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 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.

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>

scholarly journals An improved framework to predict river flow time series data

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7183 ◽  
Author(s):  
Hafiza Mamona Nazir ◽  
Ijaz Hussain ◽  
Ishfaq Ahmad ◽  
Muhammad Faisal ◽  
Ibrahim M. Almanjahie
Keyword(s):  
Time Series ◽  
Water Resource Management ◽  
River Flow ◽  
Time Series Data ◽  
Flow Time ◽  
Indus Basin ◽  
Series Data ◽  
Percentage Error ◽  
Empirical Bayesian ◽  
Noise Characteristics

Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF’s and noise free IMF’s are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF’s are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management.

scholarly journals Peramalan Banyak Pengunjung Pantai Pandasimo Bantul Menggunakan Regresi Runtun Waktu dan Seasonal Autoregressive Integrated Moving Average Exogenous

2021 ◽  
Vol 4 (1) ◽  
pp. 57
Author(s):  
Tito Tatag Prakoso ◽  
Etik Zukhronah ◽  
Hasih Pratiwi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Seasonal Pattern ◽  
Series Data ◽  
Gregorian Calendar ◽  
Time Series Regression ◽  
Autoregressive Integrated Moving Average ◽  
Past Data

<p>Forecasting is a ways to predict what will happen in the future based on the data in the past. Data on the number of visitors in Pandansimo beach are time series data. The pattern of the number of visitors in Pandansimo beach is influenced by holidays, so it looks like having a seasonal pattern. The majority of Indonesian citizens are Muslim who celebrate Eid Al-Fitr in every year. The determination of Eid Al-Fitr does not follow the Gregorian calendar, but based on the Lunar calendar. The variation of the calendar is about the determination of Eid Al-Fitr which usually changed in the Gregorian calendar, because in the Gregorian calendar, Eid Al-Fitr day will advance one month in every three years. Data that contain seasonal and calendar variations can be analyzed using time series regression and Seasonal Autoregressive Integrated Moving Average Exogenous  (SARIMAX) models. The aims of this study are to obtain a better model between time series regression and SARIMAX and to forecast the number of Pandansimo beach visitors using a better model. The result of this study indicates that the time series regression model is a better model. The forecasting from January to December 2018 in succession are 13255, 6674, 8643, 7639, 13255, 8713, 22635, 13255, 13255, 9590, 8549, 13255 visitors.</p><strong>Keywords: </strong>time series regression, seasonal, calendar variations, SARIMAX, forecasting

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.

scholarly journals Improving forecasting accuracy of river flow using gene expression programming based on wavelet decomposition and de-noising

2017 ◽  
Vol 49 (3) ◽  
pp. 711-723 ◽  
Author(s):  
Xiaorong Lu ◽  
Xuelei Wang ◽  
Liang Zhang ◽  
Ting Zhang ◽  
Chao Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Wavelet Decomposition ◽  
Water Resource Management ◽  
River Flow ◽  
Time Series Data ◽  
Gene Expression Programming ◽  
Stationary Time Series ◽  
Series Data ◽  
Effective Measure

Abstract Due to the effects of anthropogenic activities and natural climate change, streamflows of rivers have gradually decreased. In order to maintain reliable water supplies, reservoir operation and water resource management, accurate streamflow forecasts are very important. Based on monthly flow data from five hydrological stations in the middle and lower parts of the Hanjiang River Basin, between 1989 and 2009, we consider an efficient approach of adopting the gene expression programming model based on wavelet decomposition and de-noising (WDDGEP) to forecast river flow. Original flow time series data are initially decomposed into one sub-signal approximation and seven sub-signal details using the dmey wavelet. A wavelet threshold de-noising method is also applied in this study. Data that have been de-noised after decomposition are then adopted as inputs for WDDGEP models. Finally, the forecasted sub-signal results are summed to formulate an ensemble forecast for the original monthly flow series. A comparison of the prediction accuracy between the two models is based on three performance evaluation measures. Results show that the new WDDGEP models can effectively enhance accuracy in forecasting streamflow, and the proposed wavelet-based de-noising of the observed non-stationary time series is an effective measure to improve simulation accuracy.

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.

Sign in / Sign up

Export Citation Format

Share Document