Time-Series Study of Associations between Rates of People Affected by Disasters and the El Niño Southern Oscillation (ENSO) Cycle

Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

The Role of El Niño in Driving Drought Conditions over the Last 2000 Years in Thailand

Irregular climate events frequently occur in Southeast Asia due to the numerous climate patterns combining. Thailand sits at the confluence of these interactions, and consequently experiences major hydrological events, such as droughts. Proxy data, speleothem records, lake sediment sequences and tree ring chronologies were used to reconstruct paleo drought conditions. These trends were compared with modelled and historic El Niño Southern Oscillation (ENSO) data to assess if the ENSO climate phenomena is causing droughts in Thailand. Drought periods were found to occur both during El Niño events and ENSO neutral conditions. This indicates droughts are not a product of one climate pattern, but likely the result of numerous patterns interacting. There is uncertainty regarding how climate patterns will evolve under climate change, but changes in amplitude and variability could potentially lead to more frequent and wider reaching hydrological disasters. It is vital that policies are implemented to cope with the resulting social and economic repercussions, including diversification of crops and reorganisation of water consumption behaviour in Thailand.

A multi-scalar climatological analysis in preparation for extreme heat at the Tokyo 2020 Olympic and Paralympic Games

Extreme heat can be harmful to human health and negatively affect athletic performance. The Tokyo Olympic and Paralympic Games are predicted to be the most oppressively hot Olympics on record. An interdisciplinary multi-scale perspective is provided concerning extreme heat in Tokyo-from planetary atmospheric dynamics, including El Niño Southern Oscillation (ENSO), to fine-scale urban temperatures-as relevant for heat preparedness efforts by sport, time of day, and venue. We utilize stochastic methods to link daytime average wet bulb globe temperature (WBGT) levels in Tokyo in August (from meteorological reanalysis data) with large-scale atmospheric dynamics and regional flows from 1981 to 2016. Further, we employ a mesonet of Tokyo weather stations (2009-2018) to interpolate the spatiotemporal variability in near-surface air temperatures at outdoor venues. Using principal component analysis, two planetary (ENSO) regions in the Pacific Ocean explain 70% of the variance in Tokyo's August daytime WBGT across 35 years, varying by 3.95°C WGBT from the coolest to warmest quartile. The 10-year average daytime and maximum intra-urban air temperatures vary minimally across Tokyo (<1.2°C and 1.7°C, respectively), and less between venues (0.6-0.7°C), with numerous events planned for the hottest daytime period (1200-1500 hr). For instance, 45% and 38% of the Olympic and Paralympic road cycling events (long duration and intense) occur midday. Climatologically, Tokyo will present oppressive weather conditions, and March-May 2020 is the critical observation period to predict potential anomalous late-summer WBGT in Tokyo. Proactive climate assessment of expected conditions can be leveraged for heat preparedness across the Game's period.