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.

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, SO, Mn, HCO, K, and pH.

Integration of remote sensing data and in situ measurements to monitor the water quality of the Ismailia Canal, Nile Delta, Egypt.

The Ismailia Canal is one of the most important tributaries of the River Nile in Egypt. It is threatened by extinction from several sources of pollution, in addition to the intersection and nearness of the canal path with the Bilbayes drain and the effluent from the two largest conventional wastewater treatment plants in Greater Cairo. In this study, the integration of remote sensing and geospatial information system techniques is carried out to enhance the contribution of satellite data in water quality management in the Ismailia Canal.

Preparation and Optimization of Fast-Disintegrating Tablet Containing Naratriptan Hydrochloride Using D-Optimal Mixture Design.

Optimization of a lyophilized fast-disintegrating tablet (LFDT) formulation containing naratriptan hydrochloride, an antimigraine drug, was the foremost objective of the study, aiming in achieving fast headache pain relief. The Design-Expert® v10 software was used to generate formulations using D-optimal mixture design with four components: gelatin (X), hydrolyzed gelatin (X), glycine (X), and mannitol (X) of total solid material (TSM) w/w. The effect of the relative proportion of each component was determined on friability (Y), hardness (Y), and in vitro disintegration time (Y), which was then applied for formulation optimization.

Assessing the hydrogeochemical processes affecting groundwater pollution in arid areas using an integration of geochemical equilibrium and multivariate statistical techniques.

Rapid economic expansion poses serious problems for groundwater resources in arid areas, which typically have high rates of groundwater depletion. In this study, integration of hydrochemical investigations involving chemical and statistical analyses are conducted to assess the factors controlling hydrochemistry and potential pollution in an arid region. Fifty-four groundwater samples were collected from the Dhurma aquifer in Saudi Arabia, and twenty-one physicochemical variables were examined for each sample.