Effectively controlling the heating load (HL) in residential buildings is a vital component of energy conservation and sustainability. This abstract presents a new methodology for predicting HL by incorporating Gaussi...
详细信息
Effectively controlling the heating load (HL) in residential buildings is a vital component of energy conservation and sustainability. This abstract presents a new methodology for predicting HL by incorporating Gaussian Process Regression (GPR) and harnessing the power of two groundbreaking optimization techniques: the Population-based Vortex Search Algorithm (PVS) and the flow direction algorithms (FDA). GPR stands out as a robust machine learning algorithm renowned for its capacity to grasp intricate data relationships. Combining these mentioned optimizers with the GPR model results in a hybrid strategy that harnesses the unique advantages of each element. PVS and FDA are utilized to optimize the GPR's parameters, thereby elevating its predictive precision. The amalgamation of GPR, PVS, and FDA surpasses conventional techniques and even standalone GPR models regarding predictive precision and convergence velocity. This methodology offers a pragmatic and efficient approach to enhancing the forecast of HL in residential buildings, consequently aiding in better energy management and mitigating environmental impact. The hybrid GPPV model distinguishes itself with its exceptional accuracy when compared to alternative proposed models. Boasting a low RMSE of 1.013 and a R2 value of 0.990, GPPV attains the highest performance level. Furthermore, this research paves the way for the exploration of employing nature-inspired optimization techniques alongside neural networks to address a wide array of intricate challenges. The combined influence of GPR and these inventive optimizers highlights the capacity of hybrid models to tackle practical, real-world issues.
Appropriate management of our natural resources requires constant improvement and update of natural resource inventories. Remote sensing data and techniques offer an effective way to map and estimate changes in our cu...
详细信息
Appropriate management of our natural resources requires constant improvement and update of natural resource inventories. Remote sensing data and techniques offer an effective way to map and estimate changes in our current natural resources. The research presented in this dissertation will demonstrate state-of-the art remote sensing based methods for mapping natural and man-made features, including wetlands, general land cover, and building footprints. High resolution remotely sensed data used in this research included: lidar (light detection and ranging) data (low and high lidar posting density) and multispectral (NIR, blue, green and red bands) leaf-off aerial imagery. This research examined high resolution lidar data through the evaluation of various lidar posting densities and their influence on the accuracy of building footprints and DEMs. The lidar DEM analysis was extended by creating a Compound Topographic Index (CTI) from the DEM to evaluate the potential of the CTI's information for identifying wetland's location. Finally, the results from the second chapter were integrated into the third chapter by combining CTI, high resolution imagery, Digital Surface Model (DSM) and lidar intensity for mapping four land cover classes, including: wetlands, urban, agricultural and forest. A state-of-the-art remote sensing technique known as Object-Based Image Analysis (OBIA) was used to integrate lidar derived products and high resolution imagery. Results and findings of this research are important in two ways: First, advancing the understanding of lidar and lidar derivatives for mapping natural and manmade landscape features. Second, providing needed information to the scientific and civilian community, particularly in the state of Minnesota, to help with the process of updating wetland inventories such as the NWI and increasing the accuracy of mapping wetlands efforts with state-of-the-art techniques.
[ 1] Terrain attributes based on upslope contributing area, A, are used widely in distributed hydrologic models. Several grid-based algorithms are available for estimating A. In this study, five algorithms (D8, rho 8,...
详细信息
[ 1] Terrain attributes based on upslope contributing area, A, are used widely in distributed hydrologic models. Several grid-based algorithms are available for estimating A. In this study, five algorithms (D8, rho 8, MFD, DEMON, and D infinity) were compared quantitatively on two undulating agricultural fields ( 63 and 109 ha) in northeastern Colorado. Global positioning system (GPS) data (0.02-m accuracy) were used to generate grid digital elevation models (DEMs) at 5-, 10-, and 30-m cell sizes. Relative differences between A values estimated using single- and multiple-directionalgorithms increased with decreasing grid cell size. Relative differences were greatest along ridges and side slopes, and differences decreased where the terrain became more convergent. Multiple-directionalgorithms ( MFD, DEMON, and D infinity), allowing for flow divergence, are recommended on these undulating terrains for 5- and 10-m grids where A is most sensitive to the algorithm selection.
Specific catchment area (SCA) patterns are commonly computed on grids using flow direction algorithms that treat the flow as coming from a point source at the pixel centre. These algorithms are all ambiguous in the de...
详细信息
Specific catchment area (SCA) patterns are commonly computed on grids using flow direction algorithms that treat the flow as coming from a point source at the pixel centre. These algorithms are all ambiguous in the definition of the flow width to be associated with a pixel when computing the SCA. Different methods for computing the flow width have been suggested, without giving an objective reason. In the few cases where this issue has been specifically discussed, the flow width is derived from subjective analysis and incorrect conceptualizations. This paper evaluates alternative approaches for defining the flow width when computing SCA patterns using the D infinity and D8 algorithms, by comparing theoretical and computed SCA patterns on sloping planes, inward and outward cones. Two new methods of defining the flow width are also analysed for both the D infinity and D8 algorithms. The performances of the different methods are discussed in relation to two dimensionless parameters: (1) the global resolution, defined as the ratio of a characteristic length of the study area to the grid size and (2) the upslope area resolution, defined as the ratio of the theoretical SCA to the grid size. The optimal methods are identified by specific threshold values of these dimensionless parameters. We conclude that assuming the flow width invariant and equal to the grid size is generally the best approach in most practical circumstances, both for the D infinity and D8 algorithm. Copyright (c) 2005 John Wiley & Sons, Ltd.
暂无评论