Global optimization methods for calibration and optimization of the hydrologic tank model's parameters

The tank model, a lumped conceptual hydrological model, is well known due to its simplicity of concept, simplicity in computation while achieving forecasting accuracy comparable with more sophisticated models. However, the calibration of the hydrologic tank model required much time and effort to obtain better results through trial and error method. With the development of artificial intelligence, three probabilistic Global Optimization methods namely Genetic Algorithm (GA), Shuffle Complex Evolution (SCE) and Particle Swarm Optimization (PSO) were adopted for model calibration. The objective of the study is to find the best type of Global Optimization Methods and the best configuration to calibrate tank model that will produce the best fit between the observed and simulated runoff. The selected study area is Bedup Basin, located at Samarahan Division, Sarawak. Input data used for model calibration is a single storm event. The optimal parameters obtained will then be validated with 11 other single storm events. The performance of the optimization techniques is measured using Coefficient of Correlation (R) and Nash-Sutcliffe coefficient (E 2 ). Results show that all three probabilitic GOMs are able to obtain optimal value for 10 parameters of tank model. However, the best GOMs for hourly runoff simulation is PSO. SCE appeard to be the second best performance GOMs and the least performed is GA technique

Conceptual Layout of Wing Structure using Topology Optimization for Morphing Micro Air Vehicles in a Perching Maneuver

A topology optimization model for conceptual wing structure layouts of morphing micro air vehicles (MAVs) has been developed and implemented in MATLAB. Specifically, a six degree-of-freedom finite element (FE) model with a general quadrilateral discretization scheme was created by superposition of a known simple linear plane membrane element and a Kirchhoff plate bending element derived herein. The purpose of the six degree-offreedom model was to accommodate in-plane and out-of-plane aerodynamic loading combinations. The FE model was validated and the MATLAB implementation was verified with classical beam and plate solutions. A compliance minimization optimization objective was then formulated with the Solid Isotropic Material with Penalization (SIMP) method, subject to the equilibrium constraint computed by the FE model, and solved with the Optimality Criteria (OC) method. With the topology optimization model in place, four aerodynamic loading scenarios were extracted from points along a feasible MAV perching flight trajectory and used to determine wing thickness distributions for given planform shapes. The results suggest conceptual structural layouts in morphing MAVs, but equally important, the simple MATLAB implementation of the model can be adapted for a variety of objective statements for MAV morphing wing design

Toward improved identifiability of hydrologic model parameters: The information content of experimental data

We have developed a sequential optimization methodology, entitled the parameter identification method based on the localization of information (PIMLI) that increases information retrieval from the data by inferring the location and type of measurements that are most informative for the model parameters. The PIMLI approach merges the strengths of the generalized sensitivity analysis (GSA) method [Spear and Hornberger, 1980], the Bayesian recursive estimation (BARE) algorithm [Thiemann et al., 2001], and the Metropolis algorithm [Metropolis et al., 1953]. Three case studies with increasing complexity are used to illustrate the usefulness and applicability of the PIMLI methodology. The first two case studies consider the identification of soil hydraulic parameters using soil water retention data and a transient multistep outflow experiment (MSO), whereas the third study involves the calibration of a conceptual rainfall-runoff model

Hierarchical Progressive Optimization for Aerodynamic/Stealth Conceptual Design Based on Generalized Parametric Modelling and Sensitivity Analysis

A hierarchical progressive optimization approach is proposed for multidisciplinary optimal design by integrating with generalized parametric modeling and sensitivity analysis. The framework includes the following: (1) to set up a generalized parametric model for the geometric parameters of flight vehicles with different levels, (2) to reduce the number of design parameters using sensitivity analysis method and (3) to use the gradual optimization design method to solve the problem of integrated aerodynamic-stealth optimization design. The results from the application on the configuration optimization of an aircraft demonstrate that the hierarchical progressive optimization increases the fitness of the optimization design by 51.1% and improves the conceptual design efficiency

On the global stability of departure time user equilibrium: A Lyapunov approach

In (Jin, 2018), a new day-to-day dynamical system was proposed for drivers' departure time choice at a single bottleneck. Based on three behavioral principles, the nonlocal departure and arrival times choice problems were converted to the local scheduling payoff choice problem, whose day-to-day dynamics are described by the Lighthill-Whitham-Richards (LWR) model on an imaginary road of increasing scheduling payoff. Thus the departure time user equilibrium (DTUE), the arrival time user equilibrium (ATUE), and the scheduling payoff user equilibrium (SPUE) are uniquely determined by the stationary state of the LWR model, which was shown to be locally, asymptotically stable with analysis of the discrete approximation of the LWR model and through a numerical example. In this study attempt to analytically prove the global stability of the SPUE, ATUE, and DTUE. We first generalize the conceptual models for arrival time and scheduling payoff choices developed in (Jin, 2018) for a single bottleneck with a generalized scheduling cost function, which includes the cost of the free-flow travel time. Then we present the LWR model for the day-to-day dynamics for the scheduling payoff choice as well as the SPUE. We further formulate a new optimization problem for the SPUE and demonstrate its equivalent to the optimization problem for the ATUE in (Iryo and Yoshii, 2007). Finally we show that the objective functions in the two optimization formulations are equal and can be used as the potential function for the LWR model and prove that the stationary state of the LWR model, and therefore, the SPUE, DTUE, and ATUE, are globally, asymptotically stable, by using Lyapunov's second method. Such a globally stable behavioral model can provide more efficient departure time and route choice guidance for human drivers and connected and autonomous vehicles in more complicated networks.Comment: 17 pages, 3 figure

Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training

BACKGROUND: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to neural network training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations. RESULTS: Our results indicate that PSO performance can be improved if meta-optimized parameter sets are applied. In addition, we could improve optimization speed and quality on the other PSO methods in the majority of our experiments. We applied the OPSO method to neural network training with the aim to build a quantitative model for predicting blood-brain barrier permeation of small organic molecules. On average, training time decreased by a factor of four and two in comparison to the other PSO methods, respectively. By applying the OPSO method, a prediction model showing good correlation with training-, test- and validation data was obtained. CONCLUSION: Optimizing the free parameters of the PSO method can result in performance gain. The OPSO approach yields parameter combinations improving overall optimization performance. Its conceptual simplicity makes implementing the method a straightforward task

Assessment Of Blackbox Optimization Methods For Efficient Calibration Of Computationally Intensive Hydrological Models

Many studies have shown the effectiveness of blackbox optimization algorithms for the calibration of lumped conceptual hydrological models. Among these algorithms, the « Shuffled Complex Evolution method developed at the University of Arizona » (SCE-UA) is a very popular one. However, when it comes to calibrating distributed and/or physically-based models, computational efficiency becomes an issue. A single simulation with this type of model may take several minutes and the optimization process may require more than thousands of simulations. To alleviate this problem, two recently developed optimization methods, « Dynamically Dimensioned Search » (DDS) and « Nonsmooth Optimization by Mesh Adaptive Direct search » (NOMAD), adapt their search strategy based on the available budget of simulations. This work aims to verify the computational efficiency of DDS and NOMAD for the calibration of the HYDROTEL model (distributed and physically-based). Two versions of the model are used, one with 10 parameters and one with 19 parameters, and they are both applied to two different watersheds located in the province of Quebec (Canada). A second, lumped conceptual, model (HSAMI) is also applied to both watersheds to examine the impact of model structure on the results. Each combination of model-watershed is calibrated with each one of the optimization algorithms: DDS and NOMAD. SCE-UA is also used as the benchmark for comparison. The objective function uses the Nash-Sutcliffe Efficiency criterion, and is computed between simulated and observed streamflows. For every combination of model-watershed-algorithm, calibrations are repeated 32 times and the mean results are shown. This research sheds a better light and understanding on the efficiency of the three optimization algorithms for computationally intensive calibration problems, and on the model-related characteristics of the optimization problem

Poisson noise reduction with non-local PCA

Photon-limited imaging arises when the number of photons collected by a sensor array is small relative to the number of detector elements. Photon limitations are an important concern for many applications such as spectral imaging, night vision, nuclear medicine, and astronomy. Typically a Poisson distribution is used to model these observations, and the inherent heteroscedasticity of the data combined with standard noise removal methods yields significant artifacts. This paper introduces a novel denoising algorithm for photon-limited images which combines elements of dictionary learning and sparse patch-based representations of images. The method employs both an adaptation of Principal Component Analysis (PCA) for Poisson noise and recently developed sparsity-regularized convex optimization algorithms for photon-limited images. A comprehensive empirical evaluation of the proposed method helps characterize the performance of this approach relative to other state-of-the-art denoising methods. The results reveal that, despite its conceptual simplicity, Poisson PCA-based denoising appears to be highly competitive in very low light regimes.Comment: erratum: Image man is wrongly name pepper in the journal versio

Development of the City Public Service Model on the Basis of Integrated Transport Flow Indicators

The problem of modeling public services based on architectural and planning decisions is considered, the role of traffic in the formation of a model of city services is analyzed. An integrated criterion for the quality of public services is proposed. A method has been developed for determining segmented public services taking into account the transport areas of the city, which will make it possible to evenly disperse public service centers. The basis is a socio-planning organization, as a material-spatial system containing anthropogenic and natural components – the territory and institutions where the functional processes that take place in the urban planning environment take place. The described model has certain versatility, and is simultaneously suitable for characterizing various categories of service institutions. Thus, the task of optimizing the quality of public services in the city is reduced to a mathematical model for which, by setting the basic design criteria, the optimal result can be obtained.On the basis of a questionnaire survey and analysis of statistical data, calculation of traffic intensity, the demand and supply of the level of public services фre studied. The structural elements of this model: population, territory, transport and service institutions, are in dialectical interaction, which is described by the mathematical model in this study. The model is based on the calculation of the minimum population in the service area, which allows to have i-th type establishments using the social potential method, as well as a graph-analytical method in determining the optimal location of service institutions in the city.As a result of the research, a conceptual model of public services for cities is built, a layout of supermarkets in the territory of Uzhhorod and distribution of service areas of these institutions is proposed. This optimization will ensure uniform domestic servicing of the territory, optimal performance indicators of service establishments and minimum average service radii of points

Modelling the effect of land use change on hydrological model parameters via linearized calibration method in the upstream of Huaihe River Basin, China

Conceptual rainfall–runoff models have become a basic tool for evaluating effects of land use/cover changes on the hydrologic processes in small-scale as well as large watersheds. The runoff-producing mechanism is influenced by land use/cover changes. In this study, we analysed the effect of land use change on hydrological model parameters by calibrating the model parameters of different time periods with different land use via a linearized calibration method. The parameter calibration of a conceptual model usually involves the construction of objective function and optimization methods for good performance of observed data. However, the objective function of the minimum-sum-squared error will introduce an unrelated optimum solution for the parameter calibration problem of a conceptual model, which belongs to a highly complex nonlinear system. Thus, a linearized parameter calibration method, which searches for the optimal value on a parameter surface, is presented, based on the analysis of the problems of the objective function of the minimum-sum-squared error. Firstly, an ideal model is shown that illustrates the efficiency and applicability of this method. Secondly, the novel method is demonstrated for solving the Xinanjiang daily model parameter calibration. Finally, 50 years of data are divided into 4 different periods for parameter comparison, through which the effects of land use/cover changes on runoff in Dapoling watershed are evaluated. The results show that the linearized parameter calibration method is convergent, reasonable and effective. For example, the model parameter of evapotranspiration coefficient KC varied considerably, from 0.658 to 0.922, in response to land use/cover change within the watershed.Keywords: land use/cover change; parameter calibration; linearized; upper Huaihe River Basi