An improved genetic algorithm for rainfall-runoff model calibration and function optimization

Ndiritu, John; Daniell, TM

doi:10.1016/s0895-7177(00)00273-9

Cited by 45 publications

(15 citation statements)

References 12 publications

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“…Dynamic Parameter Encoding adaptively controls the mapping from fixed-length chromosomes to real values, such that at each subsequent iteration, the algorithm searches over a smaller search space. Ndiritu and Daniell (2001) used a similar approach to refine the search space of a GA used for rainfall-runoff model calibration.…”

Section: Current Statusmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

508

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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Section: Current Statusmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

508

317

View full text Add to dashboard Cite

show abstract

“…The model was prepared and calibrated with appropriate data to create a reliable basin representation [3,37]. Watershed parameters such as infiltration coefficients, time of concentration, and base-flow were modified to produce a best fit between model and observations.…”

Section: Nam Model Calibration For Langat Basinmentioning

confidence: 99%

Comparison of recorded rainfall with quantitative precipitation forecast in a rainfall-runoff simulation for the Langat River Basin, Malaysia

et al. 2011

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Observed rainfall is used for runoff modeling in flood forecasting where possible, however in cases where the response time of the watershed is too short for flood warning activities, a deterministic quantitative precipitation forecast (QPF) can be used. This is based on a limited-area meteorological model and can provide a forecasting horizon in the order of six hours or less. This study applies the results of a previously developed QPF based on a 1D cloud model using hourly NOAA-AVHRR (Advanced Very High Resolution Radiometer) and GMS (Geostationary Meteorological Satellite) datasets. Rainfall intensity values in the range of 3-12 mm/hr were extracted from these datasets based on the relation between cloud top temperature (CTT), cloud reflectance (CTR) and cloud height (CTH) using defined thresholds. The QPF, prepared for the rainstorm event of 27 September to 8 October 2000 was tested for rainfall runoff on the Langat River Basin, Malaysia, using a suitable NAM rainfall-runoff model. The response of the basin both to the rainfall-runoff simulation using the QPF estimate and the recorded observed rainfall is compared here, based on their corresponding discharge hydrographs. The comparison of the QPF and recorded rainfall showed R 2 = 0.9028 for the entire basin. The runoff hydrograph for the recorded rainfall in the Kajang sub-catchment showed R 2 = 0.9263 between the observed and the simulated, while that of the QPF rainfall was R 2 = 0.819. This similarity in runoff suggests there is a high level of accuracy shown in the improved QPF, and that significant improvement of flood forecasting can be achieved through 'Nowcasting', thus increasing the response time for flood early warnings.

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“…Then zooming method was applied to reduce the search space around the candidate optimum solution point. Several zooming methods have been developed for different applications (Ndiritu andDaniel, 2001, Kwon et al 2003). In this project, the GA was run 30 times, and then the new range was set to be between maximum and minimum numbers of the 30 points.…”

Section: Set-up Of the Genetic Algorithmmentioning

confidence: 99%

Modeling of Biological Interfacial Processes Using Thickness–Shear Mode Sensors

Ergezen¹,

Desa²,

Hochman³

et al. 2011

Acoustic Waves - From Microdevices to Helioseismology

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An improved genetic algorithm for rainfall-runoff model calibration and function optimization

Cited by 45 publications

References 12 publications

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Comparison of recorded rainfall with quantitative precipitation forecast in a rainfall-runoff simulation for the Langat River Basin, Malaysia

Modeling of Biological Interfacial Processes Using Thickness–Shear Mode Sensors

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