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Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

Index insurance is promising to mitigate drought-related income losses in agriculture. To reduce the basis risk of index insurance, the integration of satellite data is of growing interest in research. The objective of this study is to obtain preliminary evidence regarding farmers' perceived usefulness (PU) of satellite-based index insurance.

By modifying the transtheoretical model of change to a transtheoretical model of PU, German farmers' gradual PU of satellite-based index insurance was investigated.

The results show that the average farmer perceives satellite-based index insurance as useful. It can be particularly seen that a higher level of education in an agricultural context as well as higher trust in index insurance products increases farmers' gradual PU. Moreover, higher relative weather-related income losses increase farmers' gradual PU.

It is recommended to apply latent variables when conducting future investigations regarding farmers' PU.

To the best of the authors' knowledge, this is the first study to explore farmers' PU of upcoming satellite-based index insurance by modifying and applying the transtheoretical model in a new way.

This paper aims to propose a method of the safety boundary protection for unmanned aerial vehicles (UAVs) in the icing conditions.

Forty icing conditions were sampled in the continuous maximum icing conditions in the Appendix C of the Federal Aviation Regulation Part 25. Icing numerical simulations were carried out for the 40 samples and the anti-icing thermal load distribution in full evaporation mode were obtained. Based on the obtained anti-icing thermal load distribution, the surrogated model of the anti-icing thermal load distribution was established with proper orthogonal decomposition and Kriging interpolation. The weather research and forecasting (WRF) model was used for meteorological simulations to obtain the icing meteorological conditions in the target area. With the obtained icing conditions and surrogated model, the anti-icing thermal load distribution in the target area and the variation with time can be determined. According to the energy supply of the UAVs, the graded safety boundaries can be obtained.

The surrogated model can predict the effects of five factors, such as temperature, velocity, pressure, median volume diameter (MVD) and liquid water content (LWC), on the anti-icing thermal load quickly and accurately. The simulated results of the WRF mode agree well with the observed results. The method can obtain the graded safety boundaries.

The method has a reference significant for the safety of the UAVs with the limited energy supply in the icing conditions.

This paper aims to Solar photovoltaic (PV) power can significantly impact the power system because of its intermittent nature. Hence, an accurate solar PV power forecasting model is required for appropriate power system planning.

In this paper, a long short-term memory (LSTM)-based double deep Q-learning (DDQL) neural network (NN) is proposed for forecasting solar PV power indirectly over the long-term horizon. The past solar irradiance, temperature and wind speed are used for forecasting the solar PV power for a place using the proposed forecasting model.

The LSTM-based DDQL NN reduces over- and underestimation and avoids gradient vanishing. Thus, the proposed model improves the forecasting accuracy of solar PV power using deep learning techniques (DLTs). In addition, the proposed model requires less training time and forecasts solar PV power with improved stability.

The proposed model is trained and validated for several places with different climatic patterns and seasons. The proposed model is also tested for a place with a temperate climatic pattern by constructing an experimental solar PV system. The training, validation and testing results have confirmed the practicality of the proposed solar PV power forecasting model using LSTM-based DDQL NN.

The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

The purpose of this paper is to elicit the expectations for resilient post-disaster rebuilds from Caribbean project end-users. In anticipation of future climatological, meteorological, hydrological or geophysical disasters disaster, key stakeholders can articulate and incorporate strategies for resilience development, thus leading to improved end-users’ satisfaction and confidence.

This paper engages the results of a systematic literature review that identified 24 empirical resilience factors for post-disaster reconstruction projects. These factors informed a semi-structured questionnaire to elicit the perspectives of Caribbean end-users on a seven-point Likert scale. The quantitative analysis of both factor ranking and principal component analysis was performed to identify correlations and provides further interpretations on the desires of the end-users for resilient rebuilds.

The results presented in this paper highlight the collective perspectives on the Caribbean end-users on what they perceived to be aiding more resilient reconstruction projects. They identified reconstruction designs mindful of future hazards, policies that aid climate change mitigation, active assessment of key structures, readily available funding sources and ensuring stakeholder’s unbiased interest as the top-most empirical factors. Factor analysis suggested collaborations with inclusive training and multi-stakeholder engagement, critical infrastructure indexing and effective governance as the critical resilience development factors.

To the best of the authors’ knowledge, this paper is first of its kind to explore the perspective of the Caribbean people regarding disaster reconstruction projects. It addresses developmental avenues for measurement indicators towards resilience monitoring and improvement. Additionally, the perspectives can provide construction industry professionals with tools for improved operational resilience objective-setting guidance, for Caribbean construction.

Comfortable outdoor workspaces are important for employees in business parks and urban areas. Prioritizing a pleasant thermal environment is essential for employee productivity, as well as the improvement of outdoor spaces between office buildings to enhance social activities and quality of outdoor workplaces in a hot arid climate has been subjected to very little studies Thus, this study focuses on business parks (BPs) landscape elements. The objective of this study is to enhance the user's thermal comfort in the work environment, especially in the outdoors attached to the administrative and office buildings such as the BPs.

This research follows Four-phases methodology. Phase 1 is the investigation of the literature review including the Concept and consideration of BP urban planning, Achieving outdoor thermal comfort (OTC) and shading elements analysis. Phase 2 is the case study initial analysis targeting for prioritizing zones for shading involves three main methods: social assessment, geometrical assessment and environmental assessment. Phase 3 entails selecting shading elements that are suitable for the zones requiring shading parametrize the selected shading elements. Phase 4 focuses on the optimization of OTC through shading arrangements for the prioritized zones.

Shading design is a multidimensional process that requires consideration of various factors, including social aspects, environmental impact and structural integrity. Shading elements in urban areas play a crucial role in mitigating heat stress by effectively shielding surfaces from solar radiation. The integration of parametric design and computational optimization techniques enhances the shading design process by generating a wide range of alternative solutions.

While conducting this research, it is important to acknowledge certain limitations that may affect the generalizability and scope of the findings. One significant limitation lies in the use of the shade audit method as a tool to prioritize zones for shading. Although the shade audit approach offers practical benefits for designers compared to using questionnaires, it may have its own inherent biases or may not capture the full complexity of human preferences and needs.

Few studies have focused on optimizing the type and location of devices that shade outdoor spaces. As a result, there is no consensus on the workflow that should regulate the design of outdoor shading installations in terms of microclimate and human thermal comfort, therefore testing parametric shading scenarios for open spaces between office buildings to increase the benefit of the outer environment is very important. The study synthesizes OTC strategies by filling the research gap through the implementation of a proper workflow that utilizes parametric thermal comfort.

The purpose of this study is to evaluate whether a diversification by grape varieties may help wine producers reduce uncertainty in quantity and quality variations due to increasingly erratic climate conditions.

This study hand-collects granular quantity and quality data from wine harvest reports for vintages 2003 to 2017 for the Valais region in Switzerland. The data allows us to obtain detailed data on harvested kilograms/liters and Oechsle/Brix degrees. It is then merged with precise meteorological data over the same sample period. The authors use this data set to capture weather conditions and their impact on harvested quantities and quality. Finally, they build portfolios including different grape varieties to evaluate whether this reduces variations in quality and quantity over vintages.

The findings highlight that the weather varies relatively strongly over the sample period and that climate hazards such as hail, frost or ensuing vine diseases effectively occur. These strongly impact the harvested quantities but less the quality of the wine. The authors further show that planting different grape varieties allows for a significant reduction in the variation of harvested quantities over time and thus acts as a good solution against climate risk.

The effect of climate change on viticulture is becoming increasingly important and felt and bears real economic and social consequences. This study transposes portfolio diversification which is central to reducing risk in the finance industry, into the wine industry and shows that the same principle holds. The authors thus propose a novel idea on how to mitigate climate risk.

The purpose of this paper was to examine smallholder farmers’ perceptions of climate change risks, adaptation responses and the links between adaptation strategies and perceived/experienced climate change risks in South Gondar, Ethiopia.

This paper used a convergent mixed methods design, which enables us to concurrently collect quantitative and qualitative data. Survey data was collected from 352 households, stratified into Lay Gayint 138 (39%), Tach Gayint 117 (33%) and Simada district 97 (28%). A four-point Likert scale was used to produce a standardised risk perception index for 14 climate events. Moreover, using a one-way analysis of variance, statistical differences in selecting adaptation strategies between the three districts were measured. A post hoc analysis was also carried out to identify the source of the variation. The findings of this paper are supplemented by qualitative data gathered through focus group discussions and key informant interviews of households who were chosen at random.

The standardised climate change risk perception index suggests that persistent drought, delayed onset of rainfall, early termination of rainfall and food insecurity were the major potentially dangerous climate change risks perceived by households in the study area. In response to climate change risks, households used several adaptation strategies such as adjusting crop planting dates, crop diversification, terracing, tree planting, cultivating drought-tolerant crop varieties and off-farm activities. A Tukey’s post hoc test revealed a significant difference in off-farm activities, crop diversification and planting drought-tolerant crop types among the adaptation strategies in the study area between Lay Gayint and Simada districts (p < 0.05). This difference reconfirms that adaptation strategies are location-specific.

Although many studies are available on coping and adaptation strategies to climate change, this paper is one of the few studies focusing on the linkages between climate change risk perceptions and adaptation responses of households in the study area. The findings of this paper could be helpful for policymakers and development practitioners in designing locally specific, actual adaptation options that shape adaptation to recent and future climate change risks.