scholarly journals The Importance of Monitoring River Water Discharge

2021 ◽  
Vol 3 ◽  
Author(s):  
P. J. Depetris
Keyword(s):  
Time Series ◽  
River Water ◽  
Water Discharge ◽  
Atmospheric Precipitation ◽  
Hydroelectric Dams ◽  
Discharge Time ◽  
Extreme Climatic Events ◽  
Discharge Monitoring ◽  
Climatic And Environmental Change

River discharge time series, originally recorded to anticipate floods and water scarcity, later became indispensable to design hydroelectric dams. Presently, discharge monitoring aids in detecting climatic and environmental change, because the discharge and quality of river water are functions of many climatic, biological, geological, and topographic variables coexisting in the basin. Climate change is altering the atmospheric precipitation distribution pattern-both, in time and space-as well as the occurrence of extreme climatic events. It is important the global upgrading of river gauging networks to unveil hydrological trends and changing atmospheric patterns. In so doing, discharge monitoring stations–and the resulting time series-may be, as well, invaluable in revealing the role played by significant environmental variables.

scholarly journals Upstream GPS Vertical Displacement and its Standardization for Mekong River Basin Surface Runoff Reconstruction and Estimation

2019 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Hok Sum Fok ◽  
Linghao Zhou ◽  
Yongxin Liu ◽  
Zhongtian Ma ◽  
Yutong Chen
Keyword(s):  
Time Series ◽  
River Water ◽  
River Basin ◽  
Relative Error ◽  
Surface Runoff ◽  
Water Discharge ◽  
Vertical Displacement ◽  
Mekong River ◽  
Mekong River Basin

Surface runoff (R), which is another expression for river water discharge of a river basin, is a critical measurement for regional water cycles. Over the past two decades, river water discharge has been widely investigated, which is based on remotely sensed hydraulic and hydrological variables as well as indices. This study aims to demonstrate the potential of upstream global positioning system (GPS) vertical displacement (VD) and its standardization to statistically derive R time series, which has not been reported in recent literature. The correlation between the in situ R at estuaries and averaged GPS-VD and its standardization in the river basin upstream on a monthly temporal scale of the Mekong River Basin (MRB) is examined. It was found that the reconstructed R time series from the latter agrees with and yields a similar performance to that from the terrestrial water storage based on gravimetric satellite (i.e., Gravity Recovery and Climate Experiment (GRACE)) and traditional remote sensing data. The reconstructed R time series from the standardized GPS-VD was found to have a 2–7% accuracy increase against those without standardization. On the other hand, it is comparable to data that are obtained by the Palmer drought severity index (PDSI). Similar accuracies are exhibited by the estimated R when externally validated through another station location with in situ time series. The comparison of the estimated R at the entrance of river delta against that at the estuaries indicates a 1–3% relative error induced by the residual ocean tidal effect at the estuary. The reconstructed R from the standardized GPS-VD yields the lowest total relative error of less than 9% when accounting for the main upstream area of the MRB. The remaining errors may be the result of the combined effect of the proposed methodology, remaining environmental signals in the data time series, and potential time lag (less than a month) between the upstream MRB and estuary.

scholarly journals Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1158 ◽  
Author(s):  
Katerina Tsakiri ◽  
Antonios Marsellos ◽  
Stelios Kapetanakis
Keyword(s):  
Neural Network ◽  
New York ◽  
Time Series ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Water Discharge ◽  
Flood Events ◽  
Discharge Time ◽  
Artificial Neural

This research introduces a hybrid model for forecasting river flood events with an example of the Mohawk River in New York. Time series analysis and artificial neural networks are combined for the explanation and forecasting of the daily water discharge using hydrogeological and climatic variables. A low pass filter (Kolmogorov–Zurbenko filter) is applied for the decomposition of the time series into different components (long, seasonal, and short-term components). For the prediction of the water discharge time series, each component has been described by applying the multiple linear regression models (MLR), and the artificial neural network (ANN) model. The MLR retains the advantage of the physical interpretation of the water discharge time series. We prove that time series decomposition is essential before the application of any model. Also, decomposition shows that the Mohawk River is affected by multiple time scale components that contribute to the hydrologic cycle of the included watersheds. Comparison of the models proves that the application of the ANN on the decomposed time series improves the accuracy of forecasting flood events. The hybrid model which consists of time series decomposition and artificial neural network leads to a forecasting up to 96% of the explanation for the water discharge time series.

scholarly journals ANALISIS MUTU AIR AKIBAT AKTIVITAS PENAMBANG PASIR DENGAN METODE STORET DI SUNGAI SP PADANG OKI

2021 ◽  
Vol 9 (1) ◽  
pp. 22-29
Author(s):  
Yosieguspa Yosieguspa ◽  
Ria Fahleny ◽  
Yuliani Yuliani
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Discharge ◽  
Open Pit ◽  
Quality Data ◽  
Open Pit Mining ◽  
Sand Mining ◽  
Water Quality Data ◽  
Washing Process

Sand mining in the village of Sp Padang is carried out in an open pit mining model through several processes, for example the washing process which is carried out to separate sand from other components.When the sludge in the form of mud and fine sand enters the river, this part causes the quality of river water around the sand mining location to decline. The purpose of this research was to determine the condition of water quality due to sand mining in the river Sp.Padang.The type of data collected is primary data. The research was conducted at 3 stations on the river Sp.Padang.The physico-chemical parameters of river water are used, namely: turbidity, temperature, pH, TSS, DO, BOD, COD, water discharge and current velocity. The data obtained from the laboratory were then analyzed, then comparisons were made with the Storet method.Sampling for water quality replaces the surrounding area with direct measurements and measurements made in the laboratory.The use of the Storet method refers to PP No. 82 of 2001. The principle of Storet is to integrate river water quality data with river water standards and then adjust it according to its use, by classifying water quality into four classes. The results of the data analysis of the quality of river water in Sp.Padang as a result of the sand mining activities are categorized as good class B (lightly polluted) with a score of -6. Sand mining activity affects the water quality of the Sirah River in Padang Island, OKI Regency.Key words : OKI, sand miners, storet method, water quality

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

scholarly journals An Efficient Prediction Model for Water Discharge in Schoharie Creek, NY

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Katerina G. Tsakiri ◽  
Antonios E. Marsellos ◽  
Igor G. Zurbenko
Keyword(s):  
New York ◽  
Time Series ◽  
Water Discharge ◽  
Winter Period ◽  
Statistical Methodology ◽  
Summer Period ◽  
Short Term ◽  
Efficient Prediction ◽  
Climatic Time Series

Flooding normally occurs during periods of excessive precipitation or thawing in the winter period (ice jam). Flooding is typically accompanied by an increase in river discharge. This paper presents a statistical model for the prediction and explanation of the water discharge time series using an example from the Schoharie Creek, New York (one of the principal tributaries of the Mohawk River). It is developed with a view to wider application in similar water basins. In this study a statistical methodology for the decomposition of the time series is used. The Kolmogorov-Zurbenko filter is used for the decomposition of the hydrological and climatic time series into the seasonal and the long and the short term component. We analyze the time series of the water discharge by using a summer and a winter model. The explanation of the water discharge has been improved up to 81%. The results show that as water discharge increases in the long term then the water table replenishes, and in the seasonal term it depletes. In the short term, the groundwater drops during the winter period, and it rises during the summer period. This methodology can be applied for the prediction of the water discharge at multiple sites.

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