Corona virus (COVID-19) infection has been growing as a biggest threat to human society. World Health Organization (WHO) has already declared it as a pandemic for the whole world, with nearly six million positive cases. Highly contagious nature of the virus has challenged the medical facilities of all the developed and developing country health system. Early identification of the infection is very important to provide medical facilities and cease the chain of infection to new persons. The symptoms such as fever, dry cough, breathing issues generally show in patients not before 5-7 days. However, the patients feel the loss of smell or taste (anosmia) as early as second day onwards due to the presence of virus in nose and throat. Low-cost techniques such as SniffIn-sticks ® Smell Test and UPSIT etc. can be used to test anosmia along with medically approved olfactory test leading to identification of COVID-19 infections. With leading researchers findings anosmia test will be effective in breaking the chain infection of COVID-19 virus. In the exit ports, anosmia test kits may be added to thermal testing to identify the infected patients with low symptoms. Additionally, home test kits may be developed at low cost and supplied for large scale testing of the infection.
Assessing groundwater potential for sustainable resource management is critically important. In addressing this concern, this study aims to advance the field by developing an innovative approach for Groundwater potential zone (GWPZ) mapping using advanced techniques, such as FuzzyAHP, FuzzyDEMATEL, and Logistic regression (LR) models. GWPZ was carried out by integrating various primary factors, such as hydrologic, soil permeability, morphometric, terrain distribution, and anthropogenic influences, incorporating twenty-seven individual criteria using multi-criteria decision models along with a hybrid approach for the Subarnarekha River basin, India, in Google earth engine (GEE). The predictive capability of the model was evaluated using a Multi-Collinearity test (VIF <10.0), followed by applying a random forest model, considering the weighted impact of the five primary factors. The hybrid model for GWPZ classification showed that 21.97 % (4256.3 km2) of the area exhibited very high potential, while 11.37 % (2202.1 km2) indicated very low potential for GW in this area. Validation of the groundwater level data from 72 observation wells, performed by the Area under receiver operating characteristic (AUROC) curve technique, yielded values ranging between 75 % and 78 % for different models, underscoring the robust predictability of GWPZ. The hybrid and LR-FuzzyAHP models demonstrated remarkable effectiveness in GWPZ mapping, indicating that the downstream and southern regions boast substantial groundwater potential attributed to alluvial soil and favorable recharge conditions. Conversely, the central part grapples with a scarcity of groundwater. It holds the potential to assist planners and managers in formulating strategies for managing groundwater levels and alleviating the impacts of future droughts.