Satellite Modeling of Wildfire Susceptibility in California Using Artificial Neural Networking
Wildfires have become increasingly frequent and severe due to global climatic change, demanding improved methodologies for wildfire modeling. Traditionally, wildfire severities are assessed through post-event, in-situ measurements. However, developing a reliable wildfire susceptibility model has been difficult due to failures in accounting for the dynamic components of wildfires (e.g. excessive winds). This study examined the feasibility of employing satellite observation technology in conjunction with artificial neural networking to devise a wildfire susceptibility modeling technique for two regions in California. Timeframes of investigation were July 16 to August 24, 2017, and June 25 to December 8, 2017, for the Detwiler and Salmon August Complex wildfires, respectively. NASA’s MODIS imagery was utilized to compute NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), land surface temperature, net evapotranspiration, and elevation values. Neural network and linear regression modeling were then conducted between these variables and ∆NBR (Normalized Burn Ratio), a measure of wildfire burn severity. The neural network model generated from the Detwiler wildfire region was subsequently applied to the Salmon August Complex wildfire. Results suggest that a significant degree of variability in ∆NBR can be attributed to variation in the tested environmental factors. Neural networking also proved to be significantly superior in modeling accuracy as compared to the linear regression. Furthermore, the neural network model generated from the Detwiler data predicted ∆NBR for the Salmon August Complex with high accuracy, suggesting that if fires share similar environmental conditions, one fire’s model can be applied to others without the need for localized training.
Microfossil association of the Štíty locality
My thesis focuses on studying Cretaceous microfossil specimens from the excavation of former brickworks in Štíty, especially foraminifera. In the theoretical part, I have covered the structure of the Bohemian Cretaceous Basin area, especially Bystřice Lithofacial Development. I have also processed previous paleontological researches from the locality. Emphasis was placed on field research and subsequently on laboratory research of the site. I have examined the present state of the location and gathered samples of silt clay containing a wide variety of fossils. I have acquired the microfossils, determined them, and ordered them systematically. The most important part of the thesis is the systematic and palaeoecological processing of the collection of microfossils from the locality. The thesis continues the research of the last year of SOČ, where I have gathered a collection of fossil macrofauna, flora, and ichnofauna. My collection is supplemented mainly by benthic and planktonic foraminifers. I have confirmed that the specimens found are typical representatives of marine fauna belonging to the Upper Cretaceous Coniacian. The paleoecological characteristics of the locality correspond to a nutrient-rich shallow-water environment, occasionally disturbed by storm waves.
Removal of Nutrients by Chlorella Vulgaris Microalgae in Bandar Abbas Municipal Wastewater
The entry of nutrients into the environment can cause the creation of eutrophication of aquatic ecosystems. One of the methods of removing nutrients from effluents is the use of algae. Algal purification is a new and inexpensive technology for this purpose. The present study investigated the rate of cell growth and nutrient removal of urban wastewater in Bandar Abbas in winter 2020 by the Chlorella vulgaris microalgae in the phycolab of Fisheries Research. Treatments with different dilutions (0%, 25%, 50% and 75%) were prepared; in addition, specific growth rate, cell density and removal efficiency of phosphate, nitrate, nitrite were examined during a 14 day period with initial constant density (1×10⁶ cells / ml ) of microalgae. The results indicated that 0% and 75% dilution had the highest and lowest cell densities (8.675×10⁶ and 56.633×10⁶), respectively; moreover, they had the specific growth rate (0.166 and 0.311). Furthermore, there was a significant difference between them (P≥ 0.05). The highest nitrate and nitrite removal efficiencies were -40.75 and -79.84 in effluent dilution of 50%; in addition, the lowest were 1.26 and -40.26 in dilution of 75% and 25% respectively. Phosphate had the highest removal efficiency at 0% dilution with a mean of -79.65 that showed a significant difference with the lowest at 25% dilution (P≥ 0.05). Therefore, high or low levels of nutrients can affect the removal efficiency and growth rate of microalgae.