Forecasting of flash flood susceptibility mapping using random forest regression model and geographic information systems.

Flash floods, rapid and devastating inundations of water, are increasingly linked to the intensifying effects of climate change, posing significant challenges for both vulnerable communities and sustainable environmental management. The primary goal of this research is to investigate and predict a Flood Susceptibility Map (FSM) for the Ibaraki prefecture in Japan. This research utilizes a Random Forest (RF) regression model and GIS, incorporating 11 environmental variables (involving elevation, slope, aspect, distance to stream, distance to river, distance to road, land cover, topographic wetness index, stream power index, and plan and profile curvature), alongside a dataset comprising 224 instances of flooded and non-flooded locations. The data was randomly classified into a 70 % training set for model development, with the remaining 30 % used for model validation through Receiver Operating Characteristics (ROC) curve analysis. The resulting map indicated that approximately two-thirds of the prefecture as exhibiting low to very low flood susceptibility, while approximately one-fifth of the region is categorized as high to very high flood susceptibility. Furthermore, the RF model achieved a noteworthy validation with an area under the ROC curve of 99.56 %. Ultimately, this FSM serves as a crucial tool for policymakers in guiding appropriate spatial planning and flood mitigation strategies.

Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques.

Assessing water quality in arid regions is vital due to scarce resources, impacting health and sustainable management.This study examines groundwater quality in Assuit Governorate, Egypt, using Principal Component Analysis, GIS, and Machine Learning Techniques. Data from 217 wells across 12 parameters were analyzed, including TDS, EC, Cl-, Fe++, Ca++, Mg++, Na+, SO4--, Mn++, HCO3-, K+, and pH. The Water Quality Index (WQI) was calculated, and ArcGIS mapped its spatial distribution. Machine learning algorithms, including Ridge Regression, XGBoost, Decision Tree, Random Forest, and K-Nearest Neighbors, were used for predictive analysis. Higher concentrations of Na, K, Ca, Mg, Mn, and Fe were correlated with industrial and densely populated areas. Most samples exhibited excellent or good quality, with a small percentage unsuitable for consumption. Ridge Regression showed the lowest MAPE rates (0.22 % training, 0.26 % in testing). This research highlights the importance of advanced machine learning for sustainable groundwater management in arid regions. Thus, our results could provide valuable assistance to both national and local authorities involved in water management decisions, particularly for water resource managers and decision-makers. This information can aid in the development of regulations aimed at safeguarding and sustainably managing groundwater resources, which are essential for the overall prosperity of the country.

Assessment of psychological alarms and coping strategies of medical students with irritable bowel syndrome at Zagazig University: A cross-sectional study.

BACKGROUND: Irritable bowel syndrome (IBS) is one of the most prevalent functional gastrointestinal disorders. Medical students tend to report a higher prevalence of IBS since they are under constant stress. Many psychological difficulties are associated with IBS. To cope with IBS, individuals use various strategies which can impact the intensification or alleviation of IBS symptoms. The objective of this study was to assess the prevalence of IBS in medical students as well as psychological alarms and coping strategie employed by IBS sufferers. MATERIALS AND METHODS: We conducted a cross-sectional study from December 2022 to February 2023. Study participants were first to fifth year medical school students at Zagazig University, Egypt. Data were collected using a structured questionnaire comprising four sections: sociodemographic and clinical data; Rome IV criteria for the diagnosis of IBS; the alarm questionnaire for functional gastrointestinal disorders; and the Coping Strategies Questionnaire (CSQ24). Chi-square test or Fischer's exact test, as appropriate, were used to test for association. Binary logistic regression with a backward stepwise method was used to determine significant risk factors of negative coping with IBS. RESULTS: Of the studied 221 medical students, 38% had IBS. A statistically significant association was observed between IBS and the feeling of tension, anxiety, nervousness, depression, and frustration in the previous week, severe pain in the past 4 weeks, and the feeling that the bad situation would not get any better. Most of the students in the IBS group coped positively with stress, while 19.0% were negative in coping. Pain affecting the daily activities and the feelings of depression and frustration to the point of self-harm or suicide were the most significant correlates of IBS group's inability to cope. CONCLUSION: The prevalence of IBS in medical students at Zagazig University was 38%. We recommend psychological intervention and stress management programs to help medical students cope with IBS.