1,720,976 research outputs found
Enhancing algal bloom forecasting: A novel framework for machine learning performance evaluation during periods of special temporal patterns
No full textThe evaluation of algal bloom forecasting models typically relies on error metrics that quantify the forecasting performance over the whole test set as a single number. Furthermore, the comparison with simple baseline methods is often omitted. To address this, we introduce a novel framework for Model performance Analysis and Visualization of time series forecasting (MAVts). MAVts incorporates novel algorithms for the automatic identification and visualization of time series periods of interest where the forecasting models are evaluated and compared with simple baseline methods. The application of MAVts on evaluating algal bloom forecasting models composed of sophisticated machine learning (ML) methods, reveals that in 85% of experiments a single error metric is not enough and only in 12.5% of experiments a ML model outperforms all baselines on all metrics and periods of interest. Thus, MAVts emerges as a valuable tool for analyzing and comparing ML models, advancing environmental management and protection
A novel objective function DYNO for automatic multivariable calibration of 3D lake models
No full textThis study introduced a novel Dynamically Normalized Objective Function (DYNO) for multivariable (i.e., temperature and velocity) model calibration problems. DYNO combines the error metrics of multiple variables into a single objective function by dynamically normalizing each variable's error terms using information available during the search. DYNO is proposed to dynamically adjust the weight of the error of each variable hence balancing the calibration to each variable during optimization search. DYNO is applied to calibrate a tropical hydrodynamic model where temperature and velocity observation data are used for model calibration simultaneously. We also investigated the efficiency of DYNO by comparing the calibration results obtained with DYNO with the results obtained through calibrating to temperature only and with the results obtained through calibrating to velocity only. The results indicate that DYNO can balance the calibration in terms of water temperature and velocity and that calibrating to only one variable (e.g., temperature or velocity) cannot guarantee the goodness-of-fit of another variable (e.g., velocity or temperature) in our case. Our study implies that in practical application, for an accurate spatially distributed hydrodynamic quantification, including direct velocity measurements is likely to be more effective than using only temperature measurements for calibrating a 3D hydrodynamic model. Our example problems were computed with a parallel optimization method PODS, but DYNO can also be easily used in serial applications. Copyright
Surrogate Global Optimization for Identifying Cost-Effective Green Infrastructure for Urban Flood Control With a Computationally Expensive Inundation Model
No full textOptimization algorithms and urban inundation models are powerful tools to identify cost-effective designs of urban green infrastructures such as low-impact developments (LIDs). Most previous LID design optimization studies are based on one-dimensional (1D) inundation models, which cannot provide spatial information of flooding. The LID design optimization on two-dimensional (2D) models or coupled 1D-2D models was rarely explored due to the expensive computing time. This work investigates the effectiveness of surrogate optimization methods for LID design, which have not been used for the LID design problems. We propose a general LID design optimization framework that searches the optimal LID configurations based on both the spatial flood damage under a series of probable flood events and the life cycle costs (LCCs) of LID. We demonstrate the framework using a case study for an urban catchment with 55 sub-catchments and 103 LID decision variables. We tested two different surrogate optimization methods designed for high-dimensional problems: DYnamic COordinate search using Response Surface models (DYCORS) and Trust Region Bayesian Optimization (TuRBO), and one popular non-surrogate method particle swarm (PSO). The result indicates that: (a) DYCORS is a promising method (significantly faster than TuRBO and PSO) for identifying the optimal LID design to minimize the flood damage cost and LID LCC; (b) Optimized LID design could reduce damage cost by as much as $12.14 million for the urban catchment after eliminating its own LCC compared with no LID implementation; (c) LID is effective in reducing the imperviousness of lands in urban areas
Improving the speed of global parallel optimization on PDE models with processor affinity scheduling
No full textParallel global optimization of expensive simulation models like nonlinear partial differential equations (PDEs) can speed up model calibration or project design decisions, but the impact of memory management on the efficiency of using parallel global optimization methods has not been previously studied. This paper quantifies cache memory limitations arising during parallel optimization of expensive PDE models. An efficient parallel optimization algorithm is applied to model calibration for two different, expensive real-world PDEs (i.e., hydrodynamic and water quality analysis for a 250-hectare lake). One of these two lake models takes 4.5 h per simulation in serial, but that PDE simulation time per simulation increases to 12 h with parallel optimization if default processor scheduling strategy is used on a modern nonuniform memory access multicore platform. We proposed a novel mixed affinity scheduling strategy for parallel simulation optimization that increases computational efficiency by as much as 20% over the default affinity strategy
Enhanced watershed model evaluation incorporating hydrologic signatures and consistency within efficient surrogate multi-objective optimization
No full textThis paper presents a new framework for calibrating computationally expensive watershed models with multi-objective optimization methods and hydrological consistency analysis. The analysis evaluates different algorithms' efficiencies for finding watershed model calibration solutions within a limited budget. Two surrogate multi-objective algorithms GOMORS and ParEGO are compared to five evolutionary algorithms without surrogates on two watershed models. We test the algorithms’ performance with two multi-objective formulations (i.e., threshold-based flow separation and decomposition of the Nash-Sutcliffe Efficiency (NSE)). Results indicate that the surrogate-based GOMORS is the most computationally efficient overall. We also propose a framework to select among the calibration solutions obtained from multi-objective optimization using different hydrologic signatures. GOMORS is assessed for its ability to identify hydrologically acceptable calibrations. The decomposition of NSE is the most effective calibration formulation in terms of hydraulic consistency analysis. In addition, hydrologic signatures could be used effectively to filter non-dominated solutions obtained from multi-objective optimization
A Framework to Calibrate Ecosystem Demography Models Within Earth System Models Using Parallel Surrogate Global Optimization
No full textThe climatic feedbacks from vegetation, particularly from tropical forests, can alter climate through land-atmospheric interactions. Expected shifts in species composition can alter these interactions with profound effects on climate and terrestrial ecosystem dynamics. Ecosystem demographic (ED) models can explicitly represent vegetation dynamics and are a key component of next-generation Earth System Models (ESMs). Although ED models exhibit greater fidelity and allow more direct comparisons with observations, their interacting parameters can be more difficult to calibrate due to the complex interactions among vegetation groups and physical processes. In addition, while representation of forest successional coexistence in ESMs is necessary to accurately capture forest-climate interactions, few models can simulate forest coexistence and few studies have calibrated coexisted forest species. Furthermore, although both vegetation characteristics and soil properties affect vegetation dynamics, few studies have paid attention to jointly calibrating parameters related to these two processes. In this study, we develop a computationally-efficient and physical model structure-based framework that uses a parallel surrogate global optimization algorithm to calibrate ED models. We calibrate two typically coexisted tropical tree species, early and late successional plants, in a state-of-the-art ED model that is capable of simulating successional diversity in forests. We concurrently calibrate vegetation and soil parameters and validate results against carbon, energy, and water cycle measurements collected in Barro Colorado Island, Panama. The framework can find optimal solutions within 4–12 iterations for 19-dimensional problems. The calibration for tropical forests has important implications for predicting land-atmospheric interactions and responses of tropical forests to environmental changes
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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