Parallel Reservoirs Research Articles

Multi-Objective Optimal Operation Decision for Parallel Reservoirs Based on NSGA-II-TOPSIS-GCA Algorithm: A Case Study in the Upper Reach of Hanjiang River

The parallel reservoirs in the upper reach of the Hanjiang River are key projects for watershed management, development, and protection. The optimal operation of parallel reservoirs is a multiple-stage, multiple-objective, and multiple-decision attributes complex decision problem. Taking Jiaoyan–Shimen parallel reservoirs as an example, a method of multi-objective optimal operation decision of parallel reservoirs (MOODPR) was proposed. The multi-objective optimal operation model (MOOM) was constructed. The new algorithm coupling NSGA-II, TOPSIS, and GCA was used to solve the MOODPR problem. The method of MOODPR was formed by coupling problem identification, model construction, an optimization solution, and scheme evaluation. The results show that (1) combining the Euclidean distance with the grey correlation degree to construct a new hybrid closeness degree makes the multi-attribute decision making method more scientific and feasible. (2) The NSGA-II-TOPSIS-GCA algorithm is applied to obtain decision schemes, which provide decision support for management. (3) It can be seen from the Pareto chart that for the Jiaoyan–Shimen parallel reservoirs, the comprehensive water supply was negatively related to ecology. (4) The comprehensive water supply and ecological AAPFD value in the extraordinarily dry year was 4.212 × 108 m3 and 4.953. The number of maximum continuous water shortage periods was 4 and 6. The maximum ten-day water shortage was 4.46 × 107 m3 and 2.3 × 106 m3. The research results provide technical support and reference value to multi-objective optimal operation decisions for parallel reservoirs in the upper reach of the Hanjiang River.

Dynamic Control of Flood Limited Water Levels for Parallel Reservoirs by Considering Forecast Period Uncertainty

The objective of this study is to achieve the dynamic optimization of the flood limited water level (FLWL) in parallel reservoirs, using Luhun Reservoir and Guxian Reservoir as case studies. The innovation lies in establishing a dynamic control optimization model for the FLWL of parallel reservoirs, considering the uncertainty in the forecasting period of the flood forecast due to the varying locations of the rainstorm center from upstream to downstream. To commence, the Fisher optimal segmentation method is employed for flood season staging to determine the staged FLWL of each reservoir. Subsequently, considering the uncertainty in the foresight period, the upper range of the dynamic FLWL is determined through the improved pre-discharge capacity constraint method and Monte Carlo simulation. Finally, a multi-objective optimization model is established to determine the optimal dynamic FLWL control operation scheme for parallel reservoirs, utilizing the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This model takes into account both downstream flood control requirements and the water supply benefits of the parallel reservoirs. Through the optimization of the scheme, the water supply of the parallel reservoirs can be augmented by 15,347.6 m3 during the flood season. This optimization effectively achieves a harmonious balance between flood control and water supply, holding significant implications for mitigating drought risks amid changing conditions.

Derivation of Optimal Two Dimensional Rule Curve for Dualistic Reservoir Water-Supply System

In arid and semi-arid regions particularly vulnerable to climate change, optimizing the long-term operation of multi-purpose reservoirs is paramount. This study derived an optimum two-dimensional rule curve to jointly operate the parallel reservoirs of Mosul and Dukan, Northern Iraq. A hybridized optimization technique combining conventional dynamic programming with the shuffled complex evolution algorithm (SCE-UA) was developed to solve this problem. The results showed that the proportion of normal water supply areas increased from the beginning of the flood season (October) to its highest levels in April (58.77% of the total water supply area). The proportion decreased to its lowest in September (25.04% of the total water supply area). The newly derived 2D rule cure was compared with the current operation policy and was found to optimize the amount of water shortage by 21.1% during the operational period. It also reduced the shortage period and avoided catastrophic water shortages during droughts. In addition, the developed model optimized the amounts of water more than the joint water requirements, suffering from a significant deficit in meeting the demand during some months of the operational years. As a result, the storage in each reservoir was improved and thence can be adapted to face water shortages during future climate changes. This study proved the new hybridized model's applicability and can serve as a tool for sustainable water management. Doi: 10.28991/CEJ-2023-09-07-016 Full Text: PDF

Optical cascaded reservoir computing for realization of dual-channel high-speed OTDM chaotic secure communication via four optically pumped VCSEL.

In this work, we propose a chaotic secure communication system with optical time division multiplexing (OTDM), using two cascaded reservoir computing systems based on multi beams of chaotic polarization components emitted by four optically pumped VCSELs. Here, each level of reservoir layer includes four parallel reservoirs, and each parallel reservoir contains two sub-reservoirs. When the reservoirs in the first-level reservoir layer are well trained and the training errors are far less than 0.1, each group of chaotic masking signals can be effectively separated. When the reservoirs in the second reservoir layer are effectively trained and the training errors are far less than 0.1, the output for each reservoir can be well synchronized with the corresponding original delay chaotic carrier-wave. Here, the synchronization quality between them can be characterized by the correlation coefficients of more than 0.97 in different parameter spaces of the system. Under these high-quality synchronization conditions, we further discuss the performances of dual-channel OTDM with a rate of 4×60 Gb/s. By observing the eye diagram, bit error rate and time-waveform of each decoded message in detail, we find that there is a large eye-openings in the eye diagrams, low bit error rate and higher quality time-waveform for each decoded message. Except that the bit error rate of one decoded message is lower than 7 × 10-3 in different parameter spaces, and those of the other decoded messages are close to 0, indicating that high-quality data transmissions are expected to be realized in the system. The research results show that the multi-cascaded reservoir computing systems based on multiple optically pumped VCSELs provide an effective method for the realization of multi-channel OTDM chaotic secure communications with high-speed.

Storage allocation rules to meet shared water demands for optimal operation of reservoir water supply

AbstractThe priority principle of storage allocation rules of serial cascade reservoirs within an interbasin water supply can reduce water loss and reduce water supply times. Reasonable balancing curves for reservoirs in parallel are proposed, and the proportional distribution of water is determined to illustrate the optimal allocation rule for different reservoir scheduling periods. The mutation point and slope are used to describe the segmentation of reservoirs in parallel. Finally, the relative optimal water supply rule is obtained. Taking the reservoir groups in the lower reaches of the Luanhe River basin as the research object, the results show that the water supply obtained by the balancing curve is increased by approximately 4% compared with the compensation regulation, and wasted water is reduced by 10%. The results are close to the largest water supply, the annual average wasted water and the guarantee rate obtained by dynamic programming. The suggested method can achieve the operation goal effectively and improve the operation performance of the shared water demands for the reservoir water supply. This article highlights the superior results obtained compared with the current storage allocation rules to meet shared water demands.

Handwritten Digits Recognition Based on a Parallel Optoelectronic Time-Delay Reservoir Computing System

In this work, the performance of an optoelectronic time-delay reservoir computing system for performing a handwritten digit recognition task is numerically investigated, and a scheme to improve the recognition speed using multiple parallel reservoirs is proposed. By comparing four image injection methods based on a single time-delay reservoir, we find that when injecting the histograms of oriented gradient (HOG) features of the digit image, the accuracy rate (AR) is relatively high and is less affected by the offset phase. To improve the recognition speed, we construct a parallel time-delay reservoir system including multi-reservoirs, where each reservoir processes part of the HOG features of one image. Based on 6 parallel reservoirs with each reservoir possessing 100 virtual nodes, the AR can reach about 97.8%, and the reservoir processing speed can reach about 1 × 106 digits per second. Meanwhile, the parallel reservoir system shows strong robustness to the parameter mismatch between multi-reservoirs.

Flood Risk Quantification, Transmission, and Propagation Analysis for Flood Water Utilization of Parallel Reservoirs

Joint operation of the reservoir system can greatly improve flood water utilization and alleviate the contradiction between water supply and water demand. But yet, the flood risk brought by forecast uncertainties transfers between the reservoirs and propagates from upstream to downstream, which is crucial for flood water utilization but is rarely investigated. To address this issue, this study proposes a FLWL optimal control framework, which consists of three modules: the FLWL upper bound derivation module, the flood risk analysis module, and the optimal FLWL decision-making module. The first module is to derive the upper bound relationship of the flood limited water level (FLWL), i.e., the specific substitution relationship of storage, for the parallel reservoirs coupling the capacity-constrained pre-release method and the aggregation-decomposition method. In the second module, the method to calculate the flood risk of the reservoirs and downstream caused by rainfall and runoff forecast uncertainties based on the total probability formula is proposed. In the third module, the transmission and propagation of flood risks in the parallel reservoir system under different rainfall grades are quantified, based on which the optimal FLWLs are determined. The implication in the Qinghe-Chaihe parallel reservoir system located in Northeast China shows that FLWL in Qinghe Reservoir and Chaihe Reservoir can be raised to 129.5 m from 127 m and 108 m from 104 m, respectively. Controlling the FLWL at any point in the safety domain can improve both flood water utilization and flood control benefits. However, the flood risk nonlinearly transfers between the parallel reservoirs and nonlinearly propagates from the upstream to the downstream. Based on this relationship, the optimal FLWLs of the reservoirs under different decision preferences are recommended.

Four-channels optical chaos secure communications with the rate of 400 Gb/s using optical reservoir computing based on two quantum dot spin-VCSELs

In this work, we utilize four parallel reservoirs to model the chaotic dynamics of the output four polarization components (PCs) from a driving QD spin-VCSEL. Here, the four parallel reservoirs are implemented using the four PCs of a reservoir QD spin-VCSEL. High-quality chaos synchronizations of four pairs of PCs can be realized by using the four parallel reservoirs based on a reservoir QD spin-VCSEL. Under these high-quality synchronizations, we successfully implement four-channel secure communications with 4 × 100 Gb/s 16 QAM messages under guaranteeing. We further discuss the performances of the bit error ratios (BERs) for four decoding messages under different parameters. We show that all BERs via different parameters are less than 7 × 10−3, denoting that high-quality data-transmissions can be potentially obtained in the system. Moreover, we prove that our proposed multi-channel optical chaotic communication scheme has the same level of security as the traditional schemes. Our findings show that the delay-based optical reservoir computing based on a QD spin-VCSEL provides an effective method for realization of multi-channel optical secure communication.

Parallel and deep reservoir computing using semiconductor lasers with optical feedback

Abstract Photonic reservoir computing has been intensively investigated to solve machine learning tasks effectively. A simple learning procedure of output weights is used for reservoir computing. However, the lack of training of input-node and inter-node connection weights limits the performance of reservoir computing. The use of multiple reservoirs can be a solution to overcome this limitation of reservoir computing. In this study, we investigate parallel and deep configurations of delay-based all-optical reservoir computing using semiconductor lasers with optical feedback by combining multiple reservoirs to improve the performance of reservoir computing. Furthermore, we propose a hybrid configuration to maximize the benefits of parallel and deep reservoirs. We perform the chaotic time-series prediction task, nonlinear channel equalization task, and memory capacity measurement. Then, we compare the performance of single, parallel, deep, and hybrid reservoir configurations. We find that deep reservoirs are suitable for a chaotic time-series prediction task, whereas parallel reservoirs are suitable for a nonlinear channel equalization task. Hybrid reservoirs outperform other configurations for all three tasks. We further optimize the number of reservoirs for each reservoir configuration. Multiple reservoirs show great potential for the improvement of reservoir computing, which in turn can be applied for high-performance edge computing.

Accurate separation of mixed high-dimension optical-chaotic signals using optical reservoir computing based on optically pumped VCSELs.

In this work, with the mixing fractions being known in advance or unknown, the schemes and theories for the separations of two groups of the mixed optical chaotic signals are proposed in detail, using the VCSEL-based reservoir computing (RC) systems. Here, two groups of the mixed optical chaotic signals are linearly combined with many beams of the chaotic x-polarization components (X-PCs) and Y-PCs emitted by the optically pumped spin-VCSELs operation alone. Two parallel reservoirs are performed by using the chaotic X-PC and Y-PC output by the optically pumped spin-VCSEL with both optical feedback and optical injection. Moreover, we further demonstrate the separation performances of the mixed chaotic signal linearly combined with no more than three beams of the chaotic X-PC or Y-PC. We find that two groups of the mixed optical chaos signals can be effectively separated by using two reservoirs in single RC system based on optically pumped Spin-VCSEL and their corresponding separated errors characterized by the training errors are no more than 0.093, when the mixing fractions are known as a certain value in advance. If the mixing fractions are unknown, we utilize two cascaded RC systems based on optically pumped Spin-VCSELs to separate each group of the mixed optical signals. The mixing fractions can be accurate predicted by using two parallel reservoirs in the first RC system. Based on the values of the predictive mixing fractions, two groups of the mixed optical chaos signals can be effectively separated by utilizing two parallel reservoirs in the second RC system, and their separated errors also are no more than 0.093. In the same way, the mixed optical chaos signal linearly superimposed with more than three beams of optical chaotic signals can be effectively separated. The method and idea for separation of complex optical chaos signals proposed by this paper may provide an impact to development of novel principles of multiple access and demultiplexing in multi-channel chaotic cryptography communication.

General Unit Hydrograph from Chow’s Linear Theory of Hydrologic Systems and Its Applications

This research solves Chow’s linear hydrologic systems equations thoroughly to result in a theoretical instantaneous unit hydrograph (UH), which is a superposition of many (M) negative exponential functions. This implies that the instantaneous UH can be imagined as a superposition of many linear reservoirs in parallel. Mathematically, at M→∞, the theoretical UH (in terms of Taylor series) converges to the writer’s general UH that is a simple analytic expression derived inductively from empiricism. Therefore, this research turns the recent conceptual general UH to a theoretical law that approximates real-world watershed processes as a time-invariant linear hydrologic system. Specifically, we first review Chow’s linear hydrologic systems model and apply it to a conceptual watershed with an instantaneous storm, which results in a theoretical instantaneous UH and an S-hydrograph in the superposition of many negative exponential functions. The resulting S-hydrograph then is shown mathematically to be identical to the writer’s general UH at M→∞. Finally, the general theoretical UH is applied to 10 real-world watersheds for 19 rainfall-runoff simulations. It is noteworthy that the proposed method has two advantages: (1) it is general for storms with different rainfall durations, and (2) it does not require to define excess rainfall and direct runoff in advance because rainfall losses and baseflow can be a part of the solution process. It is expected that this research provides a deeper understanding of the general UH and thus helps promote its applications in practice.

Deep optical reservoir computing and chaotic synchronization predictions based on the cascade coupled optically pumped spin-VCSELs.

In this work, we utilize two cascade coupling modes (unidirectional coupling and bidirectional coupling) to construct a four-layer deep reservoir computing (RC) system based on the cascade coupled optically-pumped spin-VCSEL. In such a system, there are double sub-reservoirs in each layer, which are formed by the chaotic x-PC and y-PC emitted by the reservoir spin-VCSEL in each layer. Under these two coupling modes, the chaotic x-PC and y-PC emitted by the driving optically-pumped spin-VCSEL (D-Spin-VCSEL), as two learning targets, are predicted by utilizing the four-layer reservoirs. In different parameter spaces, it is further explored that the outputs of the double sub-reservoirs in each layer are respectively synchronized with the chaotic x-PC and y-PC emitted by the D-Spin-VCSEL. The memory capacities (MCs) for the double sub-reservoirs in each layer are even further investigated. The results show that under two coupling modes, the predictions of the double sub-reservoirs with higher-layer for these two targets have smaller errors, denoting that the higher-layer double sub-reservoirs possess better predictive learning ability. Under the same system parameters, the outputs of the higher-layer dual parallel reservoirs are better synchronized with two chaotic PCs emitted by the D-Spin-VCSEL, respectively. The larger MCs can also be obtained by the higher-layer double reservoirs. In particular, compared with the four-layer reservoir computing system under unidirectional coupling, the four-layer reservoir computing system under bidirectional coupling shows better predictive ability in the same parameter space. The chaotic synchronizations predicted by each layer double sub-reservoirs under bidirectional coupling can be obtained higher qualities than those under unidirectional coupling. By the optimization of the system parameters, the outputs of the fourth-layer double sub-reservoirs are almost completely synchronized with the chaotic x-PC and y-PC emitted by the D-Spin-VCSEL, respectively, due to their correlation coefficient used to measure synchronization quality can be obtained as 0.99. These results have potential applications in chaotic computation, chaotic secure communication and accurate prediction of time series.

Reservoir end wall effects on bivariate ion and fluid transport in micro/nano-nozzles for effective electroosmotic mixing

The aim of this study was to develop an efficient convection diffusion-based mathematical model for the species transport and mixing in different shaped (i.e., nozzle, diffuser, diffuser–nozzle, nozzle–diffuser) micro/nano-channels connected to large reservoirs. Both analytical and numerical studies are performed to illustrate the impact of inertial and contact angles for the generation of complex flow patterns due to different aspect ratio specified transformations. The hydrodynamics of the ion and fluid transport are analyzed through the Poisson–Nernst–Plank-based Navier–Stokes model subjected to specified system of forces endured by the reservoir fluids. The numerical results for pressure velocity correlations are obtained when the transport mechanism of the domain is changed from nozzle to diffuser. Mixing efficiency is evaluated for different geometric configurations and compared with a rectangular slit channel when the parallel reservoirs are connected. The role of Debye–Hückel parameter, conical angles or slope, and reservoir height/width on the transport of ions and enhancement of mixing are discussed. The mixing efficiency is found to attain a higher value after considering the reservoir connected to a nozzle without involving any hurdles or heterogeneous zeta potential along the channel wall. Closed-form analytical solutions of the electric potential are obtained through the linearized Poisson–Boltzmann model and further incorporated for the pressure evaluation. The axial and transverse velocities are evaluated from the modified Navier–Stokes equation including electric body force term and are validated with the experimental results. Effective nonlinear coupling responses of ion transport are found to be more pronounced in nozzle compared with diffuser resulting a higher mixing. Also, the solutions of velocity resulting in a low torque satisfy the equilibrium conditions and are optimized in terms of adversion of frictional factor and viscous dissipation resulting in an effective mixing. The findings manifest the species patterns with high accuracy and versatility, which could possibly help to handle the technical challenges associated with the design of pumpless actuated microfluidic devices.

A Meshless Numerical Modeling Method for Fractured Reservoirs Based on Extended Finite Volume Method

Summary In this paper, a meshless numerical modeling method named mesh-free discrete fracture model (MFDFM) of fractured reservoirs based on the newly developed extended finite volume method (EFVM) is proposed. First, matching and nonmatching point cloud generation algorithms are developed to discretize the reservoir domain with fracture networks, which avoid the gridding challenges of the reservoir domain in traditional mesh-based methods. Then, taking oil/water two-phase flow in fractured reservoirs as an example, MFDFM derives the EFVM discrete scheme of the governing equations, constructs various types of connections between matrix nodes and fracture nodes, and calculates the corresponding transmissibilities. Finally, the EFVM discrete scheme of the governing equations and the generalized finite difference discrete scheme of various boundary conditions form the global nonlinear equations, which do not increase the degree of nonlinearity compared with those in the traditional finite volume method (FVM)-based numerical simulator. The global equations can be solved by the existing nonlinear solver in the FVM-based reservoir numerical simulator by only adding the linear discrete equations of boundary conditions, which reduce the difficulty of forming a general purpose MFDFM-based fractured reservoir numerical simulator. Several numerical test cases are implemented to illustrate that the proposed MFDFM can achieve good computational performance under matching and nonmatching point clouds, and for heterogeneous reservoirs, complex fracture networks, complex boundary geometry, and complex boundary conditions, by comparing the computational results of MFDFM with embedded discrete fracture model (EDFM). Thus, MFDFM retains the computational performances of the traditional mesh-based methods and can avoid the difficulties of handling complex geometry and complex boundary conditions of the computational domain, which is the first meshless numerical framework to model fractured reservoirs in parallel with the mesh-based discrete fracture model (DFM) and EDFM.

Bidirectional parallel echo state network for speech emotion recognition.

Speech is an effective way for communicating and exchanging complex information between humans. Speech signal has involved a great attention in human-computer interaction. Therefore, emotion recognition from speech has become a hot research topic in the field of interacting machines with humans. In this paper, we proposed a novel speech emotion recognition system by adopting multivariate time series handcrafted feature representation from speech signals. Bidirectional echo state network with two parallel reservoir layers has been applied to capture additional independent information. The parallel reservoirs produce multiple representations for each direction from the bidirectional data with two stages of concatenation. The sparse random projection approach has been adopted to reduce the high-dimensional sparse output for each direction separately from both reservoirs. Random over-sampling and random under-sampling methods are used to overcome the imbalanced nature of the used speech emotion datasets. The performance of the proposed parallel ESN model is evaluated from the speaker-independent experiments on EMO-DB, SAVEE, RAVDESS, and FAU Aibo datasets. The results show that the proposed SER model is superior to the single reservoir and the state-of-the-art studies.

Reservoir Operation Sequence- and Equity Principle-Based Multi-Objective Ecological Operation of Reservoir Group: A Case Study in a Basin of Northeast China

The sequence of reservoir operations has a profound influence on the regulation and storage capacity of reservoir groups to effectively utilise the natural water inflow and external water transfer in the basin, especially for reservoir groups with water supply tasks. This study establishes the reservoir operation sequence (ROS) of four reservoir group modes, aiming at national economic and ecological water consumption, constructs a model of ROS-based multi-objective ecological operation of the reservoir group, and uses the particle swarm optimisation (PSO) method to optimise the solution. Analysing the results of the three schemes in two scenarios at the Yinma River Basin (YRB) indicates that after the Central Jilin Water Supply Project is put into operation, not only will the production and living water be effectively improved, but also the ecological water in the basin. Then, we compared the optimisation results of different water supply sequences in series and parallel reservoirs, which illustrates that the ROS of the four modes formulated in this research is the optimal water supply sequence.

The stochastic decision making framework for long-term multi-objective energy-water supply-ecology operation in parallel reservoirs system under uncertainties

The long-term multi-objective reservoir operation (LTMORO) involving multiple competitive goals such as energy, water supply and ecology protection is usually implemented under uncertain and changeable environment. Conventional methods usually obtain trade-off solutions and select most preferred one by multi-criteria decision making (MCDM) under deterministic environment. However, these methods are difficult to handle real-world reservoir system where multiple uncertainties exist in criteria performance values (PVs) and weights (CWs). In this paper, framework for solving reservoir operation and MCDM problem under various uncertainties is developed. A novel multi-objective method based on shuffled frog leaping algorithm (SFLA) is proposed to obtain high-quality trade-off solutions. Besides, hybrid utility forms based on TOPSIS, grey correlation analysis (GCA) or other models are impacted by subjectivity of combination coefficient, resulting in extra uncertainties for MCDM. To this end, the stochastic multi-criteria acceptability analysis (SMAA) model is developed by constructing new utility function based on SMAA-2 and modified GCA. The modified GCA helps to offset the uneven distribution problem in conventional version and enhance differentiation of utility function in SMAA-2. Moreover, deterministic CWs are obtained based on minimum deviation principle and two types of stochastic CWs following probability distributions are used to estimate CWs uncertainty. The risk caused by uncertain information propagating from PVs and CWs to decision results is also quantified. Finally, efficiency of established framework is tested by conducting three numerical experiments compared with deterministic GCA and SMAA-2. Results indicate that, compared with deterministic models, the novel framework provides decision maker with more reliable decision support and quantified risk information. The novel SMAA-GCA model produces relatively high probabilities for solutions to obtain their respective ranks compared with that of SMAA-2. It is effective to reduce impact of complex uncertainties on MCDM for LTMORO under stochastic environment.

Experimental Investigation of an Optical Reservoir Computing System Based on Two Parallel Time-Delay Reservoirs

We experimentally investigate the performances of an optical reservoir computing (RC) system based on two parallel time-delay reservoirs composed of two semiconductor lasers (SLs) subject to optical feedback. In such a system, the information being processed is split into two parts to send into two reservoirs through directly modulating the pump currents of two SLs, and the temporal output of the two SLs are sampled and taken as the virtual node states for training and testing. Via Santa Fe time series prediction task and multi-waveform recognition task, the performances of the proposed RC system are investigated and compared with those of the system based on one reservoir. The results show that the system based on two parallel reservoirs behaves better performance and stronger parameter robustness than that based on one reservoir. Moreover, through analyzing the dependence of the system performances on the number of virtual node states actually used for readout, the potential data processing rate (DPR) of the system is evaluated. For processing a prediction task under guaranteeing the normalized mean square error below 0.1 and a recognition task under guaranteeing the signal error rate below 0.005, the potential DPR of the proposed RC system can achieve 200 MSa/s, which is twice the DPR of the system with only one reservoir.

Comparative analysis of routed flood frequency for reservoirs in parallel incorporating bivariate flood frequency and reservoir operation

AbstractFlood frequency is commonly defined by natural flood characteristics such as flood peak, volume and duration. However, for floods at reservoir site, reservoir operation will significantly alter the natural flood process and flood frequency. This article presents a framework of routed flood frequency which incorporates multi‐reservoir flood correlation and considers the effect of reservoir operation. Aggregated flood volume during reservoir operation has been adopted as indicator for the routed flood frequency curve (RFFC). A comparison has been made with a 62‐year series of historic flood inflows observed at two parallel reservoirs in Eastern China. By comparing the derived RFFC with traditional univariate and bivariate flood frequency results, it can be seen that the RFFC results provide a reasonable estimation of flood frequency. A sensitivity analysis indicated that the reservoir maximum release allocation has significant impact on RFFC, and a balanced allocation of flood peak can greatly decrease the overtopping risk. The “peak–peak” contour lines of the RFFC show that there are different flood combination zones which need different operation strategies in real‐time operation. Our findings improve the understanding of flood frequency concepts when considering the impact of reservoir operation and provide reference to manage real‐time reservoir operation.

EEG denoising through a wide and deep echo state network optimized by UPSO algorithm

In the complex environment of telemedicine, Electroencephalogram (EEG) signals are easily overwhelmed by noise, which affects the intelligent diagnosis of diseases. Since the time-frequency domain characteristics of some noise in EEG signals are complex and the distribution is unknown, and the spectrum of some noise overlaps with the original EEG signal spectrum, it is difficult to filter those noise by traditional methods. To tackle this problem, and considering the large data characteristics of EEG signals in the context of telemedicine, a wide-deep echo state networks (WDESN) with multiple reservoirs in parallel and stacked configuration is proposed for multivariate time series denoising. Firstly, stacking and paralleling multiple reservoirs, the deep features of signals can be extracted to complete the task of reconstructing signal from the noisy signal. Then, the Uniform Search Particle Swarm Optimization (UPSO) is used to optimize reservoir parameters of WDESN. Finally, the effectiveness of UPSO-WDESN is verified through experiments. Experiment results show that compared with existing models, the proposed UPSO-WDESN model can achieve better noise removal performance, while keeping more nonlinear feature of signals.