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

Fall Risk Prediction for Community-Dwelling Older Adults: Analysis of Assessment Scale and Evaluation Items without Actual Measurement

The frequency of falls increases with age. In Japan, the population is aging rapidly, and fall prevention measures are an urgent issue. However, assessing fall risk during the coronavirus disease pandemic was complicated by the social distancing measures implemented to prevent the disease, while traditional assessments that involve actual measurements are complicated. This prospective cohort study predicted the risk of falls in community-dwelling older adults using an assessment method that does not require actual measurements. A survey was conducted among 434 community-dwelling older adults to obtain data regarding baseline attributes (age, sex, living with family, use of long-term care insurance, and multimorbidity), Frailty Screening Index (FSI) score, and Questionnaire for Medical Checkup of Old-Old (QMCOO) score. The participants were categorized into fall (n = 78) and non-fall (n = 356) groups. The binomial logistic regression analysis showed that it is better to focus on the QMCOO sub-item score, which focuses on multiple factors. The items significantly associated with falls were Q5 (odds ratio [OR] 1.95), Q8 (OR 2.33), and Q10 (OR 3.68). Our results were similar to common risk factors for falls in normal times. During the pandemic, being able to gauge the risk factors for falls without actually measuring them was important.

Trends in climatic and hydrological parameters in the Ajora-Woybo watershed, Omo-Gibe River basin, Ethiopia

Abstract

The identification of hydro-meteorological trends is essential for analyzing climate change and river discharge at the watershed level. The Ajora-Woybo watershed in Ethiopia was studied for long-term trends in rainfall, temperature, and discharge at the annual, monthly, and seasonal time scales. The rainfall and temperature data extend 1990 to 2020, whereas the discharge data span from1990 to 2015. The Pettitt and Standard Normal Homogeneity Test (SNHT) tests were used to determine homogeneity. The Mann–Kendall and Sen's slope tests, as well as numerous variability measures, were then employed for trend analysis. The degree of relationship between climate variables and river discharge was determined using Pearson correlation coefficients (r). Inhomogeneity was discovered in annual rainfall data from the Angacha and Areka stations. Rainfall and discharge showed insignificant trends over time, with increasing and decreasing variability across stations. Monthly rainfall decreasing trends were observed to be significantly falling in February and March. Rainfall and runoff increase just insignificantly during the Kiremt season. On the other hand, minimum, maximum, and mean annual temperatures showed significant trends with annual increases of 0.01 °C, 0.04 °C, and 0.025 °C, respectively. In this study, the relationships between discharge and temperature and rainfall were found to be moderate and minimal, respectively. Generally, the results of the long-term examination of the hydrological and climate parameters in the watershed show that water resources vary throughout and over time. As a result, designing strategies require due attention.

Article highlights

Spatiotemporal trends in mean and extreme climate variables over 1981-2020 in Meki watershed of central rift valley basin, Ethiopia

Understanding the spatiotemporal changes of climate extremes is essential for managing climatic risk. In the present study, trends of annual and seasonal climate variables along with extreme temperature and precipitation were examined in eight climatic stations of Meki watershed, the Central Rift Valley Basin, Ethiopia during the period 1981 to 2020. A set of 20 precipitation and temperature extreme indices were selected and computed in RClimDex package of the R program to detect climate extreme events. The recent method of Innovative trend analysis, the non-parametric Mann-Kendall trend test, and Sen's slope estimator were applied to evaluate trends of the annual and seasonal precipitation and temperature. The result indicates that the annual precipitation has been decreasing in 71% of climatic stations and 43% of these stations showed a significant trend. The annual minimum temperature declined in 75% of the stations, whereas all stations indicated a significant increasing trend in annual mean maximum temperature. The Mann-Kendall test detected a significant increasing and decreasing trend in most of the temperature and precipitation extreme indices respectively. For the temperature extremes, more frequent warm and fewer cold temperature extremes were observed. In more than 75% of the climatic stations, a considerable drying trend was found for the precipitation extreme indices. Besides, stations with significant warming and drying trends were located in the downstream areas of the watershed. Overall, this study proved that the observed warming trend in mean annual temperature contributed to changes in the normal thresholds of extremes. The change in precipitation and temperature extremes have increased the frequency, intensity, and duration of extreme events in most of the stations. Likewise, climate extreme events that occur more frequently with high intensity are likely to exacerbate climatic risks, which require proper planning of risk management strategies in Meki watershed.

An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics

With rapid urbanization, awareness of environmental pollution is growing rapidly and, accordingly, interest in environmental sensors that measure atmospheric and indoor air quality is increasing. Since these IoT-based environmental sensors are sensitive and value reliability, it is essential to deal with missing values, which are one of the causes of reliability problems. Characteristics that can be used to impute missing values in environmental sensors are the time dependency of single variables and the correlation between multivariate variables. However, in the existing method of imputing missing values, only one characteristic has been used and there has been no case where both characteristics were used. In this work, we introduced a new ensemble imputation method reflecting this. First, the cases in which missing values occur frequently were divided into four cases and were generated into the experimental data: communication error (aperiodic, periodic), sensor error (rapid change, measurement range). To compare the existing method with the proposed method, five methods of univariate imputation and five methods of multivariate imputation—both of which are widely used—were used as a single model to predict missing values for the four cases. The values predicted by a single model were applied to the ensemble method. Among the ensemble methods, the weighted average and stacking methods were used to derive the final predicted values and replace the missing values. Finally, the predicted values, substituted with the original data, were evaluated by a comparison between the mean absolute error (MAE) and the root mean square error (RMSE). The proposed ensemble method generally performed better than the single method. In addition, this method simultaneously considers the correlation between variables and time dependence, which are characteristics that must be considered in the environmental sensor. As a result, our proposed ensemble technique can contribute to the replacement of the missing values generated by environmental sensors, which can help to increase the reliability of environmental sensor data.