Characterization of phenanthrenequinone-doped poly(methyl methacrylate) for holographic memory

The holographic recording characteristics of phenanthrenequinone- (PQ-) doped poly(methyl methacrylate) are investigated. The exposure sensitivity is characterized for single-hologram recording, and the M/# is measured for samples as thick as 3 mm. Optically induced birefringence is observed in this material

Development Of A Cloud Computing Application For Water Resources Modelling And Optimization Based On Open Source Software

Cloud computing is the latest advancement in Information and Communication Technology (ICT) that provides computing as a service or delivers computation, software, data access, storage service without end-user knowledge of the physical location and system configuration. Cloud computing, service oriented architecture and web geographic information systems are new technologies for development of the cloud computing application for water resources modelling and optimization. The cloud application is deployed and tested in a distributed computer environment running on three virtual machines (VMs). The cloud application has five web services for: (1) spatial data infrastructure – 1 (SDI), (2) SDI – 2, (3) support for water resources modelling (4) water resources optimization and 5) user authentication. The cloud application is developed using several programming languages (PHP, Ajax, Java, and JavaScript), libraries (OpenLayers and JQuery) and open-source software components (GeoServer, PostgreSQL and PostGIS) and OGC standards (WMS, WFS and WFT-T). The web services for support of water resources modelling and user authentication are deployed on Amazon Web Services and are communicating using WFS with the two SDI web services. The two SDI web services are working on the two separate VMs providing geospatial data and services. The fourth web service is deployed on a separate VM because of the expected large computational requirements. The cloud application is scalable, interoperable, creates a real time multi-user collaboration platform. All code and components used are open source. The cloud application was tested with concurrent multiple users. The performance, security and utilization of the distributed computer environment are monitored and analysed together with the users’ experience and satisfaction. The applicability of the presented solution and its future are elaborated

Locating Multiple Leaks in Water Distribution Networks Combining Physically Based and Data-Driven Models and High-Performance Computing

Water utilities are urged to decrease their real water losses, not only to reduce costs but also to assure long-term sustainability. Hardware- and software-based techniques have been broadly used to locate leaks; within the latter, previous works that have used data-driven models mostly focused on single leaks. This paper presents a methodology to locate multiple leaks in water distribution networks employing pressure residuals. It consists of two phases: one is to produce training data for the data-driven model and cluster the nodes based on their leak-flow-rate-independent signatures using an adapted hierarchical agglomerative algorithm; the second is to locate the leaks using a top-down approach. To identify the leaking clusters and nodes, we employed a custom-built k-nearest neighbor (k-NN) algorithm that compares the test instances with the generated training data. This instance-to-instance comparison requires substantial computational resources for classification, which was overcome by the use of high-performance computing. The methodology was applied to a real network located in a European town, comprising 144 nodes and a total length of pipes of 24 km. Although its multiple inlets add redundancy to the network increasing the challenge of leak location, the method proved to obtain acceptable results to guide the field pinpointing activities. Nearly 70% of the areas determined by the clusters were identified with an accuracy of over 90% for leak flows above 3.0 L/s, and the leaking nodes were accurately detected over 50% of the time for leak flows above 4.0 L/

A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA

The aim of this paper is to improve semiseasonal forecast of groundwater availability in response to climate variables, surface water availability, groundwater level variations, and human water management using a two‐step data‐driven modeling approach. First, we implement an ensemble of artificial neural networks (ANNs) for the 300 wells across the High Plains aquifer (USA). The modeling framework includes a method to choose the most relevant input variables and time lags; an assessment of the effect of exogenous variables on the predictive capabilities of models; and the estimation of the forecast skill based on the Nash‐Sutcliffe efficiency (NSE) index, the normalized root mean square error, and the coefficient of determination (R2). Then, for the ANNs with low‐ accuracy, a MultiModel Combination (MuMoC) based on a hybrid of ANN and an instance‐based learning method is applied. MuMoC uses forecasts from neighboring wells to improve the accuracy of ANNs. An exhaustive‐search optimization algorithm is employed to select the best neighboring wells based on the cross correlation and predictive accuracy criteria. The results show high average ANN forecasting skills across the aquifer (average NSE \u3e 0.9). Spatially distributed metrics of performance showed also higher error in areas of strong interaction between hydrometeorological forcings, irrigation intensity, and the aquifer. In those areas, the integration of the spatial information into MuMoC leads to an improvement of the model accuracy (NSE increased by 0.12), with peaks higher than 0.3 when the optimization objectives for selecting the neighbors were maximized.t

Identifying major drivers of daily streamflow from large-scale atmospheric circulation with machine learning

Previous studies linking large-scale atmospheric circulation and river flow with traditional machine learning techniques have predominantly explored monthly, seasonal or annual streamflow modelling for applications in direct downscaling or hydrological climate-impact studies. This paper identifies major drivers of daily streamflow from large-scale atmospheric circulation using two reanalysis datasets for six catchments in Norway representing various Köppen-Geiger climate types and flood-generating processes. A nested loop of roughly pruned random forests is used for feature extraction, demonstrating the potential for automated retrieval of physically consistent and interpretable input variables. Random forest (RF), support vector machine (SVM) for regression and multilayer perceptron (MLP) neural networks are compared to multiple-linear regression to assess the role of model complexity in utilizing the identified major drivers to reconstruct streamflow. The machine learning models were trained on 31 years of aggregated atmospheric data with distinct moving windows for each catchment, reflecting catchment-specific forcing-response relationships between the atmosphere and the rivers. The results show that accuracy improves to some extent with model complexity. In all but the smallest, rainfall-driven catchment, the most complex model, MLP, gives a Nash-Sutcliffe Efficiency (NSE) ranging from 0.71 to 0.81 on testing data spanning five years. The poorer performance by all models in the smallest catchment is discussed in relation to catchment characteristics, sub-grid topography and local variability. The intra-model differences are also viewed in relation to the consistency between the automatically retrieved feature selections from the two reanalysis datasets. This study provides a benchmark for future development of deep learning models for direct downscaling from large-scale atmospheric variables to daily streamflow in Norway.publishedVersio

Meteorological Drought Forecasting Based on Climate Signals Using Artificial Neural Network – A Case Study in Khanhhoa Province Vietnam

AbstractIn Khanhhoa Province (Vietnam) long-lasting droughts often occur, causing negative consequences for this region, so accurate drought forecasting is of paramount importance. Normally, drought index forecasting model uses previously lagged observations of the index itself and rainfall as input variables. Recently, climate signals are being also used as potential predictors. In this study, we use 3-month, 6-month, and 12-month of Standardized Precipitation Evapotranspiration Index (SPEI), with a calculation time during the period from 1977 to 2014. This paper aims at examining the lagged climate signals to predict SPEI at Khanhhoa province, using artificial neural network. Climate signals indices from Indian Ocean and Pacific Ocean surrounding study area were analysed to select five predictors for the model. These were combined with local variables (lagged SPEI and rainfall) and used as input variables in 16 different models for different forecast horizons. The results show that adding climate signals can achieve better prediction. Climate signals can be also used solely as predictors without using local variables – in this case they explain the variation SPEI (longer horizons, e.g.12-month) reaching 61 – 80%. The developed model can benefit developing long-term policies for reservoir and irrigation regulation and plant alternation schemes in the context of drought hazard

A comprehensive review on the design and optimization of surface water quality monitoring networks

This is the final version. Available from Elsevier via the DOI in this record. The surface water quality monitoring network (WQMN) is crucial for effective water environment management. How to design an optimal monitoring network is an important scientific and engineering problem that presents a special challenge in the smart city era. This comprehensive review provides a timely and systematic overview and analysis on quantitative design approaches. Bibliometric analysis shows the chronological pattern, journal distribution, authorship, citation and country pattern. Administration types of water bodies and design methods are classified. The flexibility characteristics of four types of direct design methods and optimization objectives are systematically summarized, and conclusions are drawn from experiences with WQMN parameters, station locations, and sampling frequency and water quality indicators. This paper concludes by identifying four main future directions that should be pursued by the research community. This review sheds light on how to better design and construct WQMNs.Key-Area Research and Development Program of Guangdong ProvinceNational Natural Science Foundation of ChinaInnovation Project of Universities in Guangdong Province-Natural Scienc

Multiobjective direct policy search using physically based operating rules in multireservoir systems

supplemental_data_wr.1943-5452.0001159_ritter.pdf (492 KB)This study explores the ways to introduce physical interpretability into the process of optimizing operating rules for multireservoir systems with multiple objectives. Prior studies applied the concept of direct policy search (DPS), in which the release policy is expressed as a set of parameterized functions (e.g., neural networks) that are optimized by simulating the performance of different parameter value combinations over a testing period. The problem with this approach is that the operators generally avoid adopting such artificial black-box functions for the direct real-time control of their systems, preferring simpler tools with a clear connection to the system physics. This study addresses this mismatch by replacing the black-box functions in DPS with physically based parameterized operating rules, for example by directly using target levels in dams as decision variables. This leads to results that are physically interpretable and may be more acceptable to operators. The methodology proposed in this work is applied to a network of five reservoirs and four power plants in the Nechi catchment in Colombia, with four interests involved: average energy generation, firm energy generation, flood hazard, and flow regime alteration. The release policy is expressed depending on only 12 parameters, which significantly reduces the computational complexity compared to existing approaches of multiobjective DPS. The resulting four-dimensional Pareto-approximate set offers a variety of operational strategies from which operators may choose one that corresponds best to their preferences. For demonstration purposes, one particular optimized policy is selected and its parameter values are analyzed to illustrate how the physically based operating rules can be directly interpreted by the operators.Peer ReviewedPreprin

On-line hydraulic state prediction for water distribution systems

World Environmental and Water Resources Congress 2009: Great Rivers Proceedings of World Environmental and Water Resources Congress 2009 May 17–21, 2009 Kansas City, MissouriThis paper describes and demonstrates a method for on‐line hydraulic state prediction in urban water networks. The proposed method uses a Predictor‐Corrector (PC) approach in which a statistical data‐driven algorithm is applied to estimate future water demands, while near real‐time field measurements are used to correct (i.e., calibrate) these predicted values on‐line. The calibration problem is solved using a modified Least Squares (LS) fit method. The objective function is the minimization of the least‐squares of the differences between predicted and measured hydraulic parameters (i.e., pressure and flow rates at several system locations), with the decision variables being the consumers' water demands. The a‐priori estimation (i.e., prediction) of the values of the decision variables, which improves through experience, facilitates a better convergence of the calibration model and provides adequate information on the system's hydraulic state for real time optimization. The proposed methodology is demonstrated on a prototypical municipal water distribution system

Committees Of Specialized Conceptual Hydrological Models: Comparative Study

Committee modelling approach is skillful prediction in the domain of hydrological modelling that allows explicitly to derive predictive model outputs. In this approach, the different individual models are optimally combined. Generally if a single hydrological model or the model calibrated by the single aggregated objective function it is hard to capture all facets of a complex process and to present the best possible model outputs. This model could be either capable for high flows or for low flows or not for both cases hence more flexible modelling architectures are required. Here the possibilities is building several specialized models each of which is responsible for a particular sub-process (high flows or low flows), and combining them using dynamic weights – thus forming a committee model. In this study we compare two different types of committee models: (i) the combine model based on fuzzy memberships function (Kayastha et al. 2013, Fenicia et al. 2007) and (ii) the combine model based on weights that calculated from hydrological states (Oudin et al. 2006). Before combining the models the individual hydrological models are calibrated by Adaptive Cluster Covering Algorithm (Solomatine 1999) for high and low flows with (different) suitable objective functions. The committee model based on fuzzy memberships does not generate additional water in the system (preserves water balance), however there is no guarantee for this in case of committees based on hydrological states. The relative performances of the two different committee models and their characteristics are illustrated, with an application to HBV hydrological models in Bagmati catchment in Nepal