Robust Parameter Estimation Framework of a Rainfall-Runoff Model Using Pareto Optimum and Minimax Regret Approach

This study developed a robust parameter set (ROPS) selection framework for a rainfall-runoff model that considers multi-events using the Pareto optimum and minimax regret approach (MRA). The calibrated parameter sets based on the Nash-Sutcliffe coefficient (NSE) for two events were derived using a genetic algorithm. We generated 41 combinations for weighting values between two events for the multi-event objective function and derived 41 Pareto optimum points that were considered as the ROPS candidates. Then, two different approaches for parameter selection were proposed to determine the ROPS among the candidates: one uses NSE only and the other uses four performance measures (NSE, peak flow error, root mean square error and percentage of bias). In the NSE-only method, five events, including two events from the calibration set and three events from the evaluation set, were used, and the ROPS was selected based on the regrets of both the calibration and the evaluation sets. In the multiple (i.e., four) performance measure method, only three events from the evaluation set were used and the ROPS was determined based on the regrets of twelve different cases, including three events with four measures. As a result, while single- and multi-event optimizations produced satisfying results for the calibration events, the optimized parameters from the single-event calibration do not perform well for another event, even one with the same criteria, such as NSE. The results of this study suggest that the optimized parameter set from the well-weighted objective function can successfully simulate not only hydrographs in general but also others, such as peak flow. In addition, the ROPS can be selected by considering the multiple performance measures of multiple validation events, as well as the NSE only of multiple calibration and validation events. Note that the study provides a framework that could be performed reasonably well with a limited number of events. While the computational resources might not be a limiting factor these days, it is still valuable to have such a tool for several reasons: one could utilize it for an operational decision making support tool, as the full searches for an optimal set of parameters might not be performed in the operational facility. It could also be used in a situation where one has a limited number of good-quality observational data for some reason

Water Resource Vulnerability Characteristics by District’s Population Size in a Changing Climate Using Subjective and Objective Weights

The goal of this study is to derive water resource vulnerability characteristics for South Korea according to individual district populations in a changing climate. The definition of water resource vulnerability in this study consists of potential flood damage and potential water scarcity. To quantify these vulnerabilities, key factors, or indicators affecting vulnerability, are integrated with a technique for order of preference by similarity to ideal solution (TOPSIS), which is a multi-criteria decision-making approach to determine the optimal alternative by considering both the best and worst solutions. The weight for each indicator is determined based on both the Delphi technique and Shannon’s entropy, which are employed to reduce the uncertainty in the process of determining the weights. The Delphi technique reflects expert opinions, and Shannon’s entropy reflects the uncertainty of the performance data. Under A1B climate change scenarios, medium-sized districts (200,000–300,000 inhabitants) are the most vulnerable regarding potential flood damage; the largest districts (exceeding 500,000 inhabitants) are found to be the most vulnerable with respect to potential water scarcity. This result indicates that the local governments of cities or districts with more than 200,000 inhabitants should implement better preventative measures for water resources. In addition, the Delphi and entropy methods show the same rankings for flood vulnerability; however, these approaches produce slightly different rankings regarding water scarcity vulnerability. Therefore, it is suggested that rankings from not only subjective but also objective weights should be considered in making a final decision to implement specific adaptive measures to climate change

An Android Malware Detection System using a Knowledge-based Permission Counting Method

As the number of cases of damage caused by malicious apps increases, accurate detection is required through various detection conditions, not just detection using simple techniques. In this paper, we propose a knowledge-based machine learning method using authority information and adding its usage counting features. This method is classifying training apps and malicious apps through machine learning using permission features in manifest.xml of Android apps. As a result of the experiment, accuracy, recall, precision, F1 score are 99.01%, 97.70%, 100.0%, 99.01%, respectively. Since Recall is higher than other indicators, it accurately predicts malicious apps as malicious. In other words, the proposed system is effective in preventing the distribution of malicious apps

Facile Formation of Metal–Oxide Nanocraters by Laser Irradiation for Highly Enhanced Detection of Volatile Organic Compounds

Although various fabrication methods for metal–oxide nanostructures have been well developed for enlarged surface area, numerous efforts to further enhance the effective surface area for their chemical sensor applications are still being studied. Herein, a high‐power laser is irradiated on the existing metal–oxide nanostructures to expose the hidden inner surface of the nanostructures for full participation in the surface gas‐sensing reactions, resulting in extraordinary gas‐sensing performance. In addition, noble metal catalyst decoration at both the inner and outer surfaces of the nanostructures records extremely high gas response and selectivity to volatile organic compounds. The numerical simulation and experimental verification of the effects of high‐power laser irradiation for morphological evolution of the metal–oxide nanostructures can provide a new perspective toward the time‐efficient development of nanostructure‐based electronic devices