This study tried to demonstrate the role of time series models in modeling and forecasting process using long-term records of monthly mean sea level from January 1978 to October 2020 at Grand Isle, Louisiana. Following the Box-Jenkins methodology, the ARIMA(1,1,1)(2,0,0) 12 with drift model was selected to be the best fit model for the time series, according to its lowest AIC value. Using the LM algorithm, the results revealed that the NARNN model with 9 neurons in the hidden layer and 6 time delays provided the best performance in the nonlinear autoregressive neural network models at its smaller MSE value. The Mixed model, a combination of the SARIMA and NARNN models has both linear and nonlinear modelling capabilities can be a better choice for modelling the time series. The comparative results revealed that the Mixed-LM model with 9 neurons in the hidden layer and 3 time delays yielded higher accuracy than the NARNN-LM model with 9 neurons in the hidden layer and 6 time delays, and the ARIMA(1,1,1)(2,0,0) 12 with drift model, according to its lowest MSE in this study. Thus, this study may provide an integrated modelling approach as a decision-making supportive method for formulating local mean sea level forecast in advance. Understanding past sea level is important for the analysis of current and future sea level changes. In order to sustain these observations, research programs utilizing the resulting data should be able to improve significantly our understanding and narrow projections of future sea level rise and variability.

This study used scenario analysis to estimate the economic impact of the expenditures of anglers who participated in offshore fishing tournaments in Ocean City, Maryland. The primary purposes of this study were to evaluate impacts on the adjacent areas, to identify potential uses for economic impact information, and to provide justification for investment and support of programs related to the needs of coastal communities and issues related to marine recreational fisheries management. To accomplish this study, the IMPLAN modeling system was employed to calculate the economic impact of offshore fishing tournaments on local and regional economies. Preliminary results using scenario analysis suggested the total economic impact of offshore fishing tournaments in Ocean City could be nearly $2.2 million for Wicomico and Worchester counties. To get this estimate, three different scenarios were considered based on potential expenditure estimate of what each tournament party would spend on lodging, fuel, food, fishing and miscellaneous costs. Using IMPLAN, impact metric categories could be generated including general labor income, job opportunities-employment, and value added. This paper provides the extent to which fishery managers, municipalities, business and private sector stakeholders could benefit from local marine recreational fishing tournaments, and potentially justify investment in infrastructure which supports marine recreational fishing. Contribution/Originality: This study uses scenario economic impact analysis to estimate the economic impact of offshore fishing tournaments in Ocean City, Maryland. It provides the extent to fishery managers, municipalities, business and private sector stakeholders could benefit from local marine recreational fishing tournaments, and potentially justify investment in infrastructure which supports marine recreational fishing.

This study utilized cross-sectional data extracted from the 2013 National Saltwater Angler Survey, conducted by NOAA Fisheries Service, to examine saltwater recreational anglers' concerns to the threats of marine environment, identify groups exhibiting common patterns of responses, and examine the association between clusters of identified socio-demographic characteristics. The format of marine environmental threats in this study was composed of 13 Likert-scaled items scored from severe threat to not a threat at all. Concerns of marine environmental threats from these participants were examined through factor analysis which identified three reliable factors. Cluster analysis was used to identify three prominent clusters. Statistical tests were used to investigate the association between socio-demographic characteristics, including age, gender, income level, educational level, region of the respondent, and the identified factors and clusters. Results of this study may provide insight to understanding saltwater recreational anglers' concerns of marine environmental threats and could be an indicator of potential participation and behavior of saltwater recreational fishing projects. Contribution/Originality:This study focuses on trying to understand saltwater recreational anglers' perceptions on what they may consider a threat to the marine environment they interact in. This gives us the opportunity to receive some empirical insight on the groups' common response patterns. This insight can thus provide baseline information about what they may deem as a concerning factor towards marine environmental threats. In return, there is growth to take these results and apply them towards marine fisheries awareness programs and/or management campaigns that can improve the quality of marine life. There is not a lot of collected data on this particular group, whom may offer a different perspective on how marine life has changed over time.Through their expertise, their insight would be considered quality information which can be transmitted into collectable data.

Global price of soybeans has a big impact because of the trade war between the U.S. and China. Under this circumstance, price forecast is vital to facilitate efficient decisions and will play a major role in coordinating the supply and demand of soybeans globally. Hence, the primary purpose of this study was to demonstrate the role of time series models in predicting process using the time series data of monthly global price of soybeans from January 1990 to January 2021. The SARIMA and NARNN models are good at modelling linear and nonlinear problems for the time series, respectively. However, using the hybrid model, a combination of the SARIMA and NARNN models has both linear and nonlinear modelling capabilities, can be a better choice for modelling the time series. The comparative results revealed that the Hybrid-LM model with 8 neurons in the hidden layer and 3 time delays yielded higher accuracy than the NARNN-LM model with 8 neurons in the hidden layer and 3 time delays, and the SARIMA, ARIMA(0,1,3)(0,0,2)12, model,  according to its lowest MSE in this study. Thus, this study may provide an integrated modelling approach as a decision-making supportive method for formulating price forecast of soybeans for the global soybean market.

In order to understand forest composition, classifying forest cover type can help research regarding forest resilience, carbon sequestration, and climate change concerns. The purposes of this study were to develop and implement some image processing functions based on the histogram of forest cover type color image, and to classify forest cover type using its color feature sets of image pixels. Color-based image segmentation that is based on the color feature of image pixels assumes that homogeneous colors in the image correspond to separate clusters and hence meaningful objects in the image. The Image Processing Toolbox of MATLAB R2019a was used to convert the original forest cover type image to the enhance contrast image, including histogram of enhance contrast image. Furthermore, It was also used to analyze color-based forest cover type image segmentation using the enhance contrast image for this study. Using K-Means clustering analysis, a three-cluster solution was developed, labeled as Hardwoods (Yellow Color) Cover Type, Hardwoods (Gray Color) Cover Type, and Loblolly Pines Cover Type. There was a significant difference among three different forest cover type clusters in terms of histograms and L*a*b* color space features visually. Contribution/Originality: This study is one of very few studies which have classified forest cover type using its color feature sets of image pixels in order to understand forest composition. This study also addresses that K-Means clustering analysis can be utilized to develop and implement the classification of forest cover type image.