scholarly journals Study on The Physical Characteristics and Hydrology of 15 Watershed in East Java

2013 ◽  
Vol 27 (2) ◽  
pp. 159
Author(s):  
Indarto Indarto
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Daily Rainfall ◽  
Study Data ◽  
Series Data ◽  
Simple Arithmetic ◽  
Discharge Data ◽  
Average Value ◽  
Physical Data ◽  
Areal Rainfall

The study demonstrated the application of statistical method to describe physical and hydro-meteorological characteristics by means of time series analysis.  Fifteen(15) watersheds in East Java were selected for this study. Data input for the analysis include: physical data, rainfall and discharge. Physical data of the watershed (topography, river network, land use, and soil type) are extracted from existing database and treated using GIS Software. Daily rainfall data were collected from existing pluviometers around the region. Daily discharge data were obtained from measurement station located at the outlet of each watershed. Areal Rainfall for each watershed was determined using average value of existing pluviometers around the watershed and determined using simple arithmetic method. These time series data are then imported to RAP (River Analysis Package).  Analysis on the RAP, include: general statistical, flow duration curve (FDC), and baseflow analysis. The result then presented in graphic and tables. Research shows that among the watersheds have different physical and hydrological characteristics.

LamaH: Large-sample Data for Hydrology in Central Europe

2021 ◽  
Author(s):  
Christoph Klingler ◽  
Mathew Herrnegger ◽  
Frederik Kratzert ◽  
Karsten Schulz
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Vegetation Indices ◽  
River Network ◽  
Series Data ◽  
High Temporal Resolution ◽  
Stream Length ◽  
Discharge Data ◽  
Large Sample ◽  
Access To Data

<p>Open large-sample datasets are important for various reasons: i) they enable large-sample analyses, ii) they democratize access to data, iii) they enable large-sample comparative studies and foster reproducibility, and iv) they are a key driver for recent developments of machine-learning based modelling approaches.</p><p>Recently, various large-sample datasets have been released (e.g. different country-specific CAMELS datasets), however, all of them contain only data of individual catchments distributed across entire countries and not connected river networks.</p><p>Here, we present LamaH, a new dataset covering all of Austria and the foreign upstream areas of the Danube, spanning a total of 170.000 km² in 9 different countries with discharge observations for 882 gauges. The dataset also includes 15 different meteorological time series, derived from ERA5-Land, for two different basin delineations: First, corresponding to the entire upstream area of a particular gauge, and second, corresponding only to the area between a particular gauge and its upstream gauges. The time series data for both, meteorological and discharge data, is included in hourly and daily resolution and covers a period of over 35 years (with some exceptions in discharge data for a couple of gauges).</p><p>Sticking closely to the CAMELS datasets, LamaH also contains more than 60 catchment attributes, derived for both types of basin delineations. The attributes include climatic, hydrological and vegetation indices, land cover information, as well as soil, geological and topographical properties. Additionally, the runoff gauges are classified by over 20 different attributes, including information about human impact and indicators for data quality and completeness. Lastly, LamaH also contains attributes for the river network itself, like gauge topology, stream length and the slope between two sequential gauges.</p><p>Given the scope of LamaH, we hope that this dataset will serve as a solid database for further investigations in various tasks of hydrology. The extent of data combined with the interconnected river network and the high temporal resolution of the time series might reveal deeper insights into water transfer and storage with appropriate methods of modelling.</p>

Prediction and Analysis of Gold Prices using Ensemble Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 4367-4374
Author(s):  
Gudipally Chandrashakar
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Gold Price ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Average Value ◽  
Ensemble Machine Learning

In this article, we used historical time series data up to the current day gold price. In this study of predicting gold price, we consider few correlating factors like silver price, copper price, standard, and poor’s 500 value, dollar-rupee exchange rate, Dow Jones Industrial Average Value. Considering the prices of every correlating factor and gold price data where dates ranging from 2008 January to 2021 February. Few algorithms of machine learning are used to analyze the time-series data are Random Forest Regression, Support Vector Regressor, Linear Regressor, ExtraTrees Regressor and Gradient boosting Regression. While seeing the results the Extra Tree Regressor algorithm gives the predicted value of gold prices more accurately.

Pendugaan Data Runtun Waktu Debit Aliran Sungai Cikeruh dengan Metode Thomas-Fiering

2021 ◽  
Vol 1 (1) ◽  
pp. 13-20
Author(s):  
Meiske Shabrina Pesik ◽  
Didi Suhaedi ◽  
M. Yusuf Fajar
Keyword(s):  
Time Series ◽  
River Flow ◽  
Time Series Data ◽  
Seasonal Pattern ◽  
Series Data ◽  
Discharge Data ◽  
Flow Discharge ◽  
Mean Error ◽  
The People ◽  
Monthly Period

Abstract. The Cikeruh River is a source of water for the people who live in the watershed area. The shift in land management has resulted in problems in the availability of water resources. As a policy to overcome this problem, an estimation of the flow rate of the Cikeruh river was carried out. Cikeruh river flow discharge data is observational data with a monthly period included in time series data or time series data. This data has a seasonal pattern so that the method that can be used to predict the discharge data is the Thomas-Fiering Method. To estimate the discharge data for 2018, the Cikeruh river flow discharge data were used every month from 2011 to 2017 as many as 84 historical data. Then after getting the results of the 2018 debit data estimation, the mean error value calculated using Thomas-Fiering was 0.0291. Abstrak. Sungai Cikeruh merupakan sumber air bagi masyarakat yang bermukim di wilayah daerah aliran sungai. Terjadinya pergeseran tata kelola lahan mengakibatkan permasalahan ketersediaan sumber daya air. Sebagai suatu kebijakan untuk mengatasi permasalahan tersebut maka dilakukan pendugaan debit aliran sungai Cikeruh. Data debit aliran sungai Cikeruh merupakan data pengamatan dengan periode bulanan yang termasuk dalam data time series atau data runtun waktu. Data ini memiliki pola  musiman sehingga metode yang dapat digunakan untuk membuat pendugaan data debit adalah Metode Thomas-Fiering. Untuk menduga data debit tahun 2018 digunakan data debit aliran sungai Cikeruh setiap bulannya dari tahun 2011 sampai 2017 sebanyak 84 data historis. Kemudian setelah mendapatkan hasil pendugaan data debit tahun 2018 didapatkan nilai Mean Error perhitungan menggunakan Thomas-Fiering adalah 0.0291.

scholarly journals Random Modeling of Daily Rainfall and Runoff Using a Seasonal Model and Wavelet Denoising

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Chien-ming Chou
Keyword(s):  
Time Series ◽  
Probability Distribution ◽  
Time Series Data ◽  
Daily Rainfall ◽  
Wavelet Denoising ◽  
Time Series Forecasting ◽  
Series Data ◽  
Perturbation Term ◽  
Seasonal Model ◽  
Runoff Modeling

Instead of Fourier smoothing, this study applied wavelet denoising to acquire the smooth seasonal mean and corresponding perturbation term from daily rainfall and runoff data in traditional seasonal models, which use seasonal means for hydrological time series forecasting. The denoised rainfall and runoff time series data were regarded as the smooth seasonal mean. The probability distribution of the percentage coefficients can be obtained from calibrated daily rainfall and runoff data. For validated daily rainfall and runoff data, percentage coefficients were randomly generated according to the probability distribution and the law of linear proportion. Multiplying the generated percentage coefficient by the smooth seasonal mean resulted in the corresponding perturbation term. Random modeling of daily rainfall and runoff can be obtained by adding the perturbation term to the smooth seasonal mean. To verify the accuracy of the proposed method, daily rainfall and runoff data for the Wu-Tu watershed were analyzed. The analytical results demonstrate that wavelet denoising enhances the precision of daily rainfall and runoff modeling of the seasonal model. In addition, the wavelet denoising technique proposed in this study can obtain the smooth seasonal mean of rainfall and runoff processes and is suitable for modeling actual daily rainfall and runoff processes.

scholarly journals Temporal Variation of Rainfall in the Bagmati River Basin, Nepal

2016 ◽  
Vol 16 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Rajendra Man Shrestha ◽  
Azaya Bikram Sthapit
Keyword(s):  
Time Series ◽  
Temporal Variation ◽  
River Basin ◽  
Summer Monsoon ◽  
Time Series Data ◽  
Monsoon Season ◽  
Series Data ◽  
Upward Trend ◽  
Areal Rainfall ◽  
Bagmati River

The main aim of the study was to identify the temporal variation of rainfall in the Bagmati River basin, Nepal using  data available at Department of Hydrology and Meteorology, Government of Nepal. The time series data for the  period of 1981-2008 were analyzed by using non-parametric Mann-Kendal test, Spearman’s’ Rho and a parametric  linear regression. The results showed that there was a significantly increasing upward trend of the annual mean of  weighted areal rainfall, with a rate of 2.2 mm per year. Trend analysis of the monthly time series of weighted areal  rainfall showed a significant upward trend in the months of summer monsoon season (June and July). However,  there were no such significant result in the other season/months. The increasing trend in the summer monsoon  might lead to severe flooding in future.Nepal Journal of Science and Technology Vol. 16, No.1 (2015) pp. 31-40

scholarly journals Application of Vector Auto Regression Model for Rainfall-River Discharge Analysis

2015 ◽  
Vol 29 (1) ◽  
Author(s):  
Sri Hartini ◽  
Muhammad Pramono Hadi ◽  
Sudibyakto Sudibyakto ◽  
Aris Poniman
Keyword(s):  
Time Series ◽  
River Discharge ◽  
Time Series Data ◽  
Series Data ◽  
Var Model ◽  
Time Interval ◽  
Discharge Data ◽  
Vector Auto Regression ◽  
The Relationship ◽  
Auto Regression

River discharge quantity is highly depended on rainfall and initial condition of river discharge; hence, the river discharge has auto-correlation relationships. This study used Vector Auto Regression (VAR) model for analysing the relationship between rainfall and river discharge variables. VAR model was selected by considering the nature of the relationship between rainfall and river discharge as well as the types of rainfall and discharge data, which are in form of time series data. This research was conducted by using daily rainfall and river discharge data obtained from three weirs, namely Sojomerto and Juwero, in Kendal Regency and Glapan in Demak Regency, Central Java Province. Result of the causality tests shows significant relationship of both variables, those on the influence of rainfall to river discharge as well as the influence of river discharge to rainfall variables. The significance relationships of river discharge to rainfall indicate that the rainfall in this area has moved downstream. In addition, the form of VAR model could explain the variety of the relationships ranging between 6.4% - 70.1%. These analyses could be improved by using rainfall and river discharge time series data measured in shorter time interval but in longer period.

scholarly journals Prediksi Emisi CO2 dengan Analisis Runtun Waktu

2019 ◽  
Vol 1 (1) ◽  
pp. 22-16
Author(s):  
Primadina Hasanah ◽  
Irma Fitria
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Co2 Emission ◽  
Time Series Data ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Series Data ◽  
Average Value ◽  
Trend Component

Global warming is caused by various factors, one of them is the emission of CO2. Time series data of CO2 emission will be analyzed using moving average and exponential smoothing to forecast the CO2 emission of the period ahead. Both models provide estimates of forecasting based on the average value of the previous data and can be used for forecasting time series data containing trend component. The best models are selected based on the smallest error value based on the criteria of MAPE, MSD, and MAD

scholarly journals A new approach for suspended sediment load calculation based on generated flow discharge considering climate change

2021 ◽  
Author(s):  
Arash Adib ◽  
Ozgur Kisi ◽  
Shekoofeh Khoramgah ◽  
Hamid Reza Gafouri ◽  
Ali Liaghat ◽  
...  
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Sediment Load ◽  
Time Series Data ◽  
Series Data ◽  
Suspended Sediment Load ◽  
Discharge Time ◽  
Discharge Data ◽  
New Approach ◽  
Flow Discharge

Abstract Use of general circulation models (GCMs) is common for forecasting of hydrometric and meteorological parameters, but the uncertainty of these models is high. This study developed a new approach for calculation of suspended sediment load (SSL) using historical flow discharge data and SSL data of the Idanak hydrometric station on the Marun River (in the southwest of Iran) from 1968 to 2014. This approach derived sediment rating relation by observed data and determined trend of flow discharge time series data by Mann-Kendall nonparametric trend (MK) test and Theil-Sen approach (TSA). Then, the SSL was calculated for a future period based on forecasted flow discharge data by TSA. Also, one hundred annual and monthly flow discharge time series data (for the duration of 40 years) were generated by the Markov chain and the Monte Carlo (MC) methods and it calculated 90% of total prediction uncertainty bounds for flow discharge time series data by Latin Hypercube Sampling (LHS) on Monte Carlo (MC). It is observed that flow discharge and SSL will increase in summer and will reduce in spring. Also, the annual amount of SSL will reduce from 2,811.15 ton/day to 1,341.25 and 962.05 ton/day in the near and far future, respectively.

scholarly journals Research of Forecasting on Tourist Arrivals to Malaysia

2019 ◽  
Vol 8 (12S2) ◽  
pp. 686-689
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Linear Trend ◽  
Study Data ◽  
Series Data ◽  
Data Series ◽  
Suitable Model ◽  
Absolute Deviation ◽  
Double Exponential Smoothing ◽  
High Level

Tourists get attracted towards Malaysia because of our culture and geography. Apart from heritage and culture, the tourists from all over the world visit here for various purpose. Therefore, forecasting tourist arrivals with high level of accuracy becomes important because it can ensure the development of tourism industries. So, this study focuses on tourist arrivals to Malaysia. This paper attempts to define the component of patterns exist in the time series data, to determine the most suitable model best fits in data series by using the error measure that are Mean Square Error (MSE) and Mean Absolute Deviation (MAD) and to forecast the one-step ahead forecast on the best model. In this study, data of tourist arrivals to Malaysia has been obtained from January 2000 until December 2018. All 228 monthly data were analyzed by using selected Univariate Modeling. The result found that tourist arrivals to Malaysia has a linear trend model and Double Exponential Smoothing with α = 0.17 was the best model for this time series.

scholarly journals PENGARUH PENANAMAN MODAL DALAM NEGERI, PENANAMAN MODAL ASING DAN TENAGA KERJA TERHADAP PERTUMBUHAN EKONOMI DI SUMATERA BARAT

2019 ◽  
Vol 1 (4) ◽  
pp. 29
Author(s):  
Nickitha Dina Fauzy ◽  
Hasdi Aimon
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Regression Analysis ◽  
Data Collection ◽  
Foreign Investment ◽  
Time Series Data ◽  
Secondary Data ◽  
Study Data ◽  
Series Data ◽  
Domestic Investment

This study explains to determine the effect of domestic investment, foreign investment, and labor on economic growth in West Sumatera. The data used is secondary data in the form of time series data from 1988-2018, with documentation and library study data collection techniques obtainedfrom relevant institutions and agencies. The variables used are economic growth (PDRB), domestic investment, foreign investment and labor, the research methods used are: (1) Multiple Regression Analysis (OLS), (2) Classical Assumption Test which states that: (1) investment in the country has a positive and insignificant effect on economic growth in West Sumatera. (2) foreign investment has a positive and significant effect on economic growth in West Sumatera. (3) labor force has a positive and not significant effect on economic growth in West Sumatera. So only foreign investment has a positive and significant impact on economic growth in West Sumatera. 

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