Cluster Analysis on Dengue Incidence and Weather Data Using K-Medoids and Fuzzy C-Means Clustering Algorithms (Case Study: Spread of Dengue in the DKI Jakarta Province)

In Indonesia, Dengue incidence tends to increase every year but has been fluctuating in recent years. The potential for Dengue outbreaks in DKI Jakarta, the capital city, deserves serious attention. Weather factors are suspected of being associated with the incidence of Dengue in Indonesia. This research used weather and Dengue incidence data for five regions of DKI Jakarta, Indonesia, from December 30, 2008, to January 2, 2017. The study used a clustering approach on time-series and non-time-series data using K-Medoids and Fuzzy C-Means Clustering. The clustering results for the non-time-series data showed a positive correlation between the number of Dengue incidents and both average relative humidity and amount of rainfall. However, Dengue incidence and average temperature were negatively correlated. Moreover, the clustering implementation on the time-series data showed that rainfall patterns most closely resembled those of Dengue incidence. Therefore, rainfall can be used to estimate Dengue incidence. Both results suggest that the government could utilize weather data to predict possible spikes in DHF incidence, especially when entering the rainy season and alert the public to greater probability of a Dengue outbreak.

Modeling and Forecasting of Agricultural Commodity Production under Changing Climatic Condition: A Review

India is a country with an agrarian economy in which majority of its population rely on agriculture directly as their source of livelihoof. Climate has a very significant role in agricultural production. It predominantly influences growth of the crop, development of the crop and eventually crop yield. Climate also significantly influences the outbreak of disease and pest; it affects the requirement of water by the crop. Possible changes in weather factors, like precipitation, temperature and CO2 concentration are expected to have a significant impact on crop growth. If farmers are able to predict the weather activities and are aware of the effect of these activities on crop production, then it will be beneficial to them as a feasible plan can be devised synchronizing the crop production activities as per changes in the climatic conditions. In view of tackling the aforementioned problem, this article describes various statistical techniques that can play a crucial role in forecasting production of agricultural commodities changing climatic conditions.

SOLAR INSOLATION CALCULATION PROGRAM IN MATLAB / SIMULINK ENVIRONMENT

The article describes the mathematical calculation of the inflow of solar insolation, the device and operation of the insolation calculation program, graphs showing the change in radiation from the influence of weather factors, the location of the installation of solar receivers are ob-tained

A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting

The reliability of the power supply depends on the reliability of the structure of the grid. Grid networks are exposed to varying weather events, which makes them prone to faults. There is a growing concern that climate change will lead to increasing numbers and severity of weather events, which will adversely affect grid reliability and electricity supply. Predictive models of electricity reliability have been used which utilize computational intelligence techniques. These techniques have not been adequately explored in forecasting problems related to electricity outages due to weather factors. A model for predicting electricity outages caused by weather events is presented in this study. This uses the back-propagation algorithm as related to the concept of artificial neural networks (ANNs). The performance of the ANN model is evaluated using real-life data sets from Pietermaritzburg, South Africa, and compared with some conventional models. These are the exponential smoothing (ES) and multiple linear regression (MLR) models. The results obtained from the ANN model are found to be satisfactory when compared to those obtained from MLR and ES. The results demonstrate that artificial neural networks are robust and can be used to predict electricity outages with regards to faults caused by severe weather conditions.

Environmental drivers of autumn migration departure decisions in midcontinental mallards

Background The timing of autumn migration in ducks is influenced by a range of environmental conditions that may elicit individual experiences and responses from individual birds, yet most studies have investigated relationships at the population level. We used data from individual satellite-tracked mallards (Anas platyrhynchos) to model the timing and environmental drivers of autumn migration movements at a continental scale. Methods We combined two sets of location records (2004–2007 and 2010–2011) from satellite-tracked mallards during autumn migration in the Mississippi Flyway, and identified records that indicated the start of long-range (≥ 30 km) southward movements during the migration period. We modeled selection of departure date by individual mallards using a discrete choice model accounting for heterogeneity in individual preferences. We developed candidate models to predict the departure date, conditional on daily mean environmental covariates (i.e. temperature, snow and ice cover, wind conditions, precipitation, cloud cover, and pressure) at a 32 × 32 km resolution. We ranked model performance with the Bayesian Information Criterion. Results Departure was best predicted (60% accuracy) by a “winter conditions” model containing temperature, and depth and duration of snow cover. Models conditional on wind speed, precipitation, pressure variation, and cloud cover received lower support. Number of days of snow cover, recently experienced snow cover (snow days) and current snow cover had the strongest positive effect on departure likelihood, followed by number of experienced days of freezing temperature (frost days) and current low temperature. Distributions of dominant drivers and of correct vs incorrect prediction along the movement tracks indicate that these responses applied throughout the latitudinal range of migration. Among recorded departures, most were driven by snow days (65%) followed by current temperature (30%). Conclusions Our results indicate that among the tested environmental parameters, the dominant environmental driver of departure decision in autumn-migrating mallards was the onset of snow conditions, and secondarily the onset of temperatures close to, or below, the freezing point. Mallards are likely to relocate southwards quickly when faced with snowy conditions, and could use declining temperatures as a more graduated early cue for departure. Our findings provide further insights into the functional response of mallards to weather factors during the migration period that ultimately determine seasonal distributions.

Impact of weather factors on hospital admission of hand, foot and mouth disease under climate change scenarios in Ho Chi Minh City.

Hand, foot, and mouth disease (HFMD) is one of the most common communicable diseases in Vietnam. The present study aims to examine the association between weather factors and HFMD in association with hospitalisation. Daily and weekly weather and HFMD data from 2013 to 2018 in Ho Chi Minh City were deployed. Poisson regression model combined with a distributed lag non-linear model (DLNM) was applied to examine the relationship between weather factors and HFMD. The forecasting model for HFMD was performed by using the Global Climate Model (GCM) and Yasushi Honda model. The result showed that the average daily temperature induces an increase in the risk of HFDM hospitalisation was 26°C- 30.1°C. The average daily humidity also caused increasing the risk of hospitalisation of HFMD was 75% - 85%. However, the average daily humidity <60% reduced the risk of getting HFMD. The study provides quantitative evidence that the incidence of HFMD cases was associated with meteorological variables including average daily temperature and daily humidity in Ho Chi Minh City. This findings implies that there is a need for building a public health policy for eliminating and mitigating climate change impact on community health in a resilient approach.

The Use of Weather Website Data for Construction Project Decision-Making in The Short Term

In most work sites, it is a priority to keep the work going well and to avoid unforeseen incidents. Fluctuations in weather conditions are one of the factors affecting the continuity of work in construction projects. Indeed, for example, the temperature is important in concrete and asphalt works, and wind speed is important in lifting and high construction works. Therefore, taking the appropriate decision, starting and completing the work, is very important to maintain the quality of the project. This research aims to demonstrate the reliability of short-term decision-making through data taken from the weather site five days before the time to work. The data was collected for a month, five days before the intended day and on the same day, day and night, for different weather factors by weather location such as temperature, humidity, possibility of rain, Uv index, wind speed. By analyzing the data, it was found that there was little difference in those predictors of all the factors recorded. To conclude at the end of the study that it is possible to rely on the decision-making on the weather location in small and medium projects, but in large and sensitive projects, they need to rely on more accurate data than relying on weather location data.

Effects of weather and moon phases on emergency medical use after fall injury: A population-based nationwide study

Background Previous studies reported that changes in weather and phases of moon are associated with medical emergencies and injuries. However, such studies were limited to hospital or community level without explaining the combined effects of weather and moon phases. We investigated whether changes in weather and moon phases affected emergency department (ED) visits due to fall injuries (FIs) based on nationwide emergency patient registry data. Methods Nationwide daily data of ED visits after FI were collected from 11 provinces (7 metropolitan cities and 4 rural provinces) in Korea between January 2014 and December 2018. The daily number of FIs was standardized into FI per million population (FPP) in each province. A multivariate regression analysis was conducted to elucidate the relationship between weather factors and moon phases with respect to daily FPP in each province. The correlation between weather factors and FI severity was also analyzed. Results The study analyzed 666,912 patients (418,135 in metropolitan and 248,777 in rural areas) who visited EDs on weekdays. No regional difference was found in age or gender distribution between the two areas. Precipitation, minimum temperature and wind speed showed a significant association with FI in metropolitan areas. In addition, sunshine duration was also substantial risk factors for FI in rural areas. The incidence of FIs was increased on full moon days than on other days in rural areas. Injury severity was associated with weather factors such as minimum temperature, wind speed, and cloud cover. Conclusion Weather changes such as precipitation, minimum temperature, and wind speed are associated with FI in metropolitan and rural areas. In addition, sunshine duration and full moon are significantly associated with FI incidence only in rural areas. Weather factors are associated with FI severity.

Pertanggungjawaban Pidana Terhadap Pencurian Barang Penumpang Pesawat Studi Dibandara Udara Kualanamu Internasional Airport

This study aims to determine that there are still many events that are still require the liability from the commercial air transportation company, both from the carrier company and those who are related to the carrier, such as flight delays (flight delay) either caused by weather factors or internal factors from the carrier company, the occurrence of negligence from the transport officer which causes the loss of goods owned by passengers, or because of there is an event for which the reason is unknown so that the aircraft experiences interference during the flight, from the results of this research it can be seen that the carrier operating the aircraft is obliged to be responsible for losses against:a. passengers who died, disability or injury;b. lost or damaged of the cabin baggage;c. lost, destroyed, or damaged of the checked baggage;d. lost, destroyed, or damaged of the cargo;e. delay in air transportation; andf. losses suffered by third partiesas well as who are the parties involved, the requirements that must be fulfilled and how the rights and the obligations of the parties are fulfilled, as well as other provisions in its implementation if a passenger's goods are lost or damaged and provide a description of its protection.Keywords : Liability, Theft of Goods, Aircraft Passengers, Kualanamu International Airport.