Using the soil and water assessment tool (SWAT), runoff in pervious and impervious urban areas was simulated in this study. In the meantime, as a novel application of machine learning, the emotional artificial neural network (EANN) model was employed to enhance the SWAT obtained for this study. As a result of the EANN model's capabilities in rainfall-runoff phenomena, the SWAT-EANN couple model has been used to assess urban flooding. The pervious, impervious, and water body areas of the study area were classified and mapped to estimate the cover change over three epochs. Land use map, precipitation data, temperature (minimum and maximum) data, wind speed, relative humidity, soil map, solar radiation, and digital elevation model were used as inputs for modelling rainfall-runoff of the study area in the ArcGIS environment. The accuracy assessment of this study was excellent (root-mean-square error 1 mm of precipitation). It also revealed that (a) a land use map illustrating changes in impervious, pervious surface, and water body for 1998, 2008, and 2018; (b) runoff modelling using a historical pattern of rainfall-runoff changes (1998-2018); and (c) descriptive statistical analysis of the runoff results of the research. This research will aid in urban planning, administration, and development. Specifically, it will prevent flooding and environmental problems.
This paper presents the global research landscape and scientific progress on occupant thermal comfort in naturally ventilated buildings (OTC-NVB). Despite the growing interest in the area, comprehensive papers on the current status and future developments on the topic are currently lacking. Hence, the publication trends, bibliometric analysis, and systematic literature review of the published documents on OTC-NVB were examined. The search query "Thermal Comfort" AND "Natural Ventilation" AND "Buildings" was designed and executed to recover related documents on the topic from the Elsevier Scopus database. Results showed that 976 documents (comprising articles, conference papers, reviews, etc.) were published on the topic from 1995 to 2021. Further analysis showed that 97.34% of the publications were published in the English language. Richard J.de Dear (University of Sydney, Australia) is the most prolific researcher on OTC-NVB research, while Energy and Buildings has the highest publications. Bibliometric analysis showed high publications, citations, keywords, and co-authorships among researchers, whereas the most occurrent keywords are ventilation, natural ventilation, thermal comfort, buildings, and air conditioning. Systematic literature review demonstrated that OTC-NVB research has progressed significantly from empirical to computer-based studies involving complex mathematical equations, programs, or software like artificial neural networks (ANN) and computational fluid dynamics (CFD). In general, OTC-NVB research findings indicate that physiological, social, and environmental factors considerably influence OTC in NVBs. Future studies will likely employ artificial intelligence or building performance simulation (BPS) tools to examine relationships between OTC and indoor air/environmental quality, human behavior, novel clothing, or building materials in NVBs.