RESUMEN
In the realm of urban planning, the integration of deep learning technologies has emerged as a transformative force, promising to revolutionize the way cities are designed, managed, and optimized. This research embarks on a multifaceted exploration that combines the power of deep learning with Bayesian regularization techniques to enhance the performance and reliability of neural networks tailored for urban planning applications. Deep learning, characterized by its ability to extract complex patterns from vast urban datasets, has the potential to offer unprecedented insights into urban dynamics, transportation networks, and environmental sustainability. However, the complexity of these models often leads to challenges such as overfitting and limited interpretability. To address these issues, Bayesian regularization methods are employed to imbue neural networks with a principled framework that enhances generalization while quantifying predictive uncertainty. This research unfolds with the practical implementation of Bayesian regularization within neural networks, focusing on applications ranging from traffic prediction, urban infrastructure, data privacy, safety and security. By integrating Bayesian regularization, the aim is to, not only improve model performance in terms of accuracy and reliability but also to provide planners and decision-makers with probabilistic insights into the outcomes of various urban interventions. In tandem with quantitative assessments, graphical analysis is wielded as a crucial tool to visualize the inner workings of deep learning models in the context of urban planning. Through graphical representations, network visualizations, and decision boundary analysis, we uncover how Bayesian regularization influences neural network architecture and enhances interpretability.
RESUMEN
Water samples from the two underground rivers (Fenghuang River and Longju River) and samples of the dry and wet deposition of atmospheric dissolved inorganic nitrogen were taken from the Longfeng karst trough valley located in the Zhongliang mountain in the suburbs of Chongqing from May 2017 to April 2018. Anions, cations, δ15 N(NO3-), δ18 O(NO3-), δ18 O(H2O), and δ13C(DIC) isotope data were used to investigate the NO3- source and its environmental effects. The results showed:â The hydrochemistry of the two underground rivers is of the type HCO3-Ca. The NO3- concentration varied from 17.58 to 32.58 mg·L-1, with an average of 24.02 mg·L-1, and was slightly higher in rainy season than the dry season, revealing that the underground rivers were polluted. â¡ The δ15 N(NO3-) value ranged from -3.14 to 12.67, with an average value of 7.45. The δ18 O(NO3-) value ranged from -0.77 to 12.05 with an average value of 2.90, and was higher in the dry season than the rainy season, indicating that animal excreta and domestic sewage were main NO3- sources throughout the year. In addition, rainfall, fertilizer, and soil nitrogen were the NO3- sources during the rainy season. There are no significant differences between the NO3- sources of the two underground rivers, and nitrification is the main nitrogen conversion process. ⢠The molar ratio of (Ca2++Mg2+)/HCO3- varied from 0.65 to 0.82. That of the Fenghuang River was 0.75 and that of the Longju River was 0.70. The δ13C(DIC) value ranged from -12.46 to -9.20, with a mean of -11.10 in the Longju River and -10.72 in the Fenghuang River. These values indicated that the HNO3 derived from the nitrification of NH4+ was involved in the weathering of carbonate rocks. ⣠HNO3 dissolved carbonate rocks and aggravated the chemical weathering of carbonate rock in the basin, contributing 8% of the DIC in groundwater, and 9% and 7% in Fenghuang River and Longju River, respectively.
RESUMEN
In this study, we analyzed the stable hydrogen and oxygen isotopes of precipitation and three different land use patterns (cultivated land, grass land, and forest land) at 0-15 cm and 15-45 cm in a karst ridge-trough area (Zhongliang Mountain, Beibei District, Chongqing) in May 2017 and September 2017 to investigate the spatial and temporal variation of stable isotopes in different soil profiles using the isotope tracer technique. The results show that:â The average values of the soil water δD and δ18O are -50.0±33.6 and -7.9±4.3, respectively, and all plot around the local meteoric water line (LMWL), indicating that precipitation is the main source of the soil water supply in this area. â¡ The seasonal variations of δD and δ18O of the soil water are significant in different months of the rainy season, May (-19.4±6.8 and -4.1±1.0)>September (-82.2 ±14.0 and -11.9±2.2). ⢠However, there is no significant difference in the soil water δD and δ18O under different land use patterns. ⣠The soil water δD and δ18O change with soil depth gradients, which decrease along the depth in vertical direction for all types of soil land use in May but mainly increase/decrease in the cultivated land and woodland/grassland in September, respectively.