Wildfire susceptibility mapping (WSM) plays a crucial role in identifying areas with heightened vulnerability to forest fires, allowing for proactive measures in fire prevention, management, and resource allocation, ultimately leading to more effective fire control and mitigation strategies. This paper describes our undertaking to develop and compare the performance of two knowledge-based models, namely the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS), as well as two novel genetic algorithm (GA)-based ensemble data-driven models: boosting and random subspace. The objective was to map susceptibility to forest fires in the Northern Mazar District in Jordan. The ensemble models were constructed using four well-known classifiers: decision tree (DT), support vector machine (SVM), k-nearest neighbors (kNN), and naive Bayes (NB) algorithms. This study utilized seventy forest fire locations and twelve influential factors to build and evaluate the models. To identify the optimal features for constructing the data-driven models, a GA-based wrapper method and four machine learning models were applied. During the validation phase, the area under the receiver operating characteristic curve (AUROCC) values for the single SVM, single NB, single DT, single kNN, GA-based boosting, GA-based random subspace, FR-AHP, and AHP-TOPSIS models were found to be 85.3%, 85.9%, 73.8%, 88.7%, 95.0%, 95.0%, 74.0%, and 65.4% respectively. The results indicated that the GA-based ensemble models outperformed both the single machine learning models and the knowledge-based techniques in terms of performance. The developed models in this study can be effectively utilized in various management and decision-making processes aimed at mitigating forest fire risks and enhancing fire control strategies.

Cluster and Factor analysis were performed on 55 well representative groundwater samples taken from Water Authority open files and samples during 2011. A total of 12 water variables data including, Na, Ca, K, Mg, CI, HCO3, SO4 and NO3 beside to chemical analysis like TC, TDS and PH were analyzed. The study finds that there are 2 factor model variation : factor one include concentration of Na , k, Mg ,Cl , EC, TDS ,NO3 ,HCO3, Ca and SO4 ,where as factor 2 includes TC and PH . These 2 factor explains 79.8% of the total groundwater quality variation. A two main hydrochemical groups were demonstrated using Q- mode cluster analysis , the first group shows the similarity between, K, Mg, NO3 and Na ,which probably represent the effects of weathering of the rich feldspare and mica , in addition to agriculture fertilizers. The second cluster is Cl and HCO3 dominated but also contains low concentration of SO4.

Four threshold air pollutants (SO2, NO, NO2, and O3) in addition to meteorological parameters were monitored at the Campus of the Hashemite University (HU) for two years (1/1/2012 through 30/12/013). Correlations between air pollution and meteorological parameters were derived. The results showed that O3 has a positive correlation with air temperature, wind speed and wind direction, but has a negative correlation with the relative humidity (RH). SO2 was found to have a negative correlation with the RH and wind speed, but positive correlation with air temperature. NO has negative correlation with air temperature, RH, and wind speed. And finally, NO2 has a negative correlation with RH and wind speed, but it has positive correlation with air temperature. Justify the reasons in brief with recommendations to improve the air quality

This study is the first of its kind to investigate musculoskeletal injuries in Jordanian swimmers. It aimed to identify common sports injuries in male and female swimmers aged 18 to 25 in Jordan. The research involved 173 participants affiliated with the Jordanian Swimming Federation, using correlation and regression analysis. The study explored the intricate relationship between the age of starting swimming, training routines, and competitive involvement. It revealed negative correlations between the age of initiation and various performance measures, indicating that starting swimming at a younger age is associated with increased competition participation, more training time, and longer training distances. Furthermore, a strong negative correlation was observed between the number of competitions and weekly training time, suggesting that more competitions require more training. Conversely, a positive correlation was found between weekly training distance and training time, indicating that longer training sessions cover greater distances. It's important to note that these correlations do not imply causation, and other factors may contribute. Regression analysis revealed that the selected independent variables significantly influenced the prediction of swimming injuries, explaining around 37.3% of the variability. The number of competitions emerged as the most influential factor, negatively impacting injury prediction, meaning that more competitions were associated with fewer injuries. Additionally, variables like weekly training time, weekly training distance, and the age of starting swimming positively affected injury prediction. The study presented a predictive equation for estimating swimmers' injuries, providing valuable insights for injury prevention and athlete well-being.