scholarly journals Nonlinear Dynamic Modeling of Urban Water Consumption Using Chaotic Approach (Case Study: City of Kelowna)

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 753 ◽  
Author(s):  
Peyman Yousefi ◽  
Gregory Courtice ◽  
Gholamreza Naser ◽  
Hadi Mohammadi
Keyword(s):  
Phase Space ◽  
Time Scales ◽  
Water Consumption ◽  
Chaos Theory ◽  
Lead Time ◽  
Phase Space Reconstruction ◽  
Lead Times ◽  
Urban Water ◽  
Consumption Values ◽  
Forecasting Performance

This study investigated urban water consumption complexity using chaos theory to improve forecasting performance to help optimize system management, reduce costs and improve reliability. The objectives of this study were to (1) investigate urban water distribution consumption complexity and its role in forecasting technique performance, (2) evaluate forecasting models by periodicity and lead time, and (3) propose a suitable forecasting technique based on operator applications and performance through various time scales. An urban consumption dataset obtained from the City of Kelowna (British Columbia, Canada) was used as a test case to forecast future consumption values using varying lead times under different temporal scales to identify models which may improve forecasting performance. Chaos theory techniques were employed to inform model optimization. This study attempted to address the paucity of studies on chaos theory applications in water consumption forecasting. This was accomplished by applying non-linear approximation, dynamic investigation, and phase space reconstruction for input variables, to improve the accuracy in various periodicity and lead time. To reconstruct the phase space, lag time was calculated using average mutual information for daily resolution as 17 days to reconstruct the phase space. The optimum embedding dimension and correlation exponent for the phase space were 18 and 3.5, respectively. Comparing the results, the non-linear local approximation model provided the best performance. The forecasting horizon for the models was 122 days. Moreover, phase space reconstruction improved the accuracy of the models for the different lead times. The findings of this study may improve forecasting performance and provide evidence to support further investigation of the chaotic behaviour of water consumption values over different time scales.

scholarly journals The applicability of real-time flood forecasting correction techniques coupled with the Muskingum method

2019 ◽  
Vol 51 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Ruixiang Yang ◽  
Baodeng Hou ◽  
Weihua Xiao ◽  
Chuan Liang ◽  
Xuelei Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Flood Management ◽  
Flood Forecasting ◽  
Lead Times ◽  
Combination Forecasting ◽  
Permissible Range ◽  
Forecasting Performance ◽  
Muskingum Method ◽  
Better Than

Abstract Improving flood forecasting performance is critical for flood management. Real-time flood forecasting correction techniques (e.g., proportional correction (PC) and Kalman filter (KF)) coupled with the Muskingum method improve the forecasting performance but have limitations (e.g., short lead times and inadequate performance, respectively). Here, particle filter (PF) and combination forecasting (CF) are coupled with the Muskingum method and then applied to 10 flood events along the Shaxi River, China. Two indexes (overall consistency and permissible range) are selected to compare the performances of PC, KF, PF and CF for 3 h lead time. The changes in overall consistency for different lead times (1–6 h) are used to evaluate the applicability of PC, KF, PF and CF. The main conclusions are as follows: (1) for 3 h lead time, the two indexes indicate that the PF performance is optimal, followed in order by KF and PC; CF performance is close to PF and better than KF. (2) The performance of PC decreases faster than that of KF and PF with increases in the lead time. PC and PF are applicable for short (1–2 h) and long lead times (3–6 h), respectively. CF is applicable for 1–6 h lead times; however, it has no advantage over PC and PF for short and long lead times, respectively, which may be due to insufficient training and increase in cumulative errors.

Temporal Dynamics of Streamflow Using Complex Networks

2020 ◽  
Author(s):  
Bellie Sivakumar
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Complex Networks ◽  
Chaos Theory ◽  
Temporal Dynamics ◽  
The United States ◽  
Phase Space Reconstruction ◽  
Clustering Coefficient ◽  
Network Construction ◽  
Original Time

<p>Modeling the dynamics of streamflow continues to be highly challenging. The present study proposes a new approach to study the temporal dynamics of streamflow. The approach couples the concepts of complex networks and chaos theory. Applications of the concepts of complex networks for studying streamflow dynamics have been gaining momentum in recent years. A key step in such applications is the construction of the network – a network is a set of points (nodes) connected by lines (links). The present study uses the concept of phase-space reconstruction, an essential first step in chaos theory-based methods, for network construction to study the temporal dynamics of streamflow. The phase-space reconstruction involves representation of a single-variable time series in a multi-dimensional phase space using delay embedding. The reconstructed phase space is treated as a network, with the reconstructed vectors (rather than the original time series) serving as the nodes and the connections between them serving as the links. With this network construction, the clustering coefficient of the individual nodes and the entire network is calculated to assess the node and network strengths. The approach is employed to a large number of streamflow time series observed in the United States. The results indicate the usefulness and effectiveness of the phase-space reconstruction-based approach for network construction. The implications of the outcomes for identification of the appropriate type and complexity of model as well as for classification of catchments are discussed.</p>

Comparison of wavelet and machine learning methods for regional drought prediction

2021 ◽  
Author(s):  
Gilbert Hinge ◽  
Ashutosh Sharma
Keyword(s):  
Machine Learning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Support Vector ◽  
Lead Times ◽  
Model Parameters ◽  
Coupled Models ◽  
Drought Prediction ◽  
Forecasting Performance ◽  
Grid Points

<p>Droughts are considered as one of the most catastrophic natural disasters that affect humans and their surroundings at a larger spatial scale compared to other disasters. Rajasthan, one of India's semiarid states, is drought inclined and has experienced many drought events in the past. In this study, we evaluated different preprocessing and Machine Learning (ML) approaches for drought predictions in Rajasthan for a lead-time of up to 6 months. The Standardized Precipitation Index (SPI) was used as the drought quantifying measure to identify the drought events. SPI was calculated for 3, 6, and 12-month timescales over the last 115-year using monthly rainfall data at 119 grid stations.  ML techniques, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), and Linear Regression (LR), were used to evaluate their accuracy in drought forecasting over different lead times. Furthermore, two data processing methods, namely the Wavelet Packet Transform (WPT) and Discrete Wavelet Transform (DWT), have also been used to enhance the aforementioned ML models' predictability. At the outset, the preprocessed SPI data from both the methods were used as inputs for LR, SVR, and ANN to form a hybrid model. The hybrid models' drought predictability for a different lead-time was evaluated and compared with the standalone ML models. The forecasting performance of all the models for all 119 grid points was assessed with three statistical indices: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency (NSE). RMSE was used to select the optimal model parameters, such as the number of hidden neurons and the number of inputs in ANN, and the level of decomposition and mother wavelet in wavelet analysis.  Based on these measures, the coupled model showed better forecasting performance than the standalone ML models. The coupled WPT-ANN model shows superior predictability for most of the grid points than other coupled models and standalone models.  All models' performance improved as the timescale increased from 3 to 12 months for all the lead times. However, the model performance decreased as the lead time increased.  These findings indicate the necessity of processing the data before the application of any machine learning technique. The hybrid model's prediction performance also shows that it can be used for drought early warning systems in the state.</p>

LS-SVM Prediction Model Based on Phase Space Reconstruction for Dam Deformation

2013 ◽  
Vol 663 ◽  
pp. 55-59 ◽  
Author(s):  
He Zhi Liu ◽  
Song Lin Wang ◽  
Jing Yang Liu
Keyword(s):  
Phase Space ◽  
Multiple Regression ◽  
Phase Space Reconstruction ◽  
Regression Method ◽  
Least Square ◽  
Support Vector ◽  
Model Based ◽  
Forecasting Performance ◽  
Multiple Regression Method ◽  
Dam Deformation

This paper represents a Least-Square Support Vector Machine (LS-SVM) model based on the phase space reconstruction for forecasting nonlinear time series of dam deformation. Before training the LS-SVM, a time-delay reconstruction of phase space was made to view the dynamics of the monitoring data by using C-C method. And then the LS-SVM, which is a nonlinear, black-box regression method, was used for prediction. And the accuracy of this employed approach was examined by comparing it with multiple regression method. The experimental results indicate that the forecasting performance of the proposed method is significantly superior to that of the traditional multiple regression method.

scholarly journals The Effect of Catalogue Lead Time on Medium-Term Earthquake Forecasting with Application to New Zealand Data

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1264
Author(s):  
David A. Rhoades ◽  
Sepideh J. J. Rastin ◽  
Annemarie Christophersen
Keyword(s):  
New Zealand ◽  
Lead Time ◽  
Active Regions ◽  
Earthquake Forecasting ◽  
Scaling Relations ◽  
Lead Times ◽  
Medium Term ◽  
Trade Off ◽  
Forecasting Performance ◽  
Precursory Seismicity

‘Every Earthquake a Precursor According to Scale’ (EEPAS) is a catalogue-based model to forecast earthquakes within the coming months, years and decades, depending on magnitude. EEPAS has been shown to perform well in seismically active regions like New Zealand (NZ). It is based on the observation that seismicity increases prior to major earthquakes. This increase follows predictive scaling relations. For larger target earthquakes, the precursor time is longer and precursory seismicity may have occurred prior to the start of the catalogue. Here, we derive a formula for the completeness of precursory earthquake contributions to a target earthquake as a function of its magnitude and lead time, where the lead time is the length of time from the start of the catalogue to its time of occurrence. We develop two new versions of EEPAS and apply them to NZ data. The Fixed Lead time EEPAS (FLEEPAS) model is used to examine the effect of the lead time on forecasting, and the Fixed Lead time Compensated EEPAS (FLCEEPAS) model compensates for incompleteness of precursory earthquake contributions. FLEEPAS reveals a space-time trade-off of precursory seismicity that requires further investigation. Both models improve forecasting performance at short lead times, although the improvement is achieved in different ways.

scholarly journals Retrospective Sub-Seasonal Forecasts of Extreme Precipitation Events In The Arabian Peninsula Using Convective-Permitting Modeling

2021 ◽  
Author(s):  
Christoforus Bayu Risanto ◽  
Hsin-I Chang ◽  
Thang M. Luong ◽  
Christopher L. Castro ◽  
Hari P. Dasari ◽  
...  
Keyword(s):  
Time Scales ◽  
Red Sea ◽  
West Coast ◽  
Extreme Precipitation ◽  
Lead Time ◽  
Arabian Peninsula ◽  
Lead Times ◽  
The West ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract This paper is to demonstrate the potential of extreme cool-season precipitation forecasts in the Arabian Peninsula (AP) at sub-seasonal time scales, identify the region and periods of forecast opportunity, and investigate the predictability of synoptic-scale forcing at sub-seasonal time scales. To this end, we simulate 18 extreme precipitation events using the convective-permitting weather research and forecasting (CP-WRF) model with lateral boundary forcing from the European Centre of Medium-range Weather Forecasts sub-seasonal to seasonal reforecasts (ECMWF S2S reforecasts). The simulations are initiated at one-, two-, and three-week lead times. At all lead times, the CP-WRF improved the mean accumulated precipitation in the extratropical synoptic regimes over the west coastal and central AP and the central Red Sea. Based on categorical statistics with a threshold of 20-mm accumulated precipitation over 7 days, the CP-WRF accurately forecasted the precipitation over Jeddah, the west coast of AP, and the central Red Sea up to three weeks lead time. The relative operating characteristic curve reconfirmed the high forecasting skill of the CP-WRF, with an area under the curve above 0.5 in most of the events at all lead times. Finally, the correlation coefficients between the ECMWF and ECMWF reanalysis interim 500-hPa geopotential heights were higher in the events associated with the extratropical synoptic regime than in those associated with the tropical synoptic regime, regardless of lead time. Therefore, the convective-permitting model can potentially improve the accuracy of extreme winter precipitation forecasts at two-and three-week lead times over Jeddah, the west coast of AP, and the central Red Sea in the extratropical synoptic regime.

Analyzing the Lead Time and Shipping Lot-Size in a Chaotic Supply Network

2013 ◽  
pp. 163-177
Author(s):  
Mohammad Jafar Tarokh ◽  
Sina Golara
Keyword(s):  
Chaos Theory ◽  
Lead Time ◽  
Chaotic Behavior ◽  
Lead Times ◽  
Supply Network ◽  
Lot Size ◽  
Supply Networks ◽  
Key Characteristics ◽  
Multi Level ◽  
Pass Through

Supply network issues recently have attracted a lot of attention from industrial practitioners and academics worldwide. Supply networks are highly complex systems. The oscillations in demand and inventory as orders pass through the system have been widely studied in literature. Studies have shown that supply networks can display some of the key characteristics of chaotic systems. Chaos theory is the study of complex, nonlinear, dynamic systems; therefore it can be useful for studying the dynamics of supply networks. In this paper the authors implemented a system dynamic approach and simulated a chaotic multi-level supply network. The authors analyzed the effects of decision parameters, delivery lead time and shipping lot-size on chaotic behavior of the whole supply network. The simulation revealed that an increment in lead times or shipping lot-size has a similar impact on chaotic behavior of the system and reduces the chance of chaotic behavior occurrence.

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