Peer Review #2 of "An improved framework to predict river flow time series data (v0.1)"

2019 ◽  
Keyword(s):  
Time Series ◽  
Peer Review ◽  
River Flow ◽  
Time Series Data ◽  
Flow Time ◽  
Series Data

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.

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 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.

Artificial neural network forecasting of nonlinear Markov processes

2007 ◽  
Vol 85 (3) ◽  
pp. 279-294 ◽  
Author(s):  
Sean W Fleming
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
River Flow ◽  
Time Series Data ◽  
Series Data ◽  
Underlying Structure ◽  
Good Prediction ◽  
Data Set ◽  
Artificial Neural

I assessed the performance characteristics of the feed-forward artificial neural network (ANN) as a first-order nonlinear Markov modelling technique. The ability to recover the underlying structure of five synthetic random time series was first tested. The method was then applied to an observed geophysical time series, and the results were compared against external empirical constraints and a simple representation of the underlying physics. The Monte Carlo experiments suggested that the ANN–Markov technique: (i) yields good prediction skill; (ii) in general, accurately retrieves the form of the iterative mapping, even for extremely noisy data; (iii) accomplishes the foregoing without any need to consider or adjust for the distributional characteristics of the data or driving noise; and (iv) accurately estimates the distribution of the strictly stochastic signal component. Application to a historical river-flow record again showed good forecast skill. Moreover, the robustness, flexibility, and simplicity of the method permitted easy identification of the fundamental nonlinear physical dynamics of this environmental system directly from the time series data, perhaps belying the common perception of ANNs as a strictly black-box prediction technique. The ANN–Markov technique may thus serve as a valuable data-driven tool for guiding the development of both process-based and parameteric statistical models. The lack of specific distributional assumptions and requirements notwithstanding, it was also found that manual distributional transformations may permit the method to be tuned to particular applications by emphasizing or de-emphasizing certain features of the data. Drawbacks to the method include substantial data-set length requirements, a general limitation of ANNs, as well as an inconsistent but potentially troubling tendency to partially imprint the form of the ANN activation function upon the estimated recursion relationship. PACS Nos.: 02.50.Ga, 05.10.–a, 05.45.Tp, 07.05.Mh, 02.50.Ey, 92.40.Fb

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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