Flood Prediction Using Machine Learning Models

Floods are one of nature's most catastrophic calamities which cause irreversible and immense damage to human life, agriculture, infrastructure and socio-economic system. Several studies on flood catastrophe management and flood forecasting systems have been conducted. The accurate prediction of the onset and progression of floods in real time is challenging. To estimate water levels and velocities across a large area, it is necessary to combine data with computationally demanding flood propagation models. This paper aims to reduce the extreme risks of this natural disaster and also contributes to policy suggestions by providing a prediction for floods using different machine learning models. This research will use Binary Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Classifier (SVC) and Decision tree Classifier to provide an accurate prediction. With the outcome, a comparative analysis will be conducted to understand which model delivers a better accuracy.

Constraints in Developing a Complete Bengali Optical Character Recognition System

Technological advancement has led to digitizing hard copies of media effortlessly with optical character recognition (OCR) system. As OCR systems are being used constantly, converting printed or handwritten documents and books have become simple and time efficient. To be a fully functional structure, Bengali OCR system needs to overcome some constraints involved in pre-processing, segmentation and recognition phase. The aim of this research is to analyze the challenges prevalent in developing a Bengali OCR system through robust literature review and implementation.