Flood Monitoring System Research Articles

A Literature survey on Internet of Things based Flood Detection and Monitoring System Using Raspberry Pi

A Literature survey on Internet of Things based Flood Detection and Monitoring System Using Raspberry Pi

Applicability of HEC-RAS 2D and GFMS for flood extent mapping: a case study of Sangam area, Prayagraj, India

Catastrophic floods cause losses to millions of lives and property damages every year. The situation is more precarious particularly in countries lacking advanced tools and early warning systems for quick disaster management and mitigation. This article presents a study conducted to develop and understand methodology for delineation of flood extent and identification of various flood risk zones in Prayagraj, India at the confluence of River Ganga and Yamuna (sangam). In this study, a well-established hydraulic model (HEC-RAS 5.0.7 (2D)) and Global Flood Monitoring System (GFMS) tools were utilized. The methodology solves two glitches related to flood studies, i.e., capability of HEC-RAS 2D flood model and GFMS discharge for 1D and 2D flood modeling and secondly using the output of the model, development of a methodology for the identification of various flood risk zones. The modeled and observed data were in close agreement indicating a successful application of HEC-RAS and GFSM data/tools. Using the methodology of this study, various flood modeling and delineation of risk zones can be made for various flood-affected areas in India and worldwide. These tools can also be used for making guidelines for emergency response in flood-affected areas.

Flood Warning and Monitoring System Utilizing Internet of Things Technology

Flooding is one of the major disasters occurring in various parts of the world including Malaysia. To reduce the effect of the disaster, a flood warning and monitoring are needed to give an early warning to the victims at certain place with high prone to flood. By implementing Internet of Thing technology into the system, it could help the victim to get an accurate status of flood in real-time condition. This paper is develop a real-time flood monitoring and early warning system using wireless sensor node at a high prone area of flood. This system is based on NodeMCU based technology integrated using Blynk application. The wireless sensor node can help the victims by detecting the water levels and rain intensity while giving an early warning when a flood or heavy rain occurs. Basically, the sensor node consists of ultrasonic sensor and rain sensor controlled by NodeMCU as the microcontroller of the system which placed at the identified flood area. Buzzer and LED started to trigger and alert the victim when the flood had reached certain level of hazard. Data detected from the sensors are sent to the Blynk application via wireless connection. Victim will get to know the current status of flood and rain by viewing the interface and receiving push notification that available in Blynk application via IOS or Android smartphones. The flood level’s data sent to the email could help various organizations for further improvement of the system and flood forecasting purposes. After a test had been conducted, it was found that this prototype can monitor, detect and give warning with notification to the victim earlier before the occurrence of floods.

Implementation of GSM Communication on Flood Monitoring Systems Based on Multiple Locations Visualization

Flooding is one of the national disaster tragedies that currently often occurs in various countries. Therefore the government encourages various government, private and community institutions to develop disaster mitigation systems. Disaster mitigation systems are short-term programs aimed at reducing disaster victims. Therefore, this paper aims to describe the results of research in building flood monitoring information systems by developing flood data visualization systems in many locations. The system as a whole consists of two systems namely a flood detection system as a client and a flood monitoring information system as a server. the two systems communicate in sending flood data using GSM services, namely SMS. Flood detection systems are designed using ultrasonic sensors, temperature sensors, rain sensors, Arduino Uno microcontrollers and GSM modules. While the flood monitoring information system server was designed using a GSM modem, web server application, mySQL database, PHP engine and Gammu. The results showed that the flood detection system can send flood data consisting of flood locations, water levels, temperatures and rain conditions to the flood monitoring information system using SMS. SMS data received can already be converted by a flood monitoring information system server to a flood visualization-based interface for many locations in real time.

Evaluation of real-time global flood modeling with satellite surface inundation observations from SMAP

Improving flood modeling accuracy is crucial for real-time flood monitoring and early warning systems. Knowing the sources, patterns and driving factors of model uncertainty aids the development of more accurate flood predictions. This study investigates the consistency of two global flood inundation products, i.e., the Soil Moisture Active Passive (SMAP) satellite based fractional water (Fw) cover and the Global Flood Monitoring System (GFMS) modeled flood inundation. Using Pearson's correlation coefficient (r) as the indicator of the SMAP-GFMS model consistency, this research documents the spatial and temporal patterns of the correlations between the two flood products, and investigates factors affecting these relationships, including climate, land cover, hydrology and terrain distributions. Results reveal that globally, 64% locations have moderate to strong SMAP-GFMS correlation (r ≥ 0.4). Locations that are dry and have low biomass and high seasonal flood variability tend to have high correlation; for example, 47% locations with r ≥ 0.4 occur in tropical and arid climate zones, and 43% locations with r ≥ 0.4 are observed in Barren, Evergreen Broadleaf Forest, Grasslands, Open Shrubland and Savannahs. Also, larger rivers have higher correlation, and in each Strahler stream order there are 60% to 65% locations having r ≥ 0.4. Larger watersheds show higher SMAP-GFMS consistency in particular watersheds between 1000 and 40,000 km2. Regions with greater urban infrastructure tend to have lower correlation, while locations with lower elevations and relatively flat topography have higher SMAP-GFMS consistency. This study indicates that GFMS and SMAP provide complementary information on surface water storage variations influencing precipitation driven runoff and flooding, which may enable enhanced global flood predictions.

PENGEMBANGAN PROTOTYPE SISTEM MONITORING KETINGGIAN AIR UNTUK PERINGATAN DINI

Indonesia is a country with high rainfall at the rainy season; it can last four months within a year. With the increasing of development in the urban areas, causing the shortage of water absorption area. Many people disposing trash in the water flow is also a factor in the occurrence of floods. Flooding may cause the loss of property; it can also cause loss of life. It takes the presence of monitoring and warning system in order to avoid the occurrence of casualties. And minimize material losses incurred as a result of flooding By using prototype method A water quality monitoring system was created with arduino uno R3 and HCSR04 sensors. System Monitoring of water levels is made to be easily accessible anytime and anywhere. System w arnings are also made to be able to deliver warnings quickly, and have territory w ide coverage. By using this system can be monitored t hrough the web service. So this system can help the user, avoid or can suppress the loss Arising from the flood that occurred. Keywords — The flood monitoring system, Flood warning system, Arduino Uno R3

Synthetic aperture radar and optical remote sensing image fusion for flood monitoring in the Vietnam lower Mekong basin: a prototype application for the Vietnam Open Data Cube

ABSTRACT Flood monitoring systems are crucial for flood management and consequence mitigation in flood prone regions. Different remote sensing techniques are increasingly used for this purpose. However, the different approaches suffer various limitations, including cloud and weather effects (optical data), and low spatial resolution and poor colour presentation (synthetic aperture radar data). This study fuses two data types (Landsat and Sentinel-1) to overcome these limitations and produce better quality images for a prototype flood application in the Vietnam Open Data Cube (VODC). Visual and quantitative evaluation of fused image quality revealed improvement in the images compared with the original scenes. Ground-truth data was used to develop the study flood extraction algorithm and we found a good agreement between our results and SERVIR Mekong (a joint initiative by the US agency for International Development (USAID), National Aeronautics and Space Administration (NASA), Myanmar, Thailand, Cambodia, Laos and Vietnam) maps. While the algorithm is run on a personal computer (PC), it has a clear potential to be developed for application on a big data system.

Hierarchical Coloured Petri-Net Based Multi-Agent System for Flood Monitoring, Prediction, and Rescue (FMPR)

In Punjab Pakistan, a river flood event is the most important natural disasters that every year causes high human casualties as well as heavy economic losses. A safety-critical system should be correct, reliable, complete, consistent, and unambiguous for accurate and precise flood monitoring, prediction, as well as emergency and rescue services during or after a flood event. System correctness must be ensured in every phase of the development of a safety-critical Flood Monitoring, Prediction, and Rescue (FMPR) system. Correctness is a functional property related to the behavior of the system having sub-properties of safety and liveness. These sub-properties must also be correct, complete, consistent, and unambiguous. Rigorous mathematical based software engineering methods like formal methods are suitable for the specification, design, modeling, verification, and validation of an FMPR safety-critical system. The proposed safety-critical system for FMPR is distributed as it is based on multi-agents. This system is specified, analyzed and designed by using Gaia multi-agent methodology, which is based on organizational abstractions. Gaia role model-based agent roles are specified, regular expression based liveness properties are specified, and first-order predicate calculus based safety properties are specified. Thus, safety and liveness properties are formally specified. After the detailed design phase, the system is formally modeled and verified by Hierarchical Coloured-Petri Nets (CP-Nets). As a result, a novel formal system for the specification, analysis, design, modeling, and verification of the FMPR system is proposed. This safety-critical system is distributed based on multi-agents.

Flood Monitoring and Early Warning System Using Ultrasonic Sensor

The purpose of this study is to develop a real-time flood monitoring and early warning system in the northern portion of the province of Isabela, particularly the municipalities near Cagayan River. Ultrasonic sensing techniques have become mature and are widely used in the various fields of engineering and basic science. One of advantage of ultrasonic sensing is its outstanding capability to probe inside objective non-destructively because ultrasound can propagate through any kinds of media including solids, liquids and gases. This study focuses only on the water level detection and early warning system (via website and/or SMS) that alerts concern agencies and individuals for a potential flood event. Furthermore, inquiry system is also included in this study to become more interactive wherein individuals in the community could inquire the actual water level and status of the desired area or location affected by flood thru SMS keyword. The study aims in helping citizens to be prepared and knowledgeable whenever there is a flood. The novelty of this work falls under the utilization of the Arduino, ultrasonic sensors, GSM module, web-monitoring and SMS early warning system in helping stakeholders to mitigate casualties related to flood. The paper envisions helping flood-prone areas which are common in the Philippines particularly to the local communities in the province. Indeed, it is relevant and important as per needs for safety and welfare of the community.

The effect of floods on anemia among reproductive age women in Afghanistan.

This study uses biomarker information from the 2013 National Nutrition Survey Afghanistan and satellite precipitation driven modeling results from the Global Flood Monitoring System to analyze how floods affect the probability of anemia in Afghan women of reproductive age (15–49). In addition to establishing a causal relation between the two by exploiting the quasi-random variation of floods in different districts and periods, the analysis demonstrates that floods have a significant positive effect on the probability of anemia through two possible transmission mechanisms. The first is a significant effect on inflammation, probably related to water borne diseases carried by unsafe drinking water, and the second is a significant negative effect on retinol concentrations. Because the effect of floods on anemia remains significant even after we control for anemia’s most common causes, we argue that the condition may also be affected by elevated levels of psychological stress.

Review on Flood Monitoring and Early Warning System

Review on Flood Monitoring and Early Warning System

Towards a continuous inland excess water flood monitoring system based on remote sensing data

Abstract Inland excess water (IEW) is a type of flood where large flat inland areas are covered with water during a period of several weeks to months. The monitoring of these floods is needed to understand the extent and direction of development of the inundations and to mitigate their damage to the agricultural sector and build up infrastructure. Since IEW affects large areas, remote sensing data and methods are promising technologies to map these floods. This study presents the first results of a system that can monitor inland excess water over a large area with sufficient detail at a high interval and in a timely matter. The methodology is developed in such a way that only freely available satellite imagery is required and a map with known water bodies is needed to train the method to identify inundations. Minimal human interference is needed to generate the IEW maps. We will present a method describing three parallel workflows, each generating separate maps. The maps are combined to one weekly IEW map. At this moment, the method is capable of generating IEW maps for a region of over 8000 km2, but it will be extended to cover the whole Great Hungarian Plain, and in the future, it can be extended to any area where a training water map can be created.

Applicability of HEC-RAS & GFMS tool for 1D water surface elevation/flood modeling of the river: a Case Study of River Yamuna at Allahabad (Sangam), India

Floods always have been a natural disasters and it is very difficult to stop flood destruction and apply required preventive measures without any pre-prediction. Remote sensing and GIS tool like Global flood monitoring system (GFMS) and HEC-RAS model provide the idea to analyze the flood analysis and their potential. The purpose of this study was to model the water surface elevation (WSE) of river Yamuna at district Allahabad near Sangam area, Uttar Pradesh, India by using the one of the latest flood monitoring tool (GFMS) which provides near real time discharge value of various streams of the world. In this study three stations were selected for calibration of the model, at present these stations are also being used by various government organizations of India for river stage monitoring. HEC-RAS modeling was carried out for determine flood events or WSE/HFL (High Flood Level) of the year 1978 and 2001–2014. After that, the modeled output data was compared with real observed data and no significance difference in most of the cases was observed. The findings of HEC-RAS modeling indicate that applicability of this model can play the effective role to predict flood potential and identify the WSE in future for making the plan for any city situated near the river. In this regard, WSE level for upcoming 100, 500 and 1000 years was estimated by Gumbel’s distribution method and found maximum stage 89.367, 90.568 and 92.268 m, respectively, above from mean sea level. Further, this study indicates that a larger area nearby the study area falls in highly risky zone and plan for safety management is needed. This methodology can be used for urban planning and disaster management for various cities situated near the rivers in the world.

Hydrological Modeling and Capacity Building in the Republic of Namibia

Abstract The Republic of Namibia, located along the arid and semiarid coast of southwest Africa, is highly dependent on reliable forecasts of surface and groundwater storage and fluxes. Since 2009, the University of Oklahoma (OU) and National Aeronautics and Space Administration (NASA) have engaged in a series of exercises with the Namibian Ministry of Agriculture, Water, and Forestry to build the capacity to improve the water information available to local decision-makers. These activities have included the calibration and implementation of NASA and OU’s jointly developed Coupled Routing and Excess Storage (CREST) hydrological model as well as the Ensemble Framework for Flash Flood Forecasting (EF5). Hydrological model output is used to produce forecasts of river stage height, discharge, and soil moisture. To enable broad access to this suite of environmental decision support information, a website, the Namibia Flood Dashboard, hosted on the infrastructure of the Open Science Data Cloud, has been developed. This system enables scientists, ministry officials, nongovernmental organizations, and other interested parties to freely access all available water information produced by the project, including comparisons of NASA satellite imagery to model forecasts of flooding or drought. The local expertise needed to generate and enhance these water information products has been grown through a series of training meetings bringing together national government officials, regional stakeholders, and local university students and faculty. Aided by online training materials, these exercises have resulted in additional capacity-building activities with CREST and EF5 beyond Namibia as well as the initial implementation of a global flood monitoring and forecasting system.

A MODIS-based automated flood monitoring system for southeast asia

Flood disasters in Southeast Asia result in significant loss of life and economic damage. Remote sensing information systems designed to spatially and temporally monitor floods can help governments and international agencies formulate effective disaster response strategies during a flood and ultimately alleviate impacts to population, infrastructure, and agriculture. Recent destructive flood events in the Lower Mekong River Basin occurred in 2000, 2011, 2013, and 2016 (http://ffw.mrcmekong.org/historical_rec.htm, April 24, 2017).The large spatial distribution of flooded areas and lack of proper gauge data in the region makes accurate monitoring and assessment of impacts of floods difficult. Here, we discuss the utility of applying satellite-based Earth observations for improving flood inundation monitoring over the flood-prone Lower Mekong River Basin. We present a methodology for determining near real-time surface water extent associated with current and historic flood events by training surface water classifiers from 8-day, 250-m Moderate-resolution Imaging Spectroradiometer (MODIS) data spanning the length of the MODIS satellite record. The Normalized Difference Vegetation Index (NDVI) signature of permanent water bodies (MOD44W; Carroll et al., 2009) is used to train surface water classifiers which are applied to a time period of interest. From this, an operational nowcast flood detection component is produced using twice daily imagery acquired at 3-h latency which performs image compositing routines to minimize cloud cover. Case studies and accuracy assessments against radar-based observations for historic flood events are presented. The customizable system has been transferred to regional organizations and near real-time derived surface water products are made available through a web interface platform. Results highlight the potential of near real-time observation and impact assessment systems to serve as effective decision support tools for governments, international agencies, and disaster responders.

Providing peak river flow statistics and forecasting in the Niger River basin

Flooding is a growing concern in West Africa. Improved quantification of discharge extremes and associated uncertainties is needed to improve infrastructure design, and operational forecasting is needed to provide timely warnings. In this study, we use discharge observations, a hydrological model (Niger-HYPE) and extreme value analysis to estimate peak river flow statistics (e.g. the discharge magnitude with a 100-year return period) across the Niger River basin. To test the model's capacity of predicting peak flows, we compared 30-year maximum discharge and peak flow statistics derived from the model vs. derived from nine observation stations. The results indicate that the model simulates peak discharge reasonably well (on average + 20%). However, the peak flow statistics have a large uncertainty range, which ought to be considered in infrastructure design. We then applied the methodology to derive basin-wide maps of peak flow statistics and their associated uncertainty. The results indicate that the method is applicable across the hydrologically active part of the river basin, and that the uncertainty varies substantially depending on location. Subsequently, we used the most recent bias-corrected climate projections to analyze potential changes in peak flow statistics in a changed climate. The results are generally ambiguous, with consistent changes only in very few areas. To test the forecasting capacity, we ran Niger-HYPE with a combination of meteorological data sets for the 2008 high-flow season and compared with observations. The results indicate reasonable forecasting capacity (on average 17% deviation), but additional years should also be evaluated. We finish by presenting a strategy and pilot project which will develop an operational flood monitoring and forecasting system based in-situ data, earth observations, modelling, and extreme statistics. In this way we aim to build capacity to ultimately improve resilience toward floods, protecting lives and infrastructure in the region.

A high performance, low power computational platform for complex sensing operations in smart cities

Abstract This paper presents a new wireless platform designed for an integrated traffic/flash flood monitoring system. The sensor platform is built around a 32-bit ARM Cortex M4 microcontroller and a 2.4 GHz 802.15.4 ISM compliant radio module. It can be interfaced with fixed traffic sensors, or receive data from vehicle transponders. This platform is specifically designed for solar-powered, low bandwidth, high computational performance wireless sensor network applications. A self-recovering unit is designed to increase reliability and allow periodic hard resets, an essential requirement for sensor networks. A radio monitoring circuitry is proposed to monitor incoming and outgoing transmissions, simplifying software debugging. We illustrate the performance of this wireless sensor platform on complex problems arising in smart cities, such as traffic flow monitoring, machine-learning-based flash flood monitoring or Kalman-filter based vehicle trajectory estimation. All design files have been uploaded and shared in an open science framework, and can be accessed from https://osf.io/fuyqd/ . The hardware design is under CERN Open Hardware License v1.2.

Dynamic computing resource allocation in online flood monitoring and prediction

This paper presents tools and methodologies for dynamic allocation of high performance computing resources during operation of the Floreon+ online flood monitoring and prediction system. The resource allocation is done throughout the execution of supported simulations to meet the required service quality levels for system operation. It also ensures flexible reactions to changing weather and flood situations, as it is not economically feasible to operate online flood monitoring systems in the full performance mode during non-flood seasons. Different service quality levels are therefore described for different flooding scenarios, and the runtime manager controls them by allocating only minimal resources currently expected to meet the deadlines. Finally, an experiment covering all presented aspects of computing resource allocation in rainfall-runoff and Monte Carlo uncertainty simulation is performed for the area of the Moravian-Silesian region in the Czech Republic.

A GENERAL PURPOSE MODEL FOR CONTEXT AWARE BASED FLOOD MONITORING SYSTEM

One of the natural disasters that often occur is flood. Flood causes many victims both materially and fatalities, especially in poor and developing countries where people are less concerned with the surrounding natural environment. In this research we will build a model for the detection and monitoring system of the flood disaster that will explore the context obtained through the sensor. The model of Wireless Sensor Network (WSN) based flood detection and monitoring system includes four main functional components: physical phenomena, local data, context awareness, and user purposed. The system develop some useful insight information prediction scheme based on contexts and time dependent data models.

Wireless Sensor Network for Real-time Flood Monitoring Based on 6loWPAN Communication Standard

Riverine flood is a major disaster faced by most countries and has significant adverse effect on long term economic growth of affected regions and their environments. Several systems have previously employed different technologies to monitor riverine flood but are expensive with low accuracy and consumes high amount of energy. In this paper, we proposed an energy efficient and accurate flood monitoring system. The system leverages on Internet Protocol Version 6 over Low Power Wireless Personal Area Network (6loWPAN) technology to construct a Wireless Sensor Network (WSN) comprising of two XM1000 motes and a rule-base water level monitoring application. The motes were configured using NesC programming for flood monitoring with Basestation and water level sensing applications. The water level sensing mote samples and transmits real-time water level information to the Basestation mote which interfaces with a rule-based water level monitoring application. The application compares current water level with a predetermined threat level and alerts relevant agencies when flood is imminent via an email. The results obtained from the emulation of the developed system showed that, it achieved an accuracy of 95.3% in water level monitoring with a Mean Squared Error of 5.1. The power consumed in transmitting a packet of 2 bytes payload plus other overhead was 0.4µJ and 0.0396mJ with and without 6loWPAN configuration respectively.