scholarly journals Multi-criteria calibration of a conceptual runoff model using a genetic algorithm

2000 ◽  
Vol 4 (2) ◽  
pp. 215-224 ◽  
Author(s):  
J. Seibert
Keyword(s):  
Genetic Algorithm ◽  
Synthetic Data ◽  
Test Case ◽  
True Parameter ◽  
Groundwater Levels ◽  
Optimisation Algorithm ◽  
Level Data ◽  
Parameter Values ◽  
Hbv Model ◽  
Runoff Model

Abstract. Abstract: Calibration of a model against more than one output variable is important for reliable simulations of internal processes. In this study, a genetic algorithm combined with local optimisation was proposed for automatic single- and multi-criteria calibration of the HBV model, a conceptual runoff model. The model and the optimisation algorithm were applied in two catchments with different geology where, in addition to observed runoff, time series of groundwater level data were available. For a theoretical, error-free test case with synthetic data, the optimisation algorithm was usually able to find the true parameter values. For the real-world case, parameter values varied considerably when calibrating against runoff only. However, parameter values were constrained significantly when calibrating against both runoff and groundwater levels. Furthermore, for one of the catchments, the results of the multi-criteria calibration motivated a modification of the model structure. Keywords: Multi-criteria calibration; genetic algorithm; parameter uncertainty; conceptual runoff models; HBV model; groundwater levels

Multi-Frequency Matched-Field Inversion of Benchmark Data Using a Genetic Algorithm

1998 ◽  
Vol 06 (01n02) ◽  
pp. 135-150 ◽  
Author(s):  
D. G. Simons ◽  
M. Snellen
Keyword(s):  
Genetic Algorithm ◽  
Test Cases ◽  
Data Sets ◽  
True Parameter ◽  
Benchmark Data ◽  
Line Array ◽  
Standard Normal ◽  
Water Test ◽  
Parameter Values ◽  
Field Inversion

For a selected number of shallow water test cases of the 1997 Geoacoustic Inversion Workshop we have applied Matched-Field Inversion to determine the geoacoustic and geometric (source location, water depth) parameters. A genetic algorithm has been applied for performing the optimization, whereas the replica fields have been calculated using a standard normal-mode model. The energy function to be optimized is based on the incoherent multi-frequency Bartlett processor. We have used the data sets provided at a few frequencies in the band 25–500 Hz for a vertical line array positioned at 5 km from the source. A comparison between the inverted and true parameter values is made.

A Analysis and Research on the Methods of Object Oriented Software Testing

2013 ◽  
Vol 709 ◽  
pp. 616-619
Author(s):  
Jing Chen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Test Data ◽  
Object Oriented ◽  
Automatic Generation ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Path Coverage ◽  
Parameter Values

This paper proposes a genetic algorithm-based method to generate test cases. This method provides information for test case generation using state machine diagrams. Its feature is realizing automation through fewer generated test cases. In terms of automatic generation of test data based on path coverage, the goal is to build a function that can excellently assess the generated test data and guide the genetic algorithms to find the targeting parameter values.

Distributed emitter parameter refinement based on maximum likelihood

2017 ◽  
Vol 10 (1) ◽  
pp. 2-11 ◽  
Author(s):  
Xin Xu
Keyword(s):  
Maximum Likelihood ◽  
Clustering Algorithms ◽  
Measurement Data ◽  
Synthetic Data ◽  
Superior Performance ◽  
Clustering Methods ◽  
True Parameter ◽  
Content Type ◽  
Refinement Method ◽  
Parameter Values

Purpose Emitter parameter estimation via signal sorting is crucial for communication, electronic reconnaissance and radar intelligence analysis. However, due to problems of transmitter circuit, environmental noises and certain unknown interference sources, the estimated emitter parameter measurements are still inaccurate and biased. As a result, it is indispensable to further refine the parameter values. Though the benchmark clustering algorithms are assumed to be capable of inferring the true parameter values by discovering cluster centers, the high computational and communication cost makes them difficult to adapt for distributed learning on massive measurement data. The paper aims to discuss these issues. Design/methodology/approach In this work, the author brings forward a distributed emitter parameter refinement method based on maximum likelihood. The author’s method is able to infer the underlying true parameter values from the huge measurement data efficiently in a distributed working mode. Findings Experimental results on a series of synthetic data indicate the effectiveness and efficiency of the author’s method when compared against the benchmark clustering methods. Originality/value With the refined parameter values, the complex stochastic parameter patterns could be discovered and the emitters could be identified by merging observations of consistent parameter values together. Actually, the author is in the process of applying her distributed parameter refinement method for PRI parameter pattern discovery and emitter identification. The superior performance ensures its wide application in both civil and military fields.

scholarly journals Reliability of Model Predictions Outside Calibration Conditions

2003 ◽  
Vol 34 (5) ◽  
pp. 477-492 ◽  
Author(s):  
Jan Seibert
Keyword(s):  
Goodness Of Fit ◽  
Model Development ◽  
Model Performance ◽  
Extreme Value Statistics ◽  
Groundwater Levels ◽  
Model Predictions ◽  
Flood Estimation ◽  
Sample Testing ◽  
Hbv Model ◽  
Runoff Model

Predictions of probabilities and magnitudes of extreme events are essential for water management. One approach for flood estimation is the use of conceptual runoff models. This approach, however, can be questioned for the same reason as the approach of extreme-value statistics: the model has to be used for conditions far beyond those used for model development and calibration. In this study the HBV model, a conceptual runoff model, was applied to four different catchments and differential split-sample testing (calibration on years with lower runoff peaks and testing it on years with higher peaks) was used to evaluate model performance for the situation when the model has to be used to simulate runoff during conditions different from those observed during calibration. To assess the value of improved calibration different goodness-of-fit measures were used, which allowed to explicitly consider the ability of the model to simulate groundwater-levels and peak flows. The results indicated that applying a model to conditions different from those during the calibration period might not give accurate results and that improved calibration procedures might not automatically provide more accurate flood estimations.

scholarly journals A Simple Automatic Calibration Routine for the HBV Model

1997 ◽  
Vol 28 (3) ◽  
pp. 153-168 ◽  
Author(s):  
Göran Lindström
Keyword(s):  
Calibration Method ◽  
Relative Volume ◽  
Automatic Calibration ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
New Criterion ◽  
Parameter Values ◽  
Hbv Model ◽  
Large Improvement ◽  
Runoff Model

A simple, but efficient, method for automatic calibration of the conceptual HBV rainfall-runoff model was developed. A new criterion, which combines the commonly used efficienyy criterion R2 and the relative volume error was introduced. Optimising this combined criterion resulted in R2 values nearly as high as those for optimssing only R2, but with much smaller volume errors. An earlier automatic calibration method for the HBV model relied on the use of differett criteria for different parameters. With the simplification to one single criterion, the optimum search method could be made more efficient. The optimisation is made for one parameter at a time, while the others are kept constant. This one-dimensional optimisation is repeated in a loop for all parameters. A new loop is performed as long as there is a sufficiently large improvement since the last one. After each loop a search is made in the direction which is defined by the differences in parameter values between the two latest loops. The calibration routine was developed for, and tested with, the HBV model, but it should be general enough to be applicable to other modess as well.

Modeling Heart Rate Dynamics in Response to Changes in Exercise Intensity

2015 ◽  
Author(s):  
Michael J. Mazzoleni ◽  
Claudio L. Battaglini ◽  
Brian P. Mann
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Heart Rate ◽  
Heart Rate Response ◽  
Synthetic Data ◽  
Model Parameters ◽  
Nonlinear Mathematical Model ◽  
Heart Rate Dynamics ◽  
Parameter Values ◽  
Good Agreement

This paper develops a nonlinear mathematical model to describe the heart rate response of an individual during cycling. The model is able to account for the fluctuations of an individual’s heart rate while they participate in exercise that varies in intensity. A method for estimating the model parameters using a genetic algorithm is presented and implemented, and the results show good agreement between the actual parameter values and the estimated values when tested using synthetic data.

scholarly journals Depletion of groundwater resources under rapid urbanisation in Africa: recent and future trends in the Nairobi Aquifer System, Kenya

2020 ◽  
Vol 28 (8) ◽  
pp. 2635-2656
Author(s):  
Samson Oiro ◽  
Jean-Christophe Comte ◽  
Chris Soulsby ◽  
Alan MacDonald ◽  
Canute Mwakamba
Keyword(s):  
Negative Impact ◽  
Groundwater Resources ◽  
Climatic Data ◽  
Groundwater Abstraction ◽  
Management Scenarios ◽  
Aquifer System ◽  
Groundwater Levels ◽  
Future Evolution ◽  
Level Data ◽  
Groundwater Assessment

AbstractThe Nairobi volcano-sedimentary regional aquifer system (NAS) of Kenya hosts >6 M people, including 4.7 M people in the city of Nairobi. This work combines analysis of multi-decadal in-situ water-level data with numerical groundwater modelling to provide an assessment of the past and likely future evolution of Nairobi’s groundwater resources. Since the mid-1970s, groundwater abstraction has increased 10-fold at a rate similar to urban population growth, groundwater levels have declined at a median rate of 6 m/decade underneath Nairobi since 1950, whilst built-up areas have increased by 70% since 2000. Despite the absence of significant trends in climatic data since the 1970s, more recently, drought conditions have resulted in increased applications for borehole licences. Based on a new conceptual understanding of the NAS (including insights from geophysics and stable isotopes), numerical simulations provide further quantitative estimates of the accelerating negative impact of abstraction and capture the historical groundwater levels quite well. Analysis suggests a groundwater-level decline of 4 m on average over the entire aquifer area and up to 46 m below Nairobi, net groundwater storage loss of 1.5 billion m3 and 9% river baseflow reduction since 1950. Given current practices and trajectories, these figures are predicted to increase six-fold by 2120. Modelled future management scenarios suggest that future groundwater abstraction required to meet Nairobi projected water demand is unsustainable and that the regional anthropogenically-driven depletion trend can be partially mitigated through conjunctive water use. The presented approach can inform groundwater assessment for other major African cities undergoing similar rapid groundwater development.

scholarly journals An Integrated Framework for the Inference of Viral Population History From Reconstructed Genealogies

Genetics ◽  
2000 ◽  
Vol 155 (3) ◽  
pp. 1429-1437
Author(s):  
Oliver G Pybus ◽  
Andrew Rambaut ◽  
Paul H Harvey
Keyword(s):  
Maximum Likelihood ◽  
Sequence Data ◽  
Demographic History ◽  
Population History ◽  
Maximum Likelihood Estimates ◽  
Viral Population ◽  
True Parameter ◽  
Subtype B ◽  
Exponential Growth Model ◽  
Parameter Values

Abstract We describe a unified set of methods for the inference of demographic history using genealogies reconstructed from gene sequence data. We introduce the skyline plot, a graphical, nonparametric estimate of demographic history. We discuss both maximum-likelihood parameter estimation and demographic hypothesis testing. Simulations are carried out to investigate the statistical properties of maximum-likelihood estimates of demographic parameters. The simulations reveal that (i) the performance of exponential growth model estimates is determined by a simple function of the true parameter values and (ii) under some conditions, estimates from reconstructed trees perform as well as estimates from perfect trees. We apply our methods to HIV-1 sequence data and find strong evidence that subtypes A and B have different demographic histories. We also provide the first (albeit tentative) genetic evidence for a recent decrease in the growth rate of subtype B.

scholarly journals Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1779
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Random Search ◽  
Automatic Generation ◽  
Superior Performance ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Network Systems ◽  
Routing Problem

Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

Sign in / Sign up

Export Citation Format

Share Document