scholarly journals Application of Entropy Spectral Method for Streamflow Forecasting in Northwest China

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 132 ◽  
Author(s):  
Gengxi Zhang ◽  
Zhenghong Zhou ◽  
Xiaoling Su ◽  
Olusola Ayantobo
Keyword(s):  
Flood Control ◽  
Performance Metrics ◽  
Configurational Entropy ◽  
Northwest China ◽  
Low Flow ◽  
Future Research ◽  
Streamflow Forecasting ◽  
Periodic Patterns ◽  
Forecasting Accuracy ◽  
Monthly Streamflow

Streamflow forecasting is vital for reservoir operation, flood control, power generation, river ecological restoration, irrigation and navigation. Although monthly streamflow time series are statistic, they also exhibit seasonal and periodic patterns. Using maximum Burg entropy, maximum configurational entropy and minimum relative entropy, the forecasting models for monthly streamflow series were constructed for five hydrological stations in northwest China. The evaluation criteria of average relative error (RE), root mean square error (RMSE), correlation coefficient (R) and determination coefficient (DC) were selected as performance metrics. Results indicated that the RESA model had the highest forecasting accuracy, followed by the CESA model. However, the BESA model had the highest forecasting accuracy in a low-flow period, and the prediction accuracies of RESA and CESA models in the flood season were relatively higher. In future research, these entropy spectral analysis methods can further be applied to other rivers to verify the applicability in the forecasting of monthly streamflow in China.

scholarly journals GRACE Satellites Enable Long-Lead Forecasts of Mountain Contributions to Streamflow in the Low-Flow Season

2021 ◽  
Vol 13 (10) ◽  
pp. 1993
Author(s):  
Xingcai Liu ◽  
Qiuhong Tang ◽  
Seyed-Mohammad Hosseini-Moghari ◽  
Xiaogang Shi ◽  
Min-Hui Lo ◽  
...  
Keyword(s):  
Linear Models ◽  
Yellow River ◽  
Low Flow ◽  
Lead Times ◽  
High Mountain ◽  
Streamflow Forecasting ◽  
Mountain Areas ◽  
Model Based ◽  
Monthly Streamflow ◽  
Terrestrial Water

Terrestrial water storage (TWS) in high mountain areas contributes large runoff volumes to nearby lowlands during the low-flow season when streamflow is critical to downstream water supplies. The potential for TWS from GRACE (Gravity Recovery and Climate Experiment) satellites to provide long-lead streamflow forecasting in adjacent lowlands during the low-flow season was assessed using the upper Yellow River as a case study. Two linear models were trained for forecasting monthly streamflow with and without TWS anomaly (TWSA) from 2002 to 2016. Results show that the model based on streamflow and TWSA is superior to the model based on streamflow alone at up to a five-month lead-time. The inclusion of TWSA reduced errors in streamflow forecasts by 25% to 50%, with 3–5-month lead-times, which represents the role of terrestrial hydrologic memory in streamflow changes during the low-flow season. This study underscores the high potential of streamflow forecasting using GRACE data with long lead-times that should improve water management in mountainous water towers and downstream areas.

scholarly journals Multiscale nonlinear model for monthly streamflow forecasting: a wavelet-based approach

2011 ◽  
Vol 14 (2) ◽  
pp. 424-442 ◽  
Author(s):  
Maheswaran Rathinasamy ◽  
Rakesh Khosa
Keyword(s):  
Nonlinear Models ◽  
Model Performance ◽  
Low Flow ◽  
Data Series ◽  
Streamflow Forecasting ◽  
Volterra Models ◽  
Proposed Model ◽  
Monthly Streamflow ◽  
Streamflow Data ◽  
Flow Medium

The dynamics of the streamflow in rivers involve nonlinear and multiscale phenomena. An attempt is made to develop nonlinear models combining wavelet decomposition with Volterra models. This paper describes a methodology to develop one-month-ahead forecasts of streamflow using multiscale nonlinear models. The method uses the concept of multiresolution decomposition using wavelets in order to represent the underlying integrated streamflow dynamics and this information, across scales, is then linked together using the first- and second-order Volterra kernels. The model is applied to 30 river data series from the western USA. The mean monthly data series of 30 rivers are grouped under the categories low, medium and high. The study indicated the presence of multiscale phenomena and discernable nonlinear characteristics in the streamflow data. Detailed analyses and results are presented only for three stations, selected to represent the low-flow, medium-flow and high-flow categories, respectively. The proposed model performance is good for all the flow regimes when compared with both the ARMA-type models as well as nonlinear models based on chaos theory.

scholarly journals Monthly Flow Duration Curve Model for Ungauged River Basins

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 338 ◽  
Author(s):  
Halil Ibrahim Burgan ◽  
Hafzullah Aksoy
Keyword(s):  
River Basin ◽  
Performance Metrics ◽  
Low Flow ◽  
Flow Duration Curve ◽  
Flow Duration ◽  
Normal Probability ◽  
Monthly Streamflow ◽  
Flow Part ◽  
Streamflow Data ◽  
Normal Probability Distribution

Flow duration curve (FDC) is widely used in hydrology to assess streamflow in a river basin. In this study, a simple FDC model is developed for monthly streamflow data. The model consists of several steps including the nondimensionalization and then normalization in case the monthly streamflow data do not fit the normal probability distribution function. The normalized quantiles are calculated after which a back transformation is applied to the normalized quantiles to return back to the original dimensional streamflow data. In order to calculate annual streamflow of the river basin, an empirical regression equation is proposed using the drainage area and the annual total precipitation only as the input. As the final step of the model, dimensional quantiles of FDC are calculated. Ceyhan River basin in southern Turkey is chosen for the case study. Forty-two streamflow gauging stations are considered; two thirds of the gauging stations are used for the model calibration, and one third for validation. The modeled FDCs are compared to the observation and assessed with a number of performance metrics. They are found similar to the observed ones with a relatively good performance; they are good in the mid and high flow parts particularly while the low flow part of FDCs might require further detailed analysis.

scholarly journals Seasonal streamflow forecasting by conditioning climatology with precipitation indices

2017 ◽  
Vol 21 (3) ◽  
pp. 1573-1591 ◽  
Author(s):  
Louise Crochemore ◽  
Maria-Helena Ramos ◽  
Florian Pappenberger ◽  
Charles Perrin
Keyword(s):  
Long Range ◽  
Precipitation Forecast ◽  
Low Flow ◽  
Drought Risk ◽  
Streamflow Forecasting ◽  
Streamflow Forecasts ◽  
Precipitation Indices ◽  
Seasonal Streamflow ◽  
Prediction Approach ◽  
The Impact

Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

scholarly journals Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 664
Author(s):  
Nikos Kanakaris ◽  
Nikolaos Giarelis ◽  
Ilias Siachos ◽  
Nikos Karacapilidis
Keyword(s):  
Language Processing ◽  
Scientific Knowledge ◽  
Link Prediction ◽  
Performance Metrics ◽  
Future Research ◽  
Knowledge Graph ◽  
Prediction Problem ◽  
Textual Information ◽  
Research Collaborations ◽  
Processing Techniques

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

scholarly journals Opportunistic Large Array Propagation Models: A Comprehensive Survey

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4206
Author(s):  
Farhan Nawaz ◽  
Hemant Kumar ◽  
Syed Ali Hassan ◽  
Haejoon Jung
Keyword(s):  
Energy Efficient ◽  
Large Scale ◽  
Performance Metrics ◽  
Experimental Studies ◽  
Cooperative Transmission ◽  
Analytical Models ◽  
Future Research ◽  
Large Array ◽  
Propagation Models ◽  
Comprehensive Survey

Enabled by the fifth-generation (5G) and beyond 5G communications, large-scale deployments of Internet-of-Things (IoT) networks are expected in various application fields to handle massive machine-type communication (mMTC) services. Device-to-device (D2D) communications can be an effective solution in massive IoT networks to overcome the inherent hardware limitations of small devices. In such D2D scenarios, given that a receiver can benefit from the signal-to-noise-ratio (SNR) advantage through diversity and array gains, cooperative transmission (CT) can be employed, so that multiple IoT nodes can create a virtual antenna array. In particular, Opportunistic Large Array (OLA), which is one type of CT technique, is known to provide fast, energy-efficient, and reliable broadcasting and unicasting without prior coordination, which can be exploited in future mMTC applications. However, OLA-based protocol design and operation are subject to network models to characterize the propagation behavior and evaluate the performance. Further, it has been shown through some experimental studies that the most widely-used model in prior studies on OLA is not accurate for networks with networks with low node density. Therefore, stochastic models using quasi-stationary Markov chain are introduced, which are more complex but more exact to estimate the key performance metrics of the OLA transmissions in practice. Considering the fact that such propagation models should be selected carefully depending on system parameters such as network topology and channel environments, we provide a comprehensive survey on the analytical models and framework of the OLA propagation in the literature, which is not available in the existing survey papers on OLA protocols. In addition, we introduce energy-efficient OLA techniques, which are of paramount importance in energy-limited IoT networks. Furthermore, we discuss future research directions to combine OLA with emerging technologies.

scholarly journals Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection

2017 ◽  
Vol 20 (2) ◽  
pp. 520-532 ◽  
Author(s):  
A. B. Dariane ◽  
Sh. Azimi
Keyword(s):  
Wavelet Transform ◽  
Multistep Method ◽  
Test Period ◽  
Fast Method ◽  
Streamflow Forecasting ◽  
Input Selection ◽  
Fuzzy Models ◽  
Monthly Streamflow ◽  
Input Variables ◽  
Learning Machine

Abstract In this paper the performance of extreme learning machine (ELM) training method of radial basis function artificial neural network (RBF-ANN) is evaluated using monthly hydrological data from Ajichai Basin. ELM is a newly introduced fast method and here we show a novel application of this method in monthly streamflow forecasting. ELM may not work well for a large number of input variables. Therefore, an input selection is applied to overcome this problem. The Nash–Sutcliffe efficiency (NSE) of ANN trained by backpropagation (BP) and ELM algorithm using initial input selection was found to be 0.66 and 0.72, respectively, for the test period. However, when wavelet transform, and then genetic algorithm (GA)-based input selection are applied, the test NSE increase to 0.76 and 0.86, respectively, for ANN-BP and ANN-ELM. Similarly, using singular spectral analysis (SSA) instead, the coefficients are found to be 0.88 and 0.90, respectively, for the test period. These results show the importance of input selection and superiority of ELM and SSA over BP and wavelet transform. Finally, a proposed multistep method shows an outstanding NSE value of 0.97, which is near perfect and well above the performance of the previous methods.

scholarly journals Evaluation and Hydrologic Validation of Three Satellite-Based Precipitation Products in the Upper Catchment of the Red River Basin, China

2018 ◽  
Vol 10 (12) ◽  
pp. 1881 ◽  
Author(s):  
Yueyuan Zhang ◽  
Yungang Li ◽  
Xuan Ji ◽  
Xian Luo ◽  
Xue Li
Keyword(s):  
River Basin ◽  
Southwest China ◽  
Spatial Scales ◽  
Red River ◽  
Low Flow ◽  
Efficiency Coefficient ◽  
Monthly Streamflow ◽  
Red River Basin ◽  
Hydrological Applications ◽  
Trmm 3B42

Satellite-based precipitation products (SPPs) provide alternative precipitation estimates that are especially useful for sparsely gauged and ungauged basins. However, high climate variability and extreme topography pose a challenge. In such regions, rigorous validation is necessary when using SPPs for hydrological applications. We evaluated the accuracy of three recent SPPs over the upper catchment of the Red River Basin, which is a mountain gorge region of southwest China that experiences a subtropical monsoon climate. The SPPs included the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 product, the Climate Prediction Center (CPC) Morphing Algorithm (CMORPH), the Bias-corrected product (CMORPH_CRT), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Climate Data Record (PERSIANN_CDR) products. SPPs were compared with gauge rainfall from 1998 to 2010 at multiple temporal (daily, monthly) and spatial scales (grid, basin). The TRMM 3B42 product showed the best consistency with gauge observations, followed by CMORPH_CRT, and then PERSIANN_CDR. All three SPPs performed poorly when detecting the frequency of non-rain and light rain events (<1 mm); furthermore, they tended to overestimate moderate rainfall (1–25 mm) and underestimate heavy and hard rainfall (>25 mm). GR (Génie Rural) hydrological models were used to evaluate the utility of the three SPPs for daily and monthly streamflow simulation. Under Scenario I (gauge-calibrated parameters), CMORPH_CRT presented the best consistency with observed daily (Nash–Sutcliffe efficiency coefficient, or NSE = 0.73) and monthly (NSE = 0.82) streamflow. Under Scenario II (individual-calibrated parameters), SPP-driven simulations yielded satisfactory performances (NSE >0.63 for daily, NSE >0.79 for monthly); among them, TRMM 3B42 and CMORPH_CRT performed better than PERSIANN_CDR. SPP-forced simulations underestimated high flow (18.1–28.0%) and overestimated low flow (18.9–49.4%). TRMM 3B42 and CMORPH_CRT show potential for use in hydrological applications over poorly gauged and inaccessible transboundary river basins of Southwest China, particularly for monthly time intervals suitable for water resource management.

scholarly journals Development and Formative Evaluation of a Low-Fidelity Equine Castration Model for Veterinary Education

2021 ◽  
Vol 8 ◽  
Author(s):  
M. Katie Sheats ◽  
Megan J. Burke ◽  
James B. Robertson ◽  
Katherine E. Fiebrandt ◽  
Callie A. Fogle
Keyword(s):  
Student Performance ◽  
Student Attitudes ◽  
Formative Evaluation ◽  
Performance Metrics ◽  
Early Stage ◽  
Future Research ◽  
Student Survey ◽  
Significant Difference ◽  
Veterinary Faculty ◽  
Fidelity Model

Entrustable Professional Activities (EPAs) are units of activity that early-stage professionals perform in the workplace that necessitate simultaneous integration of multiple competencies. EPA #6 requires students to perform a common surgical procedure on a stable patient, including pre-operative and post-operative management. Castration is one of the most common surgeries performed by equine primary care practitioners and is considered an “entry-level competency” for veterinary graduates entering equine private practice, however, to our knowledge there are no equine castration models available for veterinary student education. Therefore, we developed an inexpensive, low-fidelity model of equine field castration and evaluated it using a mixed-methods approach. Two different groups of students, with or without model experience, completed surveys before and after live horse castration. Students who used the model also completed model specific surveys. Videos of the students completing the model were evaluated by at least two different equine veterinary faculty using a 15-point rubric, and inter-rater reliability of the rubric was determined. After completing the model, students reflected on strengths and weaknesses of their performance. From our student survey results, we determined that student attitudes toward the model were mostly positive. Interestingly, there were several student attitudes toward the model that became significantly more favorable after live horse castration. Prior to live horse castration, there was no significant difference in confidence in model vs. no-model groups. Following live horse castration, students who used the model had higher confidence in procedure preparation and hand-ties than students who did not use the model, but they had lower scores for confidence during patient recovery. When reflecting on model castration, students most commonly cited preparation and surgical description as strengths, and ligature placement and hand-ties as weaknesses. Experts provided several suggestions to improve the model, including incorporation of emasculators and the need for better model stabilization. Our findings suggest that both students and veterinary educators feel that this low-fidelity model has educational value. Rubric performance metrics were favorable, but additional steps are needed to improve grading consistency among educators. Future research will determine whether student performance on the model is predictive of competence score during live-horse castration.

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