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Chapter 5 makes an attempt to predict trends in average land temperature due to CO2 emissions from all the non-renewable energy resources like “coal”, “oil”, “natural gas”, and “flaring”. The chapter illustrates the step-by-step process of time series analysis that is used to forecast temperature values. Predictions with the help of a machine learning model reveal that if no significant and immediate steps are implemented, emissions from these sources of energy will continue to rise in the...

To ensure continued food security and economic development in Africa, it is very important to address and adapt to climate change. Excessive dependence on rainfed agricultural production makes Africa more vulnerable to climate change effects. Weather information and services are essential for farmers to more effectively survive the increasing occurrence of extreme weather events due to climate change. Weather information is important for resource management in agricultural production and...

Climate change and other environmental disturbances are causing sea level rise all over the world. Due to sea-level rise and other unprecedented atmospheric phenomena caused by climate change, Indian coasts are vulnerable to coastal erosion. The Kerala coast, at the southern tip of India's west coast, has experienced a sea change in the last decade. To address coastal variations, we investigate coastal erosion, coastal accretion, and shoreline changes (from 2006 to 2020) along this coast...

Evolutionary algorithms and allied fields are getting more visibility as well as familiarity due to their numerous flexibilities such as handling high-dimensional non-linear problems and more. This book will help budding researchers to formulate their research problems, and comprises 10 chapters: three on optimization, five on machine learning algorithms, one on Internet of Things, and one on remote sensing.In Focus–a book series that showcases the latest accomplishments in water research....

The occupants' presence, activities, and behaviour can significantly impact the building's performance and energy efficiency. Currently, heating, ventilation, and airconditioning (HVAC) systems are often run based on assumed occupancy levels and fixed schedules, or manually set by occupants based on their comfort needs. However, the unpredictability and variability of occupancy patterns can lead to over/under the conditioning of space when using such approaches, affecting indoor air quality...

The occupants' presence, activities, and behaviour can significantly impact the building's performance and energy efficiency. Currently, heating, ventilation, and airconditioning (HVAC) systems are often run based on assumed occupancy levels and fixed schedules, or manually set by occupants based on their comfort needs. However, the unpredictability and variability of occupancy patterns can lead to over/under the conditioning of space when using such approaches, affecting indoor air quality...

Personal thermal comfort models are crucial for the future of human-in-the-loop HVAC control in energy-efficient buildings. Individual comfort models, compared to average population responses, can provide the personalization required for successful control. In this work, we frame the thermal preference prediction task as a multivariate, multi-class classification problem and use deep learning and time-series-based approach for thermal preference prediction. We combine l1 regularization with...

Personal thermal comfort models are crucial for the future of human-in-the-loop HVAC control in energy-efficient buildings. Individual comfort models, compared to average population responses, can provide the personalization required for successful control. In this work, we frame the thermal preference prediction task as a multivariate, multi-class classification problem and use deep learning and time-series-based approach for thermal preference prediction. We combine l1 regularization with...

Climate change in India is one of the most alarming problems faced by our community. Due to adverse and sudden changes in climate in past few years, mankind is at threat. Various impacts of climate change include extreme heat, changing rainfall patterns, droughts, groundwater, glacier melt, sea-level rise, and many more. Machine Learning can be used to analyze and predict the graph of change using previous data and thus design a model which in the future can furthermore be used to catalyze...

Several relationships between air temperature and work performance have been published. We reanalysed the one developed in 2006 by Seppänen et al.; which is probably the best known. We found that even when significant, its prediction accuracy is very low (R2 = 0.05, MAE = 1.9%, RMSE = 3.1%). We consequently reviewed the literature and found 35 studies on the effects of temperature on office work performance. We used Seppänen et al.’s approach to normalise the data reported in these studies...

Climate change is already affecting health in populations around the world, threatening to undermine the past 50 years of global gains in public health. Health is not only affected by climate change via many causal pathways, but also by the emissions that drive climate change and their co-pollutants. Yet there has been relatively limited synthesis of key insights and trends at a global scale across fragmented disciplines. Compounding this, an exponentially increasing literature means that...

Housing markets are known to be affected by adverse environments (i.e., environmental air pollution incidents affect Indian urban residents). Urban atmosphere quality has changed extensively with PM2.5 and O3 becoming the primary atmosphere indicators of concern because of dense cities in recent years. There is a correlation between the air pollution of Amaravati with the housing market model. When estimating the housing market, the chapter makes use of the extended regression model together...

<b>Background</b> The global literature on the links between climate change and human health is large, increasing exponentially, and it is no longer feasible to collate and synthesise using traditional systematic evidence mapping approaches. We aimed to use machine learning methods to systematically synthesise an evidence base on climate change and human health. <br><b>Methods</b> We used supervised machine learning and other natural language processing methods (topic modelling and...

Climatic data archives, including grid-based remote-sensing and general circulation model (GCM) data, are used to identify future climate change trends. The performances of climate models vary in regions with spatio-temporal climatic heterogeneities because of uncertainties in model equations, anthropogenic forcing or climate variability. Hence, GCMs should be selected from climatically homogeneous zones. This study presents a framework for selecting GCMs and detecting future climate change...

Buildings consume about 40 % of globally-produced energy. A notable amount of this energy is used to provide sufficient comfort levels to the building occupants. Moreover, given recent increases in global temperatures as a result of climate change and the associated decrease in comfort levels, providing adequate comfort levels in indoor spaces has become increasingly important. However, striking a balance between reducing building energy use and providing adequate comfort levels is a...

Abstract Catchment scale conceptual hydrological models apply calibration parameters entirely based on observed historical data in the climate change impact assessment. The study used the most advanced machine learning algorithms based on Ensemble Regression and Random Forest models to develop dynamically calibrated factors which can form as a basis for the analysis of hydrological responses under climate change. The Random Forest algorithm was identified as a robust method to model the...