19-CE05-0022), for partial financial support.</p>

scholarly journals The association between seasonal influenza-like illness cases and foetal death: a time series analysis

2018 ◽  
Vol 147 ◽  
Author(s):  
I. S. Rasmussen ◽  
L. H. Mortensen ◽  
T. G. Krause ◽  
A-M. Nybo Andersen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Spontaneous Abortion ◽  
Seasonal Pattern ◽  
Influenza Virus Infection ◽  
Temporal Association ◽  
Spontaneous Abortions ◽  
Foetal Death ◽  
Influenza Like Illness ◽  
Series Analysis

AbstractIt has been reported that foetal death follows a seasonal pattern. Influenza virus infection has been postulated as one possible contributor to this seasonal variation. This ecological study explored the temporal association between the influenza activity and the frequency of foetal death. Time series analysis was conducted using weekly influenza-like illness consultation proportions from the Danish sentinel surveillance system and weekly proportions of spontaneous abortions and stillbirths from hospital registers from 1994 to 2009. The association was examined in an autoregressive (AR) integrated (I) moving average (MA) model and subsequently analysed with cross-correlation functions. Our findings confirmed the well-known seasonality in influenza, but also seasonality in spontaneous abortion. No clear pattern of seasonality was found for stillbirths, although the analysis exposed dependency between observations. One final AR integrated MA model was identified for the influenza-like illness (ILI) series. We found no statistically significant relationship between weekly influenza-like illness consultation proportions and weekly spontaneous abortion proportions (five lags: P = 0.52; 11 lags: P = 0.91) or weekly stillbirths (five lags: P = 0.93; 11 lags: P = 0.40). Exposure to circulating influenza during pregnancy was not associated with rates of spontaneous abortions or stillbirths. Seasonal variations in spontaneous abortion were confirmed and this phenomenon needs further investigation.

scholarly journals Feed Forward Neural Network Modeling for Rainfall Prediction

2018 ◽  
Vol 73 ◽  
pp. 05017
Author(s):  
Yasin Hasbi ◽  
Warsito Budi ◽  
Santoso Rukun
Keyword(s):  
Neural Network ◽  
Autocorrelation Function ◽  
Seasonal Pattern ◽  
Optimization Method ◽  
Rainfall Data ◽  
Neural Network Modeling ◽  
Least Mean Square ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Hidden Layer

Prediction of rainfall data by using Feed Forward Neural Network (FFNN) model is proposed. FFNN is a class of neural network which has three layers for processing. In time series prediction, including in case of rainfall data, the input layer is the past values of the same series up to certain lag and the output layer is the current value. Beside a few lagged times, the seasonal pattern also considered as an important aspect of choosing the potential input. The autocorrelation function and partial autocorrelation function patterns are used as aid of selecting the input. In the second layer called hidden layer, the logistic sigmoid is used as activation function because of the monotonic and differentiable. Processing is done by the weighted summing of the input variables and transfer process in the hidden layer. Backpropagation algorithm is applied in the training process. Some gradient based optimization methods are used to obtain the connection weights of FFNN model. The prediction is the output resulting of the process in the last layer. In each optimization method, the looping process is performed several times in order to get the most suitable result in various composition of separating data. The best one is chosen by the least mean square error (MSE) criteria. The least of in-sample and out-sample predictions from the repeating results been the base of choosing the best optimization method. In this study, the model is applied in the ten-daily rainfall data of ZOM 136 Cokrotulung Klaten. Simulation results give a consecution that the more complex architecture is not guarantee the better prediction.

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