Accuracy of stream-flow data

In this study, new hybrid model is developed by integrating two models, the discrete wavelet transform and least square support vector machine (WLSSVM) model. The hybrid model is then used to measure for monthly stream flow forecasting for two major rivers in Pakistan. The monthly stream flow forecasting results are obtained by applying this model individually to forecast the rivers flow data of the Indus River and Neelum Rivers. The root mean square error (RMSE), mean absolute error (MAE) and the correlation (R) statistics are used for evaluating the accuracy of the WLSSVM, the proposed model. The results are compared with the results obtained through LSSVM. The outcome of such comparison shows that WLSSVM model is more accurate and efficient than LSSVM.

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An analysis of stream-flow data for iowa

Transactions American Geophysical Union ◽

10.1029/tr018i002p00419 ◽

1937 ◽

Vol 18 (2) ◽

pp. 419

Author(s):

F. T. Mavis ◽

Edward Soucek

Keyword(s):

Stream Flow ◽

Flow Data

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Environmental DNA for the enumeration and management of Pacific salmon

10.1101/394445 ◽

2018 ◽

Cited By ~ 1

Author(s):

Taal Levi ◽

Jennifer M. Allen ◽

Donovan Bell ◽

John Joyce ◽

Joshua R. Russell ◽

...

Keyword(s):

Stream Flow ◽

Life Histories ◽

Pacific Salmon ◽

Environmental Dna ◽

Flow Data ◽

Counting Method ◽

Daily Sampling ◽

Fish Abundances ◽

Management Challenge ◽

Keystone Resource

AbstractPacific salmon are a keystone resource in Alaska, generating annual revenues of well over ∼US$500 million/yr. Due to their anadromous life history, adult spawners distribute amongst thousands of streams, posing a huge management challenge. Currently, spawners are enumerated at just a few streams because of reliance on human counters and, rarely, sonar. The ability to detect organisms by shed tissue (environmental DNA, eDNA) promises a more efficient counting method. However, although eDNA correlates generally with local fish abundances, we do not know if eDNA can accurately enumerate salmon. Here we show that daily, and near-daily, flow-corrected eDNA rate closely tracks daily numbers of returning sockeye and coho spawners and outmigrating sockeye smolts. eDNA thus promises accurate and efficient enumeration, but to deliver the most robust numbers will need higher-resolution stream-flow data, at-least-daily sampling, and a focus on species with simple life histories, since shedding rate varies amongst jacks, juveniles, and adults.

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Stochastic Generation of Low Stream Flow Data of Iokastis Stream, Kavala City, NE Greece

Proceedings ◽

10.3390/proceedings2110579 ◽

2018 ◽

Vol 2 (11) ◽

pp. 579

Author(s):

Thomas Papalaskaris ◽

Theologos Panagiotidis

Keyword(s):

Stream Flow ◽

Goodness Of Fit ◽

Probability Distributions ◽

Specific Area ◽

Low Flow ◽

Urban Stream ◽

Flow Data ◽

Goodness Of Fit Tests ◽

Chi Squared ◽

Anderson Darling

Only a few scientific research studies, especially dealing with extremely low flow conditions, have been compiled so far, in Greece. The present study, aiming to contribute in this specific area of hydrologic investigation, generates synthetic low stream flow time series of an entire calendar year considering the stream flow data recorded during a center interval period of the year 2015. We examined the goodness of fit tests of eleven theoretical probability distributions to daily low stream flow data acquired at a certain location of the absolutely channelized urban stream which crosses the roads junction formed by Iokastis road an Chrisostomou Smirnis road, Agios Loukas residential area, Kavala city, NE Greece, using a 3-inches conventional portable Parshall flume and calculated the corresponding probability distributions parameters. The Kolmogorov-Smirnov, Anderson-Darling and Chi-Squared, GOF tests were employed to show how well the probability distributions fitted the recorded data and the results were demonstrated through interactive tables providing us the ability to effectively decide which model best fits the observed data. Finally, the observed against the calculated low flow data are plotted, compiling a log-log scale chart and calculate statistics featuring the comparison between the recorded and the forecasted low flow data.

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Upper Washita River Experimental Watersheds: Reservoir, Groundwater, and Stream Flow Data

Journal of Environmental Quality ◽

10.2134/jeq2013.08.0329 ◽

2014 ◽

Vol 43 (4) ◽

pp. 1262-1272 ◽

Cited By ~ 4

Author(s):

Daniel N. Moriasi ◽

Patrick J. Starks ◽

Jorge A. Guzman ◽

Jurgen D. Garbrecht ◽

Jean L. Steiner ◽

...

Keyword(s):

Stream Flow ◽

Flow Data ◽

Experimental Watersheds

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Applying Deterministic Distributed Hydrological Model for Stream Flow Data Reproduction. A Case Study of Cu De Catchment, Vietnam

Procedia Engineering ◽

10.1016/j.proeng.2016.07.590 ◽

2016 ◽

Vol 154 ◽

pp. 1010-1017 ◽

Cited By ~ 3

Author(s):

Vo Ngoc Duong ◽

Nguyen Quang Binh ◽

Le Xuan Cuong ◽

Qiang Ma ◽

Philippe Gourbesville

Keyword(s):

Stream Flow ◽

Hydrological Model ◽

Distributed Hydrological Model ◽

Flow Data

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Comparison of Distribution Methods of Low Flow Analysis for Bandar Segamat, Johor

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.773-774.1266 ◽

2015 ◽

Vol 773-774 ◽

pp. 1266-1270

Author(s):

Yuliarahmadila Erfen ◽

Mohd Shalahuddin Adnan ◽

Noorfathiah Che Ali ◽

Nurul Farehah Amat ◽

Zawani Mohd Zahudi

Keyword(s):

Stream Flow ◽

Transition Period ◽

Monsoon Season ◽

Flow Analysis ◽

Gumbel Distribution ◽

Low Flow ◽

Flow Data ◽

Type Iii ◽

Pearson Type ◽

Pearson Type Iii Distribution

During the monsoon season, certain areas in Malaysia are experiencing a flood. While during the transition period Malaysia is experiencing a drought. This phenomenon could lead to severe disaster and precaution monitoring is needed to avoid this occurrences. Low flow during the dry season could lead to several negative effects on the river ecosystem. Thus, this study was conducted to determine the low flow frequency and intensity for the Segamat city. The duration for 2 years to 100 years based on the previous 20 years of stream flow data were used to calculated. Stream flow data were obtained from the Department of Irrigation and Drainage (DID). Two prominent distribution analyses methods known as Gumbel Distribution and Log pearson Type III Distribution were applied. The distribution results were validated using Root Mean Square Error (RMSE) and California method and Weibull method are selected. Based on the analyses results, it clearly shows that the distibution of low flow are between 1 m3/s to 10 m3/s. The flow are significantly correlate with the rainfall intensity. RMSE was selected based on the lowest value of 0.721 for the Gumble Distribution and 1.831 for Log Pearson Type III Distribution. Chi-square test shows a good agreement for Gumble Distribution (7.615<12.59) and Log Pearson Type III(5.201<11.07) using 5% significant level. The confident level form both tests are valid (p>0.05), thus, this results could be used for further analyses to alleviate the low flow in the study area.

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Daily river flow forecasting using wavelet ANN hybrid models

Journal of Hydroinformatics ◽

10.2166/hydro.2010.040 ◽

2010 ◽

Vol 13 (1) ◽

pp. 49-63 ◽

Cited By ~ 44

Author(s):

Niranjan Pramanik ◽

Rabindra K. Panda ◽

Adarsh Singh

Keyword(s):

Neural Network ◽

Stream Flow ◽

Network Models ◽

Hybrid Models ◽

Wavelet Coefficients ◽

Flow Data ◽

Neural Network Models ◽

Time Step ◽

Frequency Bands ◽

Ann Models

Advance time step stream flow forecasting is of paramount importance in controlling flood damage. During the past few decades, artificial neural network (ANN) techniques have been used extensively in stream flow forecasting and have proven to be a better technique than other forecasting methods such as multiple regression and general transfer function models. This study uses discrete wavelet transformation functions to preprocess the time series of the flow data into wavelet coefficients of different frequency bands. Effective wavelet coefficients are selected from the correlation analysis of the decomposed wavelet coefficients of all frequency bands with the observed flow data. Neural network models are proposed for 1-, 2- and 3-day flow forecasting at a site of Brahmani River, India. The effective wavelet coefficients are used as input to the neural network models. Both the wavelet and ANN techniques are employed to form a loose type of wavelet ANN hybrid model (NW). The hybrid models are trained using Levenberg–Marquart (LM) algorithm and the results are compared with simple ANN models. The results revealed that the predictabilities of NW models are significantly superior to conventional ANN models. The peak flow conditions are predicted with better accuracy using NW models than compared to ANN models.

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