Development of flood prediction models using machine learning techniques

Flooding and flash flooding events damage infrastructure elements and pose a significant threat to the safety of the people residing in susceptible regions. There are some methods that government authorities rely on to assist in predicting these events in advance to provide warning, but such methodologies have not kept pace with modern machine learning. To leverage these algorithms, new models must be developed to efficiently capture the relationships among the variables that influence these events in a given region. These models can be used by emergency management personnel to develop more robust flood management plans for susceptible areas. The research investigates machine learning techniques to analyze the relationships between multiple variables influencing flood activities in Missouri. The first research contribution utilizes a deep learning algorithm to improve the accuracy and timelessness of flash flood predictions in Greene County, Missouri. In addition, a risk analysis study is conducted to advise the existing flash flood management strategies for the region. The second contribution presents a comparative analysis of different machine learning techniques to develop a classification model and predict the likelihood of flash flooding in Missouri. The third contribution introduces an ensemble of Long Short-Term Memory (LSTM) deep learning models used in conjunction with clustering to create virtual gauges and predict river water levels at unmonitored locations. The LSTM models predict river water levels 4 hours in advance. These outputs empower emergency management decision makers with an advanced warning to better implement flood management plans in regions of Missouri not served with river gauge monitoring --Abstract, page iv

Aetiological evaluation of hyponatremia in hospitalised patients and its prognostic implication in disease outcome

Background: Hyponatremia is very common in clinical practice. Proper evaluation of hyponatremia is essential as causes are many and management of it depends on the aetiology and its long-term outcome. Aetiological evaluation of hyponatremia in hospitalised patients and its prognostic implication in disease outcome was undertaken as such studies were rare in this zone.Methods: One hundred patients whose serum sodium level was <135 mEq/L were studied. The serum sodium and osmolality and urinary sodium and osmolality were estimated in all. The degree of hyponatremia, outcome after treatment and duration of hospital stay were analysed.Results: The mean age was 60.5 years. There were 73% males and 27% females. The incidence of hyponatremia was 10.7%. The mean serum sodium was 129.96 mEq/L and urinary sodium was 40.3 mEq/lL while the mean serum osmolality was 272.8 mOsm/kg and urinary osmolality was 357.7 mOsm/kg. Euvolemia, hypervolemia and hypovolemia were observed in 51%, 28% and 21% respectively. The common clinical features were drowsiness (22%), disorientation (20%), fever (28%), nausea (24%), anorexia (15%), vomiting (14%), hiccup (10%). The common causes were SIADH (34%), renal causes (15%), sepsis (13%), endocrinopathy (11%) and diuretics (11%). The common comorbidities were hypertension (66%) and diabetes mellitus (41%). The mortality was 7%. No side effect was observed during management of hyponatremia.Conclusions: Proper management of hyponatremia irrespective of aetiology had a better prognosis. Factors which are modifiable should be searched and rectified

Supply Chain Infrastructure Restoration Calculator Software Tool -- Developer Guide and User Manual

This report describes a software tool that calculates costs associated with the reconstruction of supply chain interdependent critical infrastructure in the advent of a catastrophic failure by either outside forces (extreme events) or internal forces (fatigue). This tool fills a gap between search and recover strategies of the Federal Emergency Management Agency (or FEMA) and construction techniques under full recovery. In addition to overall construction costs, the tool calculates reconstruction needs in terms of personnel and their required support. From these estimates, total costs (or the cost of each element to be restored) can be calculated. Estimates are based upon historic reconstruction data, although decision managers do have the choice of entering their own input data to tailor the results to a local area

Mapping Influential Nodes for Transportation Network Post-Disaster Restoration Planning Using Real-World Data

Transportation networks are vital elements in modern economic and social systems. These networks are vulnerable to damage from the impact of extreme events. Such damage adversely affects network connectivity, as well as delaying relief and restoration operations. To better plan how to restore these infrastructure elements, this study develops network-analysis and graph theory based tools using real-world data for network restoration planning. Models are developed that identify the influential nodes to map the interdependencies between different modes of transportation and determine which network components contribute most to its connectivity. An efficient node ranking method is also proposed to aid in the restoration of the critical infrastructure network in the aftermath of a disaster. Weighting factors are used to rank and map influential nodes for prioritizing respective network regions by their actual use. This approach is applied to publicly available real-world data for St. Louis, Missouri

Pituitary Stalk Duplication: A Radiological Surprise in a Child With Short Stature

Objective: Pituitary stalk abnormalities are one of the causes of hypopituitarism. Isolated pituitary stalk duplication with a single pituitary gland is extremely rare with only a few cases reported to date. The present case has a different clinical picture as compared to the cases that were previously reported in the literature. Case Report: A 2 years 6-month-old male child, a product of nonconsanguineous marriage, presented with short stature, micropenis with unilateral undescended testis, and delayed motor milestones. His bone age was delayed by 6 months. On further evaluation, he was found to be euthyroid, with stimulated growth hormone (GH) and stimulated gonadotropin levels were suboptimal, whereas the cortisol and the prolactin were normal. Magnetic resonance imaging of the pituitary revealed pituitary stalk duplication with a single pituitary gland of normal dimensions and fused tuber cinereum and mammillary body. Discussion: To our knowledge, only 7 cases with isolated pituitary stalk duplication were reported. The presenting complaint could be primarily of hypopituitarism like short stature or a neurologic complaint or ocular abnormality. The pituitary hormone deficiencies are variable with GH deficiency being the most common as seen in our case. Other associated features could be the morning glory disc anomaly, moyamoya disease, pituitary adenoma or hypoplasia, split hypothalamus, and sellar dermoid. Conclusion: Pituitary stalk duplication is a developmental disorder that is diagnosed only by imaging. Patients should be evaluated for hypopituitarism, particularly the GH and gonadotrophins deficiency, and also screened for associated neurologic and ocular abnormalities