Seismic stratigraphy of the Mianwali and Bannu depressions,north-western Indus foreland basin

International Journal of Earth Sciences - Regional seismic reflection profiles, deep exploratory wells, and outcrop data have been used to study the structure and stratigraphic architecture of the...     

Hydroclimatological Perspective of the Kerala Flood of 2018

Flood is among the deadliest disasters in India, and the frequency of floods and extreme precipitation events is projected to increase under the warming climate. The frequency of floods in India varies geographically as some regions are more prone to floods than the others. The Kerala flood of 2018 caused enormous economic damage, affected millions of people, and resulted in the death of more than 400 people. Here we provide a hydroclimatological perspective on the Kerala flood of 2018. Using the observations and model simulations from the Variable Infiltration Capacity (VIC) model, we show that the 2018 extreme precipitation and runoff conditions that caused flooding were unprecedented in the record of the past 66 years (1951–2017). Our results show that mean monsoon precipitation has significantly declined while air temperature has significantly increased during 1951–2017 in Kerala. The drying and warming trends during the monsoon season resulted in a declined total runoff in large part of the state in the last 66 years. Apart from the mean hydroclimatic conditions, extreme precipitation, and extreme total runoff have also declined from 1951 to 2017. However, 1 and 2-day extreme precipitation and extreme runoff conditions in August 2018 exceeded substantially from the long-term 95^th percentiles recorded during 1951–2017. Since there is no increase in mean and extreme precipitation in Kerala over the last six decades, the extreme event during August 2018 is likely to be driven by anomalous atmospheric conditions due to climate variability rather anthropogenic climate warming. The severity of the Kerala flood of 2018 and the damage caused might be affected by several factors including land use/land cover change, antecedent hydrologic conditions, reservoir storage and operations, encroachment of flood plains, and other natural factors. The impacts of key drivers (anthropogenic and natural) on flood severity need to be established to improve our understanding of floods and associated damage.     

School vulnerability to disaster: examination of school closure,demographic, and exposure factors in Hurricane Ike’s wind swath

Damage and destruction to schools from climate-related disasters can have significant and lasting impacts on curriculum and educational programs, educational attainment, and future income-earning potential of affected students. As such, assessing the potential impact of hazards is crucial to the ability of individuals, households, and communities to respond to natural disasters, extreme events, and economic crises. Yet, few studies have focused on assessing the vulnerability of schools in coastal regions of the USA. Using Hurricane Ike’s tropical storm wind swath in the State of Texas as our study area, we: (1) assessed the spatial distribution patterns of school closures and (2) tested the relationship between school closure and vulnerability factors (namely physical exposure and school demographics) using zero-inflated negative binomial regression models. The regression results show that higher probabilities of hurricane strikes, more urbanized school districts, and school districts located in coastal counties on the right side of Ike’s path have significant positive associations with an increase in the number of school closure days. Socioeconomic characteristics were not significantly associated with the number of days closed, with the exception of proportion of Hispanic youth in schools, a result which is not supported by the social vulnerability literature. At a practical level, understanding how hurricanes may adversely impact schools is important for developing appropriate preparedness, mitigation, recovery, and adaptation strategies. For example, school districts on the right side of the hurricane track can plan in advance for potential damage and destruction. The ability of a community to respond to future natural disasters, extreme events, and economic crises depends in part on mitigating these adverse effects.     

2-D delineation of individual citrus trees from UAV-based dense photogrammetric surface models

One of the challenges of remote sensing and computer vision lies in the three-dimensional (3-D) reconstruction of individual trees by using automated methods through very high-resolution (VHR) data sets. However, a successful and complete 3-D reconstruction relies on precise delineation of the trees in two dimensions. In this paper, we present an original approach to detect and delineate citrus trees using unmanned aerial vehicles based on photogrammetric digital surface models (DSMs). The symmetry of the citrus trees in a DSM is handled by an orientation-based radial symmetry transform which is computed in a unique way. Next, we propose an efficient strategy to accurately build influence regions of each tree, and then we delineate individual citrus trees through active contours by taking into account the influence region of each canopy. We also present two efficient strategies to filter out erroneously detected canopy regions without having any height thresholds. Experiments are carried out on eight test DSMs composed of different types of citrus orchards with varying densities and canopy sizes. Extensive comparisons to the state-of-the-art approaches reveal that our proposed approach provides superior detection and delineation performances through supporting a nice balance between precision and recall measures.     

Effect of Drain Pipes on Uplift Force and Exit Hydraulic Gradient and the Design of Gravity Dams Using the Finite Element Method

Geotechnical and Geological Engineering - The effects of diameter and location of drain pipes on the uplift force and exit hydraulic gradient for a gravity dam are investigated. A numerical model...     

An effective stress-based DSC model for predicting the coefficient of lateral soil pressure in unsaturated soils

Acta Geotechnica - In this study, an analytical model is developed to establish a framework for predicting the coefficient of lateral soil pressure in unsaturated soils. To this end, the disturbed...     

3D Static Modeling and Petrographic Aspects of the Albian/Cenomanian Reservoir,Komombo Basin,Upper Egypt

Natural Resources Research - The Albian/Cenomanian reservoir is one of the two main reservoirs composing the petroleum system in the Komombo Basin. However, these reservoirs have not previously...     

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.