scholarly journals Conditional Operation Rules For Optimal Conjunctive Use of Surface And Groundwater

2021 ◽  
Author(s):  
Mina Khosravi ◽  
Abbas Afshar ◽  
Amir Molajou
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
River Flow ◽  
Absolute Error ◽  
Water Shortage ◽  
Conjunctive Use ◽  
Flow Time ◽  
Worst Case ◽  
Operating Rules ◽  
Operation Rules

Abstract The current study presents an efficient method for deriving precise operation rules from all subsystems of a distributed conjunctive use system (CUS), including aquifer, river, and reservoir. Distributed aquifer simulation has been performed using the URM method. Given that the historical flow time series can only represent one of the possible situations in the future and its use to determine the performance of the CUS is certainly not very reliable, in this study, river flow uncertainties are implicitly considered. To develop the operation rules, the time series of river flow were generated using autoregressive model. Then, the operation optimization model of the system was implemented with the objective function of minimizing water shortage for different river flow time series. 70% of the data was used for model training and 30% for model validation. Finally, using the decision tree algorithm (M5Rules), the conditional operation rules were extracted and compared with the single linear regression operation rules. Using five efficiency criteria CC, MAE, RMSE, RAE, and RRSE, the comparison of conditional and single linear regression operating rules has been done. The results showed that the the conditional operation rules reduces relative absolute error by a minimum of 39% and a maximum of 71%. If the system is operated according to the conditional rules, in the worst case, the amount of water shortage imposed will be 16.61 MCM over ten years.

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.

Wavelet neural network model for river flow time series

2012 ◽  
Vol 165 (8) ◽  
pp. 425-439 ◽  
Author(s):  
Budu Krishna ◽  
Yellamelli Ramji Satyaji Rao ◽  
Purna Chandra Nayak
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
River Flow ◽  
Flow Time ◽  
Wavelet Neural Network

Optimal choice of type and order of river flow time series models

1982 ◽  
Vol 18 (4) ◽  
pp. 1097-1109 ◽  
Author(s):  
A. Ramachandra Rao ◽  
R. L. Kashyap ◽  
Liang-Tsi Mao
Keyword(s):  
Time Series ◽  
River Flow ◽  
Flow Time ◽  
Optimal Choice ◽  
Time Series Models

Newly explored machine learning model for river flow time series forecasting at Mary River, Australia

2020 ◽  
Vol 192 (12) ◽  
Author(s):  
Fang Cui ◽  
Sinan Q. Salih ◽  
Bahram Choubin ◽  
Suraj Kumar Bhagat ◽  
Pijush Samui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
River Flow ◽  
Learning Model ◽  
Flow Time ◽  
Time Series Forecasting ◽  
Machine Learning Model

scholarly journals Detecting determinism and nonlinearity in river-flow time series

2003 ◽  
Vol 48 (5) ◽  
pp. 763-780 ◽  
Author(s):  
AMILCARE PORPORATO ◽  
LUCA RIDOLFI
Keyword(s):  
Time Series ◽  
River Flow ◽  
Flow Time

scholarly journals Using variograms to detect and attribute hydrological change

2014 ◽  
Vol 11 (10) ◽  
pp. 11763-11795
Author(s):  
A. Chiverton ◽  
J. Hannaford ◽  
I. Holman ◽  
R. Corstanje ◽  
C. Prudhomme ◽  
...  
Keyword(s):  
Time Series ◽  
River Flow ◽  
Kendall Test ◽  
Flow Time ◽  
Quality Data ◽  
Dependence Structure ◽  
Monotonic Trend ◽  
Detection And Attribution ◽  
Precipitation Characteristics ◽  
Two Measures

Abstract. There have been many published studies aiming to identify temporal changes in river flow time-series, most of which use monotonic trend tests such as the Mann–Kendall test. Although robust to both the distribution of the data and incomplete records, these tests have important limitations and provide no information as to whether a change in variability mirrors a change in magnitude. This study develops a new method for detecting periods of change in a river flow time-series using Temporally Shifting Variograms, TSV, based on applying variograms to moving windows in a time-series and comparing these to the long-term average variogram, which characterises the temporal dependence structure in the river flow time-series. Variogram properties in each moving window can also be related to potential meteorological drivers. The method is applied to 94 UK catchments which were chosen to have minimal anthropogenic influences and good quality data between 1980 and 2012 inclusive. Each of the four variogram parameters (Range, Sill and two measures of semi-variance) characterise different aspects of change in the river flow regime, and have a different relationship with the precipitation characteristics. Three variogram parameters (the Sill and the two measures of semi-variance) are related to variability (either day-to-day or over the time-series) and have the largest correlations with indicators describing the magnitude and variability of precipitation. The fourth (the Range) is dependent on the relationship between the river flow on successive days and is most correlated with the length of wet and dry periods. Two prominent periods of change were identified: 1995 to 2001 and 2004 to 2012. The first period of change is attributed to an increase in the magnitude of rainfall whilst the second period is attributed to an increase in variability in the rainfall. The study demonstrates that variograms have considerable potential for application in the detection and attribution of temporal variability and change in hydrological systems.

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