Patching Monthly Streamflow Data — A Case Study Using the Em Algorithm and Kalman Filtering

Author(s):  
G. G. S. Pegram
2017 ◽  
Vol 2017 ◽  
pp. 1-16
Author(s):  
Shatha H. D. Al-Zakar ◽  
N. Şarlak ◽  
Omar M. A. Mahmood Agha

This study utilizes a recent nonparametric disaggregation K-nearest neighbor (KNN) model, to resample monthly flows depending on annual flows at different sites. Both temporal and spatial approaches will be followed in this model while preserving the distributional statistics of the observed data. This model assumes that a set of aggregated annual streamflows at a key station is available and desired for disaggregation to a corresponding series of streamflow at key station (temporal disaggregation) as well as at tributary stations (spatial disaggregation). The model is applied to the annual streamflow data of particular stations in the Kızılırmak Basin, which is exposed to drought periods during the years (1970–1974 and 1994-1995). The aim of this study is to find the possibilities of using the nonparametric approaches as generators of monthly flows, with emphasis on the ability to reproduce the statistics related to drought and storage analysis for the selected stations in Turkey. The results show that the spatial disaggregation approach has the ability to reproduce the historical data better than the temporal approach for the tested sites and provides a variety of generated monthly sequence flows that can then be utilized to analyze the performance of the water resources planning system.


Author(s):  
Sonam S. Dash ◽  
Dipaka R. Sena ◽  
Uday Mandal ◽  
Anil Kumar ◽  
Gopal Kumar ◽  
...  

Abstract The hydrologic behaviour of the Brahmani River basin (BRB) (39,633.90 km2), India was assessed for the base period (1970–1999) and future climate scenarios (2050) using the Soil and Water Assessment Tool (SWAT). Monthly streamflow data of 2000–2009 and 2010–2012 was used for calibration and validation, respectively, and performed satisfactorily with Nash-Sutcliffe Efficiency (ENS) of 0.52–0.55. The projected future climatic outcomes of the HadGEM2-ES model indicated that minimum temperature, maximum temperature, and precipitation may increase by 1.11–3.72 °C, 0.27–2.89 °C, and 16–263 mm, respectively, by 2050. The mean annual streamflow over the basin may increase by 20.86, 11.29, 4.45, and 37.94% under RCP 2.6, 4.5, 6.0, and 8.5, respectively, whereas the sediment yield is likely to increase by 23.34, 10.53, 2.45, and 27.62% under RCP 2.6, 4.5, 6.0, and 8.5, respectively, signifying RCP 8.5 to be the most adverse scenario for the BRB. Moreover, a ten-fold increase in environmental flow (defined as Q90) by the mid-century period is expected under the RCP 8.5 scenario. The vulnerable area assessment revealed that the increase in moderate and high erosion-prone regions will be more prevalent in the mid-century. The methodology developed herein could be successfully implemented for identification and prioritization of critical zones in worldwide river basins.


2012 ◽  
Vol 532-533 ◽  
pp. 1445-1449
Author(s):  
Ting Ting Tong ◽  
Zhen Hua Wu

EM algorithm is a common method to solve mixed model parameters in statistical classification of remote sensing image. The EM algorithm based on fuzzification is presented in this paper to use a fuzzy set to represent each training sample. Via the weighted degree of membership, different samples will be of different effect during iteration to decrease the impact of noise on parameter learning and to increase the convergence rate of algorithm. The function and accuracy of classification of image data can be completed preferably.


2015 ◽  
Vol 4 (2) ◽  
pp. 74
Author(s):  
MADE SUSILAWATI ◽  
KARTIKA SARI

Missing data often occur in agriculture and animal husbandry experiment. The missing data in experimental design makes the information that we get less complete. In this research, the missing data was estimated with Yates method and Expectation Maximization (EM) algorithm. The basic concept of the Yates method is to minimize sum square error (JKG), meanwhile the basic concept of the EM algorithm is to maximize the likelihood function. This research applied Balanced Lattice Design with 9 treatments, 4 replications and 3 group of each repetition. Missing data estimation results showed that the Yates method was better used for two of missing data in the position on a treatment, a column and random, meanwhile the EM algorithm was better used to estimate one of missing data and two of missing data in the position of a group and a replication. The comparison of the result JKG of ANOVA showed that JKG of incomplete data larger than JKG of incomplete data that has been added with estimator of data. This suggest  thatwe need to estimate the missing data.


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