An early flood detection system using mobile networks

2015 ◽  
Author(s):  
Hung Ngoc Do ◽  
Minh-Thanh Vo ◽  
Van-Su Tran ◽  
Phuoc Vo Tan ◽  
Cuong Viet Trinh
Keyword(s):  
Mobile Networks ◽  
Detection System ◽  
Flood Detection

scholarly journals Evaluation of the satellite-based Global Flood Detection System for measuring river discharge: influence of local factors

2014 ◽  
Vol 18 (11) ◽  
pp. 4467-4484 ◽  
Author(s):  
B. Revilla-Romero ◽  
J. Thielen ◽  
P. Salamon ◽  
T. De Groeve ◽  
G. R. Brakenridge
Keyword(s):  
Real Time ◽  
River Discharge ◽  
Detection System ◽  
Poor Performance ◽  
Model Verification ◽  
Area Index ◽  
General Terms ◽  
Skill Scores ◽  
Flood Detection ◽  
Discharge Measurements

Abstract. One of the main challenges for global hydrological modelling is the limited availability of observational data for calibration and model verification. This is particularly the case for real-time applications. This problem could potentially be overcome if discharge measurements based on satellite data were sufficiently accurate to substitute for ground-based measurements. The aim of this study is to test the potentials and constraints of the remote sensing signal of the Global Flood Detection System for converting the flood detection signal into river discharge values. The study uses data for 322 river measurement locations in Africa, Asia, Europe, North America and South America. Satellite discharge measurements were calibrated for these sites and a validation analysis with in situ discharge was performed. The locations with very good performance will be used in a future project where satellite discharge measurements are obtained on a daily basis to fill the gaps where real-time ground observations are not available. These include several international river locations in Africa: the Niger, Volta and Zambezi rivers. Analysis of the potential factors affecting the satellite signal was based on a classification decision tree (random forest) and showed that mean discharge, climatic region, land cover and upstream catchment area are the dominant variables which determine good or poor performance of the measure\\-ment sites. In general terms, higher skill scores were obtained for locations with one or more of the following characteristics: a river width higher than 1km; a large floodplain area and in flooded forest, a potential flooded area greater than 40%; sparse vegetation, croplands or grasslands and closed to open and open forest; leaf area index > 2; tropical climatic area; and without hydraulic infrastructures. Also, locations where river ice cover is seasonally present obtained higher skill scores. This work provides guidance on the best locations and limitations for estimating discharge values from these daily satellite signals.

scholarly journals Internet-Based Flood Detection System (Iot) and Telegram Messenger Using Mcu Node and Water Level Sensor

2020 ◽  
Vol 4 (1) ◽  
pp. 230-235
Author(s):  
Novianda Nanda Nanda ◽  
Rizalul Akram ◽  
Liza Fitria
Keyword(s):  
Water Level ◽  
Early Warning ◽  
High Intensity ◽  
Rainy Season ◽  
Early Warning System ◽  
Detection System ◽  
Warning System ◽  
Level Sensor ◽  
Flood Detection ◽  
Internet Of Thing

During the rainy season, several regions in Indonesia experienced floods even to the capital of Indonesia also flooded. Some of the causes are the high intensity of continuous rain, clogged or non-smooth drainage, high tides to accommodate the flow of water from rivers, other causes such as forest destruction, shallow and full of garbage and other causes. Every flood disaster comes, often harming the residents who experience it. The late anticipation from the community and the absence of an early warning system or information that indicates that there will be a flood so that the community is not prepared to face floods that cause a lot of losses. Therefore it is necessary to have a detection system to provide early warning if floods will occur, this is very important to prevent material losses from flooded residents. From this problem the researchers designed an internet-based flood detection System of Things (IoT). This tool can later be controlled via a smartphone remotely and can send messages Telegram messenger to citizens if the detector detects a flood will occur.Keywords: Flooding, Smartphone, Telegram messenger, Internet of Thing (IoT).

A robust intrusion detection system using machine learning techniques for MANET

2020 ◽  
Vol 24 (3) ◽  
pp. 253-260
Author(s):  
N. Ravi ◽  
G. Ramachandran
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Mobile Computing ◽  
Mobile Networks ◽  
Intrusion Detection System ◽  
Detection System ◽  
Mobile Network ◽  
Machine Learning Techniques ◽  
Test Bed ◽  
Learning Techniques

Recent advancement in technologies such as Cloud, Internet of Things etc., leads to the increase usage of mobile computing. Present day mobile computing are too sophisticated and advancement are reaching great heights. Moreover, the present day mobile network suffers due to external and internal intrusions within and outside networks. The existing security systems to protect the mobile networks are incapable to detect the recent attacks. Further, the existing security system completely depends on the traditional signature and rule based approaches. Recent attacks have the property of not fluctuating its behaviour during attack. Hence, a robust Intrusion Detection System (IDS) is desirable. In order to address the above mentioned issue, this paper proposed a robust IDS using Machine Learning Techniques (MLT). The key of using MLT is to utilize the power of ensembles. The ensembles of classifier used in this paper are Random Forest (RF), KNN, Naïve Bayes (NB), etc. The proposed IDS is experimentally tested and validated using a secure test bed. The experimental results also confirms that the proposed IDS is robust enough to withstand and detect any form of intrusions and it is also noted that the proposed IDS outperforms the state of the art IDS with more than 95% accuracy.

scholarly journals Development and Evaluation of Congestion Detection System Using Complex Event Processing for Mobile Networks

2015 ◽  
Vol 23 (4) ◽  
pp. 430-440
Author(s):  
Hiroshi Yamamoto ◽  
Tatsuya Takahashi ◽  
Norihiro Fukumoto ◽  
Shigehiro Ano ◽  
Katsuyuki Yamazaki
Keyword(s):  
Mobile Networks ◽  
Detection System ◽  
Complex Event Processing ◽  
Event Processing ◽  
Congestion Detection

scholarly journals FLOOD DETECTION IN NORWAY BASED ON SENTINEL-1 SAR IMAGERY

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 349-355 ◽  
Author(s):  
J. H. Reksten ◽  
A.-B. Salberg ◽  
R. Solberg
Keyword(s):  
Detection System ◽  
Aerial Images ◽  
Automated System ◽  
Training Dataset ◽  
Reference Image ◽  
Visual Interpretation ◽  
Sar Images ◽  
Additional Information ◽  
Flood Detection ◽  
Sar Imagery

<p><strong>Abstract.</strong> After large flood incidents in Norway, The Norwegian Water Resources and Energy Directorate (NVE), has the responsibility for documenting the flooded areas. This has so far mainly been performed by utilising aerial images and visual interpretation. Satellite images are a valuable source of additional information as they are able to cover vast areas in each satellite pass. In this paper a fully automated system for detecting and delineating floods with the use of Synthetic Aperture Radar (SAR) images from the Sentinel-1 satellites is presented. In SAR images wet areas and water bodies usually show lower backscatter than dry areas. The flood detection system is thus based on comparing a reference image acquired before the flood with the flood event image. A Sentinel-1 training dataset has been obtained and manually annotated by NVE from three flood events in Norway. This training set has been used to train a random forest (RF) classifier, which outputs a score for each pixel in the SAR image. This score image is thresholded in order to obtain a crude flood detection. Unfortunately, changes in the backscatter may also be triggered by other events such as melting snow and harvested fields of crops. To mitigate such <q>lookalikes</q>, several techniques have been implemented and tested. This includes masking based on size, slope and <q>height above nearest drainage</q> (HAND). The experiments presented show that the system performance is very good. Of the 179 manually labelled flood objects, 168 are detected. The system is being applied operationally at NVE.</p>

scholarly journals Automatic Detection of Flood Using Remote Sensing Images

2020 ◽  
Vol 02 (01) ◽  
pp. 11-26 ◽  
Author(s):  
Amith Chandrakant Chawan ◽  
Vaibhav K Kakade ◽  
Jagannath K Jadhav
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Remote Sensing Images ◽  
Hazardous Area ◽  
High Resolution Images ◽  
Flood Detection

Remote sensing imaging (RSI) technology has recently been identified as an effective photogrammetric data acquisition platform to rapidly provide high resolution images due to its profitability, its ability to fly at low altitude and the ability to analysis in dangerous areas. The various kinds of classification techniques are have been used for flood extent mapping for finding the flood affected region, but based on the color region based analysis the classified hazardous area has very complex. Due to over the above issues in this work there significant enhancements have appeared in the classification of remote sensing images using Contiguous Deep Convolutional Neural Network (CDCNN).In the flood detection system the four different kinds of process like preprocessing, segmentation, feature extraction and the Contiguous Deep Convolutional Neural Network (CDCNN) has been executed for identifying the flood defected region. This works also investigates and compare with the possible methods with the proposed CDCNN for accurately identified by the Classification details of the RSI

scholarly journals Flood Detection System Using Ultrasonic Sensor with Fuzzy Logic Method

2021 ◽  
Author(s):  
Jessy Nasyta Putri Santoso ◽  
Tri Tisna Firly Hartini ◽  
Ali Suryaperdana Agoes
Keyword(s):  
Fuzzy Logic ◽  
Detection System ◽  
Ultrasonic Sensor ◽  
Fuzzy Logic Method ◽  
Flood Detection ◽  
Logic Method
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