Automatic Calibration of a Hydrologic Model for Simulating Groundwater Table Fluctuations on Farms in the Everglades Agricultural Area of South Florida

Kwon, Hoyoung; Grunwald, Sabine; Beck, Howard W.; Yun-Chul, Jung

doi:10.1002/ird.1822

Cited by 2 publications

(1 citation statement)

References 31 publications

(40 reference statements)

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“…Kaewboonma et al (2014) offer a drought ontology model that includes river basin, statistics, and task ontology and develop a drought management IS. Kwon et al (2014) 2014) create a component library (ontology) for semi-distributed watershed modeling that consists of organized and structured modeling knowledge. Basic modeling knowledge is incorporated into the library, allowing for accurate modeling of various water fluxes and nutrient loadings on a watershed scale.…”

Section: Hydrology Domain Summarymentioning

confidence: 99%

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

Baydaroğlu¹,

Yeșilköy²,

Sermet³

et al. 2022

Preprint

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Open science presents a new approach for knowledge discovery, dissemination, and integrity. The idea behind open science is to reinforce research activities and create open knowledge networks by exploring, organizing, and sharing scientific data, as well as making research results transparent, open and integrated. Big data derived from remote sensing, ground-based measurements, models and simulations, social media and crowdsourcing, and wide variety of structured and unstructured sources requires significant efforts for data and knowledge management. Innovations and developments in information technology for the last couple of decades have made data and knowledge management possible for insurmountable amount of data collected and generated over the last decades. This enabled open knowledge networks to be built that lead to new ideas in scientific research and business world. To design and develop open knowledge networks, ontologies are essential since they form the backbone of conceptualization of a given knowledge domain. In this article, a systematic literature review is conducted to examine research involving ontologies related to hydrological processes and water resources management. The hydrologic cycle (water cycle) is a multi-component and multi-process system that is shaped by various dynamic factors. Because all components of the hydrologic cycle interact with one another and water distribution on Earth is not uniform in time and space, modeling the hydrologic cycle and management of hydrologic cycle components are complex and difficult endeavors. Ontologies in the hydrology domain support the comprehension, monitoring, and representation of the hydrologic cycle’s complex structure, as well as the predictions of its processes. They contribute to the development of necessary ontology-based information and decision support systems, the comprehension of environmental and atmospheric phenomena, the development of climate and water resiliency concepts, the creation of educational tools with artificial intelligence, and the strengthening of related cyberinfrastructures. This review provides an explanation of key issues and challenges in ontology development based on hydrologic processes to guide development of next generation artificial intelligence applications. The authors discuss future research prospects in combination with the artificial intelligence and hydroscience.

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Section: Hydrology Domain Summarymentioning

confidence: 99%

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

Baydaroğlu¹,

Yeșilköy²,

Sermet³

et al. 2022

Preprint

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Comparison of Performance between Genetic Algorithm and SCE-UA for Calibration of SCS-CN Surface Runoff Simulation

Jeon

Park

Engel

2014

Water

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Global optimization methods linked with simulation models are widely used for automated calibration and serve as useful tools for searching for cost-effective alternatives for environmental management. A genetic algorithm (GA) and shuffled complex evolution (SCE-UA) algorithm were linked with the Long-Term Hydrologic Impact Assessment (L-THIA) model, which employs the curve number (SCS-CN) method. The performance of the two optimization methods was compared by automatically calibrating L-THIA for monthly runoff from 10 watersheds in Indiana. The selected watershed areas ranged from 32.7 to 5844.1 km 2 . The SCS-CN values and total five-day rainfall for adjustment were optimized, and the objective function used was the Nash-Sutcliffe value (NS value). The GA method rapidly reached the optimal space until the 10th generating population (generation), and after the 10th generation solutions increased dispersion around the optimal space, called a cross hair pattern, because of mutation rate increase. The number of looping executions influenced the performance of model calibration for the SCE-UA and GA method. The GA method performed better for the case of fewer loop executions than the SCE-UA method. For most watersheds, calibration performance using GA was better than for SCE-UA until the 50th generation when the number of model loop executions was OPEN ACCESSWater 2014, 6 3434 around 5150 (one generation has 100 individuals). However, after the 50th generation of the GA method, the SCE-UA method performed better for calibrating monthly runoff compared to the GA method. Optimized SCS-CN values for primary land use types were nearly the same for the two methods, but those for minor land use types and total five-day rainfall for AMC adjustment were somewhat different because those parameters did not significantly influence calculation of the objective function. The GA method is recommended for cases when model simulation takes a long time and the model user does not have sufficient time for an optimization program to search for the best values of calibration parameters. For other cases, the SCE-UA program is recommended for automatic model calibration.

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Automatic Calibration of a Hydrologic Model for Simulating Groundwater Table Fluctuations on Farms in the Everglades Agricultural Area of South Florida

Cited by 2 publications

References 31 publications

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

Comparison of Performance between Genetic Algorithm and SCE-UA for Calibration of SCS-CN Surface Runoff Simulation

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