Comparison of Missing Data Infilling Mechanisms for Recovering a Real-World Single Station Streamflow Observation.

Reconstructing missing streamflow data can be challenging when additional data are not available, and missing data imputation of real-world datasets to investigate how to ascertain the accuracy of imputation algorithms for these datasets are lacking. This study investigated the necessary complexity of missing data reconstruction schemes to obtain the relevant results for a real-world single station streamflow observation to facilitate its further use. This investigation was implemented by applying different missing data mechanisms spanning from univariate algorithms to multiple imputation methods accustomed to multivariate data taking time as an explicit variable. The performance accuracy of these schemes was assessed using the total error measurement (TEM) and a recommended localized error measurement (LEM) in this study. The results show that univariate missing value algorithms, which are specially developed to handle univariate time series, provide satisfactory results, but the ones which provide the best results are usually time and computationally intensive. Also, multiple imputation algorithms which consider the surrounding observed values and/or which can understand the characteristics of the data provide similar results to the univariate missing data algorithms and, in some cases, perform better without the added time and computational downsides when time is taken as an explicit variable. Furthermore, the LEM would be especially useful when the missing data are in specific portions of the dataset or where very large gaps of 'missingness' occur. Finally, proper handling of missing values of real-world hydroclimatic datasets depends on imputing and extensive study of the particular dataset to be imputed.

A Security Concept Based on Scaler Distribution of a Novel Intrusion Detection Device for Wireless Sensor Networks in a Smart Environment.

Following the significant improvement of technology in terms of data collection and treatment during the last decades, the notion of a smart environment has widely taken an important pedestal in the science industry. Built in order to better manage assets, smart environments provide a livable environment for users or citizens through the deployment of sensors responsible for data collection. Much research has been done to provide security to the involved data, which are extremely sensitive. However, due to the small size and the memory constraint of the sensors, many of these works are difficult to implement. In this paper, a different concept for wireless sensor security in smart environments is presented. The proposed security system, which is based on the scaler distribution of a novel electronic device, the intrusion detection system (IDS), reduces the computational functions of the sensors and therefore maximizes their efficiency. The IDS also introduces the concept of the feedback signal and "trust table" used to trigger the detection and isolation mechanism in case of attacks. Generally, it ensures the whole network security through cooperation with other IDSs and, therefore, eliminates the problem of security holes that may occur while adopting such a security technique.

Data-Driven Modeling and the Influence of Objective Function Selection on Model Performance in Limited Data Regions.

The identification of unit hydrographs and component flows from rainfall, evapotranspiration and streamflow data (IHACRES) model has been proven to be an efficient yet basic model to simulate rainfall-runoff processes due to the difficulty in obtaining the comprehensive data required by physical models, especially in data-scarce, semi-arid regions. The success of a calibration process is tremendously dependent on the objective function chosen. However, objective functions have been applied largely in over daily and monthly scales and seldom over sub-daily scales. This study, therefore, implements the IHACRES model using 'hydromad' in R to simulate flood events with data limitations in Zhidan, a semi-arid catchment in China. We apply objective function constraints by time aggregating the commonly used Nash-Sutcliffe efficiency into daily and hourly scales to investigate the influence of objective function constraints on the model performance and the general capability of the IHACRES model to simulate flood events in the study watershed. The results of the study demonstrated the advantage of the finer time-scaled hourly objective function over its daily counterpart in simulating runoff for the selected flood events. The results also indicated that the IHACRES model performed extremely well in the Zhidan watershed, presenting the feasibility of the use of the IHACRES model to simulate flood events in data scarce, semi-arid regions.