WATER LEVEL FLUCTUATION USING SURVEILLANCE CAMERA

Floods are the most frequent type of natural disaster that cause loss of life and damages to personal property and eventually affect the economic state of the country. Researchers around the world have been made significant efforts in dealing with the flood issue. Computer vision is one of the common approaches being employed which include the use of image segmentation techniques for image understanding and image analysis. The technique has been used in various fields including in flood disaster applications. This paper explores the use of a hybrid segmentation technique in detecting water regions from surveillance images and introduces a flood index calculation to study water level fluctuations. The flood index was evaluated by comparing the result with water level measured by sensor on-site. The experimental results demonstrated that the flood index reflects the trend of water levels of the river. Thus, the proposed technique can be used in detecting water regions and monitoring the water level fluctuation of the river.

Variations in meteorological floods during summer monsoon over India

In this study, rainfall data of 31 mteorological sub-divisioins in India for 113 years (1875-1987) have been used to develop a flood index and statistical properties of the Index are discussed. Relationship of the index with the seasonal rainfall, number of depressions and El-N/no phenomenon are examined. The study revealed that 1971-80 decade, had more number of flood years than the drought years. The flood index was found to be significantly related to flood situation over India. It IS difficult to associate any particular phase of the quasi-biennial oscillations (QBO) with occurrence of floods.

Flood-prone areas delineation in coastal regions using the Geomorphic Flood Index

<p>Detecting areas exposed to flood inundation in coastal zones is of paramount importance for reducing damages and preventing human and economic losses. In general, the Geomorphic Flood Index (GFI) method, based on a Digital Elevation Model (DEM) and mostly applied to riverine flood, provides a good representation of flood-prone areas with low requirements in terms of data, time and costs. However, the method does not account for inter-basin floodwater transfers and, therefore, performs poorly on coastal basins. The present work addresses this shortcoming by explicitly taking into account these potential inter-basin water transfers. We applied the GFI method with this new feature to a coastal basin located in southern Italy and the outcome was compared with a flood inundation map obtained by a two-dimensional hydraulic simulation for a return period of 300 years. Its transferability was tested in a second adjacent coastal basin using a threshold binary classification and the sensitivity of the methodology to the return period was investigated. Results show that coastal flood-prone areas are successfully delineated with performance metrics above 93%. This achievement represents a step further in the application of the GFI method, that can help stakeholders in flood risk management to rapidly and inexpensively characterize hazard-prone areas.</p>

Estimating Flood Characteristics Using Geomorphologic Flood Index with Regards to Rainfall Intensity-Duration-Frequency-Area Curves and CADDIES-2D Model in Three Iranian Basins

There is not enough data and computational power for conventional flood mapping methods in many parts of the world, thus fast and low-data-demanding methods are very useful in facing the disaster. This paper presents an innovative procedure for estimating flood extent and depth using only DEM SRTM 30 m and the Geomorphic Flood Index (GFI). The Geomorphologic Flood Assessment (GFA) tool which is the corresponding application of the GFI in QGIS is implemented to achieved the results in three basins in Iran. Moreover, the novel concept of Intensity-Duration-Frequency-Area (IDFA) curves is introduced to modify the GFI model by imposing a constraint on the maximum hydrologically contributing area of a basin. The GFA model implements the linear binary classification algorithm to classify a watershed into flooded and non-flooded areas using an optimized GFI threshold that minimizes the errors with a standard flood map of a small region in the study area. The standard hydraulic model envisaged for this study is the Cellular Automata Dual-DraInagE Simulation (CADDIES) 2D model which employs simple transition rules and a weight-based system rather than complex shallow water equations allowing fast flood modelling for large-scale problems. The results revealed that the floodplains generated by the GFI has a good agreement with the standard maps, especially in the fluvial rivers. However, the performance of the GFI decreases in the less steep and alluvial rivers. With some overestimation, the GFI model is also able to capture the general trend of water depth variations in comparison with the CADDIES-2D flood depth map. The modifications made in the GFI model, to confine the maximum precipitable area through implementing the IDFAs, improved the classification of flooded area and estimation of water depth in all study areas. Finally, the calibrated GFI thresholds were used to achieve the complete 100-year floodplain maps of the study areas.

Thành lập nhanh bản đồ lũ bằng chỉ số lũ khác biệt chuẩn hóa NDFI và chỉ số khác biệt lũ trong vùng thực vật thấp NDFVI sử dụng lợi thế của hệ thống Vietnam Data Cube

Trong bối cảnh biến đổi khí hậu, việc có được một bản đồ lũ kịp thời và chính xác ngay khi thiên tai là thực sự cần thiết trong việc lập kế hoạch quản lý khẩn cấp nhằm giảm thiểu rủi ro thiên tai một cách hiệu quả.Nghiên cứu áp dụng một phương pháp lập bản đồ lũ lụt nhanh dựa trên hai chỉ số khác biệt lũ chuẩn hóa (NDFI – Normalized Difference Flood Index) và chỉ số khác biệt lũ trong vùng thực vật thấp (NDFVI - Normalized Difference Flood inshort Vegetation Index) từ chuỗi dữ liệu của ảnh Radar khẩu độ tổng hợp (SAR –Synthetic Aperture Radar) Sentinel-1.Hai chỉ số này được tính toán, trên các cảnh ảnh trước lũ và trong lúc có lũ, từ đó lập bản đồ các khu vực ngập nước lũ và các khu vực có nước ngập thảm thực vật thấp bằng phương pháp phân ngưỡng. Dữ liệu SAR băng tần C của vệ tinhSentinel-1 được sử dụng trong nghiên cứu này do đây là nguồn dữ liệu miễn phívà có tần suất chụp ảnh khá tốt (12 ngày). Tuy vậy, phương pháp cũng có thể được áp dụng cho tất cả các dữ liệu vệ tinh SAR băng C khác để tăng cao tần suất quan sát, một điều rất cần thiết trong theo dõi lũ. Áp dụng phương pháp trên khu vực thử nghiệm tỉnh Đồng Tháp năm 2018 cho thấy có độ tin cậy cao và hiệu quả của phương pháp qua việc đánh giá, so sánh với dữ liệu thực địa gồm143 điểm. Hiện nay, toàn bộ ảnh Sentinel-1 đã được thu thập và liên tục cập nhật trong hệ thống Vietnam Data Cube do Trung tâm Vũ trụ Việt Nam vận hành cho phép áp dụng phương pháp trên toàn lãnh thổ Việt Nam