Land use and land cover mapping using deep learning based segmentation approaches and VHR Worldview-3 images

Deep learning-based segmentation of very high-resolution (VHR) satellite images is a significant task providing valuable information for various geospatial applications, specifically for land use/land cover (LULC) mapping. The segmentation task becomes more challenging with the increasing number and complexity of LULC classes. In this research, we generated a new benchmark dataset from VHR Worldview-3 images for twelve distinct LULC classes of two different geographical locations. We evaluated the performance of different segmentation architectures and encoders to find the best design to create highly accurate LULC maps. Our results showed that the DeepLabv3+ architecture with an ResNeXt50 encoder achieved the best performance for different metric values with an IoU of 89.46%, an F-1 score of 94.35%, a precision of 94.25%, and a recall of 94.49%. This design could be used by other researchers for LULC mapping of similar classes from different satellite images or for different geographical regions. Moreover, our benchmark dataset can be used as a reference for implementing new segmentation models via supervised, semi- or weakly-supervised deep learning models. In addition, our model results can be used for transfer learning and generalizability of different methodologies

Comparative study of geo-statistical and multivariate models for air temperature interpolation in central and northern regions of Iran

Air temperature is one of the major variables required for agroclimatic classifications. For spatial zoning of temperature point observations, the interpolation approaches in which the horizontal and vertical gradients are included may be applied. In this research, the skill of Kriging, Co-Kriging, geographically weighted regression and Linear Multivariate Regression was evaluated for the interpolation of the monthly mean temperature values using the data of 56 synoptic stations located in the northern and central regions of Iran. The results of the statistical analysis indicated that the geographically weighted regression have the greatest difference with the other methods in month of December, with root mean square error (RMSE) equal to 0.83 °C, Based on the RMSE values of all months, the geographically regression method (with RMSE of 1.26°C) is the most suitable approach for temperature spatial zoning in this region. and then linear multiple regression method with RMSE of 2.24 °C, Kriging with RMSE of 2.52 °C and Cokriging with highest RMSE of 2.86 °C were ranked second to fourth, respectively. Besides, it is concluded that for high altitude areas where almost no weather station exist, the geographically weighted regression method provided the most accurate interpolated data of the air temperature

A New Model to Determine the Two-phase Drilling Fluid Behaviors through Horizontal Eccentric Annular Geometry, Part B: Frictional Pressure Losses Estimation

Drilling with aerated muds is becoming more often used in underbalanced drilling operations. One of the major challenges that has to be faced in such operations is the estimation of the physical behavior of aerated fluids inside the annulus. In this study, experiments have been conducted at METU Multiphase Flow Loop using air-water mixtures with various in-situ flow velocities of 0-120 and 0-10 ft/s, respectively. This study aims to develop a model to estimate the frictional pressure losses for two-phase flow through horizontal eccentric annular geometry. In order to estimate the frictional pressure losses, three different methods were developed: (i) definition of new friction factors by using experimental data; (ii) modification of Lockhart-Martinelli pressure loss correction factor; and (iii) modification of Beggs and Brill model by changing the equation constants. The comparison of the developed models with experimental data has shown that frictional pressure losses can be estimated with a reasonable accuracy

Pressure drop estimation in horizontal annuli for liquid-gas 2 phase flow: Comparison of mechanistic models and computational intelligence techniques

Frictional pressure loss calculations and estimating the performance of cuttings transport during underbalanced drilling operations are more difficult due to the characteristics of multi-phase fluid flow inside the wellbore. In directional or horizontal wellbores, such calculations are becoming more complicated due to the inclined wellbore sections, since gravitational force components are required to be considered properly. Even though there are numerous studies performed on pressure drop estimation for multiphase flow in inclined pipes, not as many studies have been conducted for multiphase flow in annular geometries with eccentricity. In this study, the frictional pressure losses are examined thoroughly for liquid-gas multiphase flow in horizontal eccentric annulus. Pressure drop measurements for different liquid and gas flow rates are recorded. Using the experimental data, a mechanistic model based on the modification of Lockhart and Martinelli [18] is developed. Additionally, 4 different computational intelligence techniques (nearest neighbor, regression trees, multilayer perceptron and Support Vector Machines - SVM) are modeled and developed for pressure drop estimation. The results indicate that both mechanistic model and computational intelligence techniques estimated the frictional pressure losses successfully for the given flow conditions, when compared with the experimental results. It is also noted that the computational intelligence techniques performed slightly better than the mechanistic model. (C) 2014 Elsevier Ltd. All rights reserved

Co-optimal PMU Placement for Complete Monitoring of Distributed Generations Installed System

Considering the substantial amount of advantageous side of distributed generations (DG), power system operators are opting it as efficient generations option to meet up the today’s huge power demand. This paper proposes a novel technique for placement of PMUs co-optimally for complete power system monitoring when distributed generations (DG) are installed in the system. Graph theory-based intellectual search technique has been used to identify the further locations of PMU optimally to achieve complete observability. This method is applied on IEEE standard test systems—14 bus as the first test case and 30 bus system as the second test case to test the usefulness of it. The proposed technique is simple and straightforward. The result shows better redundant measurement percentage