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Articles 1 - 5 of 5
Full-Text Articles in Entire DC Network
Machine Learning For Modeling Wildfire Susceptibility At The State Level: An Example From Arkansas, Usa, Abdullah Al Saim, Mohamed H. Aly
Machine Learning For Modeling Wildfire Susceptibility At The State Level: An Example From Arkansas, Usa, Abdullah Al Saim, Mohamed H. Aly
Geosciences Faculty Publications and Presentations
Fire susceptibility modeling is crucial for sustaining and managing forests among many other valuable land resources. With 56% of its area covered by forests, Arkansas is known as the "natural state". About 1000 wildfires occurred and burned more than 10,000 acres each year during 1981-2018. In this paper, we use remote-sensing-based machine learning methods to address the natural and anthropogenic factors influencing wildfires and model fire susceptibility in Arkansas. Among the 15 explored variables, potential evapotranspiration, soil moisture, Palmer drought severity index, and dry season precipitation were recognized as the most significant factors contributing to the fire density. The obtained …
Machine Learning & Big Data Analyses For Wildfire & Air Pollution Incorporating Gis & Google Earth Engine, Abdullah Al Saim
Machine Learning & Big Data Analyses For Wildfire & Air Pollution Incorporating Gis & Google Earth Engine, Abdullah Al Saim
Graduate Theses and Dissertations
The climatic condition, the vegetation type, and the landscape of the United States have made it susceptible to wildfires. This research is divided into two parts based on the analysis of two different aspects of wildfires of two distinct regions. The first part of the study investigates the wildfire susceptibility in Arkansas. Arkansas is a natural state, and it is heavily dependent on its forest and agricultural resources. During the last 30 years, more than 1,000 wildfires occurred in Arkansas and caused more than 10,000 acres of burned areas. Therefore, identifying wildfire-susceptible areas is crucial for ensuring sustainable forest and …
A Geospatial Study Of The Drought Impact On Surface Water Reservoirs: Study Cases From Texas And California, Zachary Asbury
A Geospatial Study Of The Drought Impact On Surface Water Reservoirs: Study Cases From Texas And California, Zachary Asbury
Graduate Theses and Dissertations
Drought in Texas and California has been a long-term problem. Over the past 60 years reservoir construction has occurred to remedy the situation. Satellite imagery has been used historically to measure and monitor fluctuations in surface water reservoirs. This investigation integrates remote sensing and geographic information system (GIS) technologies to study the impact of drought on selected surface water reservoirs in San Angelo and Dallas in Texas, and Lake Oroville in California. Expansion and shrinkage over the 2005-2016 period reveal the concrete impact that drought, along with other factors, have on the selected lakes. Fluctuations in reservoir sizes during summer …
Adaptive Controller Using Runtime Partial Hardware Reconfiguration For Unmanned Aerial Vehicles (Uavs), Nikhil Thomas
Adaptive Controller Using Runtime Partial Hardware Reconfiguration For Unmanned Aerial Vehicles (Uavs), Nikhil Thomas
Graduate Theses and Dissertations
The goal of this thesis is to explore the feasibility of a multirotor controller system which can dynamically change the arm configuration of a multirotor. Currently most of the multirotor systems have to be powered down, rewired, and programmed with new firmware, to configure how many arms/motors they use to fly. The focus of our effort is to develop a Field Programmable Gate Array (FPGA) based hardware/software controller which uses dynamic partial hardware reconfiguration to switch the arm/motor configuration of a multirotor during operation. We believe that this will make a multirotor more fault tolerant and adaptive. This thesis explains …
Landscape Epidemiology And Machine Learning: A Geospatial Approach To Modeling West Nile Virus Risk In The United States, Sean Gregory Young
Landscape Epidemiology And Machine Learning: A Geospatial Approach To Modeling West Nile Virus Risk In The United States, Sean Gregory Young
Graduate Theses and Dissertations
The complex interactions between human health and the physical landscape and environment have been recognized, if not fully understood, since the ancient Greeks. Landscape epidemiology, sometimes called spatial epidemiology, is a sub-discipline of medical geography that uses environmental conditions as explanatory variables in the study of disease or other health phenomena. This theory suggests that pathogenic organisms (whether germs or larger vector and host species) are subject to environmental conditions that can be observed on the landscape, and by identifying where such organisms are likely to exist, areas at greatest risk of the disease can be derived. Machine learning is …