Application of NRCS-curve number method for runoff estimation in a mountainous watershed

The major problem in the assessment of relationships between rainfall and runoff occurs when a study is carried out in ungauged watersheds in the absence of hydro-climatic data. This study aims to evaluate the applicability of Natural Resources Conservation Service-Curve Number (NRCS-CN) method together with GIS in estimating runoff depth in a mountainous watershed. The study was carried out in the semi-arid Kardeh watershed which lies between 36º 37´ 17˝ to 36º 58´ 25˝ N latitude and 59º 26´ 3˝ to 59º 37´ 17˝ E longitude, about 42 km north of Mashhad, Khorasan Razavi Province, Iran. The hydrologic soil groups, land use and slope maps were generated with GIS tools. The curve number values from NRCS Standard Tables were assigned to the intersected hydrologic soil groups and land use maps to generate CN values map. The curve number method was followed to estimate runoff depth for selected storm events in the watershed. Nash-Sutcliffe efficiency, pair-wise comparison by the t-test, Pearson correlation and percent error were used to assess the accuracy of estimated data and relationship between estimated and observed runoff depth. The results showed relatively low Nash-Sutcliffe efficiency (E = – 0.835). There was no significant difference between estimated and observed runoff depths (P > 0.05). Fair correlation was detected between estimated and observed runoff depth (r = 0.56; P < 0.01). About 9% of the estimated runoff values were within ±10% of the recorded values and 43% had error percent greater than ±50%. The results indicated that the combined GIS and CN method can be used in semi-arid mountainous watersheds with about 55% accuracy only for management and conservation purposes

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024