Assessing Site Selection of College Student Housing : Commuting Efficiency across Time

Universities around the world are promoting walking for their students because it provides many health and environmental benefits at the personal as well as the community level. This paper aims to help universities, city planners and housing investors in the process of efficient site selection for future student housing projects, by analyzing off-campus students’ commuting habits and travel time preferences to and from the university campuses. An online survey is operated to collect responses of students (n= 527) from two Jordanian universities located within the city of Irbid (N-Jordan). Results indicate that the mean value for students’ longest preferred one-way walking duration is 17.04± 8.25 minutes for the whole sample. A statistically significant negative correlation is found between students’ longest preferred one-way walking duration and age. The percentage of students who would accept this duration was represented in a formula in order to calculate the accumulated walking potential of varied sites around university campuses. The paper presented a local scenario using GIS mapping where this process was implemented to evaluate prospect vacant sites' walking potential around Yarmouk University, Irbid, Jordan

Assessment of Sentinel-2 and Landsat-8 OLI for Small-Scale Inland Water Quality Modeling and Monitoring Based on Handheld Hyperspectral Ground Truthing

This study investigates the best available methods for remote monitoring inland small-scale waterbodies, using remote sensing data from both Landsat-8 and Sentinel-2 satellites, utilizing a handheld hyperspectral device for ground truthing. Monitoring was conducted to evaluate water quality indicators: chlorophyll-a (Chl-a), colored dissolved organic matter (CDOM), and turbidity. Ground truthing was performed to select the most suitable atmospheric correction technique (ACT). Several ACT have been tested: dark spectrum fitting (DSF), dark object subtraction (DOS), atmospheric and topographic correction (ATCOR), and exponential extrapolation (EXP). Classical sampling was conducted first; then, the resulting concentrations were compared to those obtained using remote sensing analysis by the above-mentioned ACT. This research revealed that DOS and DSF achieved the best performance (an advantage ranging between 29% and 47%). Further, we demonstrated the appropriateness of the use of Sentinel-2 red and vegetation red edge reciprocal bands (1/(B4 X B6)) for estimating Chl-a (R2 = 0.82, RMSE = 14.52mg/m3). As for Landsat-8, red to near-infrared ratio (B4/B5) produced the best performing model (R2 = 0.71, RMSE = 39.88 mg/m3), but it did not perform as well as Sentinel-2. Regarding turbidity, the best model (with (R2 =0.85, RMSE = 0.87 NTU) obtained by Sentinel-2 utilized a single band (B4), while the best model (with R2 = 0.64, RMSE = 0.90 NTU) using Landsat-8 was performed by applying two bands (B1/B3). Mapping the water quality parameters using the best performance biooptical model showed the significant effect of the adjacent land on the boundary pixels compared to pixels of deeper water

Evaluation of the gulf of aqaba coastal water, Jordan

© 2020 by the authors. (1) Background: The Gulf of Aqaba (GoA) supports unique and diverse marine ecosystems. It is one of the highest anthropogenically impacted coasts in the Middle East region, where rapid human activities are likely to degrade these naturally diverse but stressed ecosystems. (2) Methods: Various water quality parameters were measured to assess the current status and conditions of GoA seawater including pH, total dissolved solids (TDS), total alkalinity (TA), Cl-, NO3-, SO42-, PO43-, NH4+, Ca2+, Mg2+, Na+, K+, Sr, Cd, Co, Cr, Cu, Fe, Mn, Pb, and Zn. (3) Results: The pH values indicated basic coastal waters. The elevated levels of TDS with an average of about 42 g/L indicated highly saline conditions. Relatively low levels of inorganic nutrients were observed consistent with the prevalence of oligotrophic conditions in GoA seawater. The concentrations of Ca2+, Mg2+, Na+, K+, Sr, Cl-, and SO42- in surface layer varied spatially from about 423-487, 2246-2356, 9542-12,647, 513-713, 9.2-10.4, 22,173-25,992, and 317-407 mg/L, respectively. The average levels of Cd, Co, Cr, Cu, Fe, Mn, Pb and Zn ranged from 0.51, 0.38, 1.44, 1.29, 0.88, 0.38, and 6.05 μg/L, respectively. (4) Conclusions: The prevailing saline conditions of high temperatures, high evaporation rates, the water stratification and intense dust storms are major contributing factors to the observed seawater chemistry. The surface distribution of water quality variables showed spatial variations with no specific patterns, except for metal contents which exhibited southward increasing trends, closed to the industrial complex. The vast majority of these quality parameters showed relatively higher values compared to those of other regions

PLANAR CONSTRAINTS FOR AN IMPROVED UAV-IMAGE-BASED DENSE POINT CLOUD GENERATION

In this paper, we proposed a new refinement procedure for the semi-global dense image matching. In order to remove outliers and improve the disparity image derived from the semi-global algorithm, both the local smoothness constraint and point cloud segments are utilized. Compared with current refinement technique, which usually assumes the correspondences between planar surfaces and 2D image segments, our proposed approach can effectively deal with object with both planar and curved surfaces. Meanwhile, since 3D point clouds contain more precise geometric information regarding to the reconstructed objects, the planar surfaces identified in our approach can be more accurate. In order to illustrate the feasibility of our approach, several experimental tests are conducted on both Middlebury test and real UAV-image datasets. The results demonstrate that our approach has a good performance on improving the quality of the derived dense image-based point cloud

Predictive decision making under risk and uncertainty: A support vector machines model

In this paper, a decision making model using support vector machine (SVM) approach is presented. Here, human attitude towards risk and uncertainty is identified via optimizing SVM certainty classification model. In particular, individuals are given different pairs of gambles in order to reveal their preference. Unlike traditional methods used to estimate the utility function through direct inquiry of the certainty equivalents, pair-wise comparisons are used here in the training process to predict human preferences and to compute the utility parameters. The presented study is characterized by first, the use of SVM in the field of decision making to classify individuals’ choices, second, it uses such model to search for the optimal utility parameters, third, the model can be used to guide the decision makers towards better decisions. In contrast to existing utility models, the SVM utility approach is characterized by its tolerance to misclassification in the training and testing data sets which makes it cope with the existing violations such as the common consequence, common ratio and violation of betweenness in the utility theory. To demonstrate the merits of the model, different data sets were used from well known literature studies and new conducted surveys that elicit individual preferences. The data is split into training and testing sets. The results demonstrated a notable consistency in the computed utility parameters and remarkable predictions without the need to strict certainty equivalent estimation. The model can be beneficial in predictive decision making under risk and uncertainty

Numerical Simulation of Coir Geotextile Reinforced Soil Under Cyclic Loading

Coir geotextile is a natural fiber derived from the husk of coconut, and its use as a reinforcement element has recently shown great potential for applications in geostructures. The biodegradable nature of the coir fibers makes coir geotextiles an environmentally friendly ground improvement alternative for use in various infrastructure projects. In the recent past, a number of laboratory investigations have been carried out on the behavior of coir geotextile reinforced soil during monotonic loading; however, only limited number of studies have been carried out on coir geotextile reinforced soil during cyclic loading. In this study, a robust finite element model has been developed to understand and investigate the behavior of coir geotextile reinforced soil during cyclic loading. It was observed that the inclusion of coir geotextiles increases the bearing capacity and reduces the settlement of soil during cyclic loading. The inclusion of coir geotextile in the soil creates a shear interface between the geotextile and soil, which helps in transferring the stress from the soil to the geotextile. The inclusion of coir geotextiles in the middle of the subbase layer yielded their optimum performance during cyclic loading

Newsvendor revisited: risk premiums of loss aversion

The classical newsvendor model in economics and decision theory treats losses and gains equally likely. However, decision makers are usually loss-averse as probable losses have more impact on humans than probable gains. This study presents a new variant of the newsvendor problem of loss-averse decision makers. The optimal order quantity is found by maximizing the expected utility of bounded functions. The implications of loss aversion on the certainty equivalents and risk premiums were also analyzed. Two case studies of exponential utility and normal demand were considered. A new elegant form of the optimal order quantity is established. The results show that when exponential loss aversion exists, the newsvendor optimal quantity serves as a lower(upper) bound on the optimal quantities. Moreover, high loss aversion entails higher RP. Similar findings hold by increasing the overage/underage costs and the demand standard deviation. Possible future extensions are demonstrated at the end of the paper

A GIS-based drastic model for assessing aquifer vulnerability in Amman-Zerqa groundwater basin, Jordan

Amman-Zerqa Basin (AZB) is a major basin in Jordan. The concentration of economic, agricultural and social activities within the basin makes it of prime importance to Jordan. Intensive agricultural practices are widespread and located close to groundwater wells, which pose imminent threats to these resources. Groundwater contamination is of particular concern as groundwater resources are the principal source of water for irrigation, drinking and industrial activities. A DRASTIC model integrated with, and GIS tool has been used to evaluate the groundwater vulnerability of AZB. The Drastic index map showed that only 1.2% of the basin’s total area of 3792 km2 lies in the no vulnerable zone and about 69% is classified as having low pollution potential. The results also revealed that about 30% of the catchment area is moderately susceptible to pollution potential and slightly 1% is potentially under high pollution risk. These results suggest that almost one third of the AZB is at moderate risk of pollution potential. These areas are mainly in the north-east and central parts of the basin where the physical factors like gentle slope and high water table well support the chances of getting shallow aquifer water polluted. Areas with high vulnerable pollution are mainly the central of Amman old city.Validerad; 2013; 20130314 (nadhir

Establishing Regional Power Sustainability and Feasibility Using Wind Farm Land-Use Optimization

Wind-farm planning optimization is important for decision-making concerning regional energy planning in developing countries. This process is governed by restrictions on site selection based on land suitability metric variables, wind turbine technology variables, and land-use governing criteria. This study aims to create a framework for land appropriation strategies for locating optimum sites suitable for wind farms. It is using Jordan as an Area of Interest (AOI), where the scope is to illustrate how this framework will employ wind turbine energy to positively enhance the national Gross Domestic Product (GDP). The methodology employs thirteen GIS thematic layers with a 250-m spatial resolution to substantiate how site-specific criteria, turbine type, and turbine hub height variables are determining factors in the optimal solution. This method involves selecting relevant factors, database construction, data layer generation and preparation, numerical ranking and weighting of each factor, and computation of the potential wind farm locations map by overlaying all the thematic GIS layers. The results showed that the establishment of wind farms would not only meet the AOI’s growing energy needs, rather exceed them to generating income for the developing nation. The results of the feasibility study will boost the national GDP by 3.4%; where, for example, one governorate alone could produce 274.3% of the total required national consumption at a turbine hub height of 50 m. The study attests to a valuable framework that can be implemented elsewhere to establish regional power sustainability and feasibility for other nations. The results show that an added land-use layer indicating the potential value of land in terms of its suitability for establishing wind farms should be considered in future sustainable regional planning studies when considering networks for smart cities, industrial cities, smart agriculture, and new agglomerations