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Statistical Downscaling (SD) is a model that uses satellite data from General Circulation Models (GCM), which in climatology are very useful in predicting climate for the next few decades. GCM data is generally ill-conditioned, which is high dimension and multicollinearity, so a special technique is needed to handle this poorly conditioned. One of the variable selection techniques and handling of multicollinearity which is currently highly developed is regularization techniques including Adaptive Lasso, where selective parameters are adaptive, which can differ for each regression coefficient. Until now, predictions of extreme rainfall in Indonesia have not used Adaptive Lasso in SD modeling. This paper aims to predict the amount of rainfall (in millimeters) at moderate extreme (quantile 0.75) and high extreme rainfall (quantile 0.9 da 0.95) and handling poorly conditioned GCM data with Adaptive Lasso techniques and building predictive models of local rainfall by utilizing GCM data using the Bayes quantile regression model. Response in the form of monthly rainfall at Indramayu district, West Java Indonesia, and 49 explanatory variables in the form of GCM precipitation data in the period January 1981 - December 2013, which handled multicollinearity and variable selection using the Adaptive Lasso. The results are very satisfying with correlation between predicted and real data is above 0.91 for and the RMSEP is less than 50. [ABSTRACT FROM AUTHOR]

Highlights • First attempt to apply concept of corporate memory to study strategic planning. • Original empirical study on reclamation for a small open economy. • Original neo-institutional economic inquiry of reclamation. Abstract Informed by property rights economics, this paper explores some unique advantages of reclamation, as a form of expansionist dynamic zoning, compared to densification of existing built up urban land and conversion of rural and peri-urban land. One major argument is that modern reclamation package, in light of global circulation of capital, can be one comprehensive project that avoids old mistakes and tries out new ideas without upsetting pre-existing development or interests. It then explains, with the help of statistics collected from a historical study of land reclamation of Hong Kong, that the future urban development in this open economy will be arrested if she gives up reclamation. The loss of corporate memory, using civil service staff movements based on the Staff List, seldom used in Hong Kong studies on town planning, as a proxy, in strategic planning is advanced as a factor behind the current situation. [ABSTRACT FROM AUTHOR]

Climate change presents major uncertainties in future safe water access especially in rural communities that are already faced with water insecurity. For Uganda, rainwater harvesting has become a major adaptation strategy to ensure the attainment of safe water access for all. However, climate change effects on rainwater harvesting potential remains largely unexplored. Therefore, this paper assesses climate change effects on water savings and water security from rainwater harvesting systems for Kabarole district, Uganda. The top performing Global circulation models (GCMs), used in this study included; MIROC5, BCC-CSM-1-M, CNRM-CM5, ACCESS1-0, HADGEM2-ES and HADGEM2-CC using projections for 2025–2055 (2040s) and 2060–2090 (2070s) periods. Seasonal analysis used the 4 seasons for Uganda, namely; the 2 dry seasons of December, January, February (DJF) and June, July, August (JJA) and the 2 rainy seasons of September, October, November (SON) and March, April, May (MAM). Although there are conflicting model predictions, generally, the models agree that water savings and security will reduce in DJF and MAM and increase in JJA and SON seasons. MAM rain season will be most affected with water savings and security projected to reduce by more than 50% in the 2070s period. Therefore, households should harness the increased water savings in JJA and SON to cater for the predicted reduction in water savings in MAM and DJF seasons especially for the 2070s period. A larger tank of 5 m 3 along with at least 50 m 2 roof size combined with a low cost household water treatment is thus recommended. [ABSTRACT FROM AUTHOR]

Regression problems with many potential predictors cannot be modeled using classical regression. In high dimensional data cases, predictors are much greater than its observations, so thus the penalized regression is better approach than classical regression in selecting predictors in the model. Group LASSO is one of penalized regression method which its predictors forming groups. Groups of predictors can be based on high correlation among predictors in a group, or can be based on its identically in shape, behavior or location. This paper performs rainfall data modeling using Group LASSO, with GLMSELECT procedure in SAS. The groups of predictors formed based on Principal Component Analysis. Rainfall data in Indramayu District, West Java, Indonesia as the response and 49 variables of Global Precipitation Climatology Project (GPCP) as its predictors. We have the best model for rainfall data based on the largest Adjusted R Square, minimum SBC and Root MSE. [ABSTRACT FROM AUTHOR]

Signage system is important to lead occupants to circulate as well as to evacuate people in emergency. Multiple exit signs leading to different directions at an intersection for general circulation may create confusion in people under emergency, and therefore can jeopardize the situation into a chaotic state, especially in buildings with a complex layout. Many past studies focused on implementing different techniques or designs to improve the existing signage system for better visual and navigating effects, such as the use of laser beams, the combination of audio and video cues, and the selection of color, location, and mounting height. How to better control the signage system under emergency has been overlooked. This paper proposed a BIM-controlled signage system to solve the problem. With the help of heat sensors that detect the fire prone areas in the building, the proposed system identifies the best route and activates the corresponding sign blinkers with high intensity. During this action, exit signs that do not lead to the best route become inactive. This system provides a natural interface for remote operation in facility management to efficiently guide people to the right exit under emergency. A prototype of the signage system is presented and future research directions are discussed. [ABSTRACT FROM AUTHOR]

Climate change poses a critical threat to future development, particularly in areas where poverty is widespread and key assets such as infrastructure are underdeveloped for even current needs. The focus of this study includes ten geographically and economically diverse countries and the impact of 54 distinct AR4 Global Circulation Model (GCM) scenarios of future climate change on their existing road networks. The analysis is completed using a software tool which uses engineering and materials-based stressor-response functions to determine the impact of climate on maintenance, repair and construction. This study represents an update to a previous study conducted by the authors in 2011. The key updates include methodological advances, policy-oriented results presentation and the use of a new software tool developed by the authors. For nine out of ten countries in the study, pro-active adaptation measures result in lower fiscal costs and higher connectivity rates as early as 2025. The results through 2100 are presented and the costs of climate change present clear findings for these countries in terms of road maintenance, construction, and adaptation policy. In rural areas, particularly those in low-income countries, roads represent a lifeline for economic and agricultural livelihood, as well as a number of indirect benefits including access to healthcare, education, credit, political participation, and more. Roads may be sparse through geographic locations, making each road critical. Extreme events pose a costly hazard to roads in terms of degradation, necessary maintenance, and potential decrease in lifespan due to climatic impacts. Climate change poses costly impacts in terms of maintenance, repairs and lost connectivity; yet many of these impacts can be mitigated and avoided by pro-active adaptation measures. It is a crucial consideration for protecting current and future infrastructure investments and the economic, social, and other functions they serve. The Infrastructure Planning Support System (IPSS) is a software tool designed to quantify the impacts of both extreme events and incremental climatic changes on road infrastructure in any geographic location throughout the world. The system identifies the financial cost on a yearly basis through 2100 and allows users to compare proactive adaptation measures and reactive non-adaptation measures. IPSS compares a ‘no climate change’ scenario as a baseline to provide information on the ‘regret’ that may occur if a predicted outcome of climate change model does not manifest as projected. Infrastructure impacts are determined based on civil engineering materials research, field studies of actual impacts on roads and buildings, and additional data. These resources are combined into stressor-response equations which are implemented to provide specific cost estimates. Additionally, the program can be customized to a specific location where data is available on stressor-response impacts on the infrastructure elements being analyzed. This paper focuses on the methodology and application of the IPSS tool to countries representing a range of incomes including low-income, middle, and upper income countries. The IPSS tool is used to compare costs of adaptation and opportunity cost for each country. The results indicate that higher income countries face significant dollar costs due to the extensive road networks, with very high costs in Japan and Italy, in particular. Bolivia, Ethiopia, and Cameroon all show extremely high advantages to adaptation, yet the costs required to simply maintain existing networks are equivalent to funding equal to doubling or tripling the existing paved road inventory. These results can help policy makers at the national and international levels decide where and how to invest; and show that climate change represents a significant and urgent threat to transportation throughout the world. [ABSTRACT FROM AUTHOR]

• 40-years datasets are used to train and validate different data-driven models. • The performance of different popular data-driven methods is compared. • The proposed GBDT method outperforms other data-driven methods. • Future electricity demand in Hong Kong is predicted by the GBDT model. • Future climatic and socio-economic uncertainties are considered in the model. Data-driven methods, such as artificial neural networks (ANNs), support vector regression (SVM), Gaussian process regression (GPR), multiple linear regression (MLR), decision trees (DTs), and gradient boosting decision trees (GBDTs), are the most popular and advanced methods for energy demand prediction. However, these methods have not been cross compared to analyze their performances for long-term energy demand predictions. Therefore, this paper aims to identify the best method among these data-driven methods for quantifying the impacts of climatic and socioeconomic changes on future long-term monthly electricity demand in Hong Kong. First, historical 40-year climatic, socioeconomic, and electricity consumption data are used to train and validate these models. Second, different representation concentration pathway (RCP) scenarios and three percentiles of 24 global circulation model outputs are adopted as future climatic changes, while five shared socioeconomic pathways are considered for future socioeconomic uncertainties. The results show that the GBDT method provides the best accuracy, generalization ability, and time-series stability, while ANN method exhibits the lowest accuracy and lower generalization ability. The monthly electricity demands in Hong Kong under the RCP8.5–2090 s scenario are predicted to increase by up to 89.40 % and 54.34 % in the residential and commercial sectors, respectively, when compared with 2018 levels. [ABSTRACT FROM AUTHOR]

The direct collisions that thermospheric neutral particles experience with convecting ionospheric ions is the primary way in which energy from the solar wind is deposited into the Earth's atmosphere. Because of these collisions, the neutral gas polewards of middle latitudes (∼ ± 4 0 ∘ geomagnetic) is often set into motion due to ion-drag, and Joule heated due to friction. Transient ionospheric phenomena, such as auroral arcs and sub-auroral polarisation streams (SAPS) also modify neutral winds in their own distinctive way. Thanks to the large spatial coverage of the Super Dual Auroral Radar Network (SuperDARN) at both middle and high latitudes, studies into ionosphere–thermosphere coupling have been enabled. These range from collaborative studies with neutral wind measurements from other instruments, to the driving electrodynamic input of various global circulation models. This paper aims to provide a general overview of the ion-neutral coupling research conducted utilising the SuperDARN, and to offer some directions as to where we believe future work should be directed. [ABSTRACT FROM AUTHOR]

• Bayesian statistics and clustering techniques for predictions of long-term urban water consumption are described. • The predictions are useful for the planning of water supply and support of sustainable urban development. • The impact of climate change on future urban water consumption is quantified. The proper planning of future urban water supply is essential to sustainable development. This paper answers two questions: What quantity of water will be needed in the long term? To what extent will water consumption be affected by climate change? We forecast water consumption using Bayesian statistics methods. A clustering analysis of observed daily water consumption and climate variables splits observations into base water use and seasonal water use, on the basis of the correlation between water consumption and air temperature. We show that the base water use is independent of climate change, but is subject to weekend effects. The seasonal water use depends on daily air temperature and total precipitation. Our forecast allows for uncertainties in climate variables and model parameters. The results from Bayesian linear regression give a probability distribution of daily water use. We obtained climate projections from multiple general circulation models and downscaled them for Greater Montreal. Bias corrections were made to the downscaled daily minimum temperature, maximum temperature and total precipitation. Using these corrected data as input to the Bayesian linear regression model, we forecast water consumption for the next three decades. The forecast results show a trend of increasing seasonal water use over time. [ABSTRACT FROM AUTHOR]