Study and Analysis of Chennai Flood 2015 Using GIS and Multicriteria Technique

Seenirajan, Muthusamy; Muthusamy, Natarajan; Thangaraj, Ramasundaram; Bagyaraj, Murugesan

doi:10.4236/jgis.2017.92009

Cited by 22 publications

(11 citation statements)

References 3 publications

(1 reference statement)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The river sediments contain clay, silt, and sands originating from the alteration of charnockite rocks and reworked alluvium deposits. A thin layer of laterite is also found in some places, and well-rounded pebbles have been encountered at several locations at varying depths [8,39,63,64].…”

Section: Description Of the Studied Fluvial Systemmentioning

confidence: 99%

Geochemical Footprint of Megacities on River Sediments: A Case Study of the Fourth Most Populous Area in India, Chennai

et al. 2019

View full text Add to dashboard Cite

An intensive surface sediment survey was carried out over 24 locations from the upstream to downstream sections of two large rivers (Adyar and Cooum) in Chennai (India) during the February dry season of 2015. Trace element concentrations were assessed on a <63 µm fraction using the Geoaccumulation Index (Igeo) and the newly proposed Geochemical Urban Footprint Index (GUFI), which can be performed to determine the pollution status of any megacity river influenced by urban development. The sediment quality of Chennai’s rivers was also compared to worldwide megacity pollution using sediment quality guidelines (SQGs), and a new megacity pollution ranking was determined. The Igeo results indicate that the Chennai rivers studied are strongly to extremely polluted regarding trace element content of sediment. Silver (Ag), Cadmium (Cd) and Mercury (Hg) are the most significant tracers of urban contamination. Chromium (Cr) concentrations show an industrial contamination gradient in relation to levels of other trace elements (As, Cu, Ni, Pb, and Zn) at the Chennai megacity scale. The GUFI ranges from moderate to extreme contamination, particularly in the downstream stretches of the two rivers. This spatial trend is related to various point sources and identified at specific sampling stations, with a lack of identifiable buffer zones. According to the worldwide comparison of megacity pollution, Chennai is ranked in fifth position. The present position can be attributed to a number of explanations: a population explosion associated with the unplanned growth of the city and non-controlled point sources of pollution in Chennai’s waterways.

show abstract

Section: Description Of the Studied Fluvial Systemmentioning

confidence: 99%

Geochemical Footprint of Megacities on River Sediments: A Case Study of the Fourth Most Populous Area in India, Chennai

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Chennai city received nearly 300% above normal rainfall during late November and early December in 2015. The extreme precipitation event caused incessant flooding in several low-lying regions because of an improper drainage system [51]. These areas include Madhavaram, Tondiarpet, Tiruvottiyur, Manali and Royapuram (see Figure 9) and account for nearly 34% of the total slum population in the city [33].…”

Section: Modelling Drainage Flow Over Inundated North Chennai Slumsmentioning

confidence: 99%

Quick Predictions of Onset Times and Rain Amounts from Monsoon Showers over Urban Built Environments

Gumber

Ghosh

2022

Atmosphere

View full text Add to dashboard Cite

Predicting the onset times of precipitation over densely populated cities for the purposes of timely evacuation is a challenge. This paper explored a flooding event over an urban built environment in a South Asian mega city, Chennai, where extant urban planning models rely on predicted rainwater amounts for early warning and impact assessment studies. However, the time duration of flooding events is related to the nature of the urban sprawl in the built environment. Any evacuation measure is invariably tied down to the time duration over which the precipitation event occurs, and therefore to the expected time of a precipitation event to begin. In this context, a crucial parameter useful to municipal authorities is the onset time of precipitation. This study used optimised analytical formulations to predict this time, and the derived analytical expressions for the case study yielded comparable times estimated from a computer-intensive full-scale large eddy model within an accuracy of 2%. It is suggested that municipal authorities (who are non-experts in fluid mechanics) use this early prediction for the purposes of quick alerts to a congested city’s most vulnerable citizens within urban sprawls. However, for the procedure to work at its best, it involves a two-stage procedure. The first step involves the use of a parcel model to obtain the expected cloud droplet spectral spreads based on the prevailing dynamical characterisations. The second step involves an optimisation procedure involving cloud spectral properties from the first step to quantify both the auto-conversion rates and the threshold. Thereafter, an onset time calculation based on cloud properties is estimated. These new results are cast in closed form for easy incorporation into meteorological applications over a variety of urban scales. Rain mass amounts were also predicted analytically and used to configure Aeronautical Reconnaissance Coverage Geographic Information System (ARCGIS) to compute low drainage flow rates over the vulnerable parts of Chennai city. It was found that heavy precipitation over the North Chennai region yielded discharge rates to the tune of ~250 m3s−1 during a 24 h period, causing intense flooding in the low-lying areas around the Cooum River basin with a large population density, with estimates sufficiently corroborating observations.

show abstract

“…The service layer encapsulates core modules or functions for services, which allow the application and operational system developers to focus on the business process and user demands and simplifies the development workload in the application layer. The service and application layers are loosely coupled to enhance system flexibility and scalability [43,44]. The application layer performs a dynamic visual simulation of the flood risk for decision support, acting as an interface for the conversion of data to information and knowledge.…”

Section: Spatio-temporal Computation Frameworkmentioning

confidence: 99%

Dynamic 3D Simulation of Flood Risk Based on the Integration of Spatio-Temporal GIS and Hydrodynamic Models

Peng

et al. 2019

IJGI

View full text Add to dashboard Cite

Dynamic visual simulation of flood risk is crucial for scientific and intelligent emergency management of flood disasters, in which data quality, availability, visualization, and interoperability are important. Here, a seamless integration of a spatio-temporal Geographic Information System (GIS) with one-dimensional (1D) and two-dimensional (2D) hydrodynamic models is achieved for data flow, calculation processes, operation flow, and system functions. Oblique photography-based three-dimensional (3D) modeling technology is used to quickly build a 3D model of the study area (including the hydraulic engineering facilities). A multisource spatio-temporal data platform for dynamically simulating flood risk was built based on the digital earth platform. Using the spatio-temporal computation framework, a dynamic visual simulation and decision support system for flood risk management was developed for the Xiashan Reservoir. The integration method proposed here was verified using flood simulation calculations, dynamic visual simulations, and downstream river channel and dam-break flood simulations. The results show that the proposed methods greatly improve the efficiency of flood risk simulation and decision support. The methods and system put forward in this study can be applied to flood risk simulations and practical management.

show abstract

Study and Analysis of Chennai Flood 2015 Using GIS and Multicriteria Technique

Cited by 22 publications

References 3 publications

Geochemical Footprint of Megacities on River Sediments: A Case Study of the Fourth Most Populous Area in India, Chennai

Geochemical Footprint of Megacities on River Sediments: A Case Study of the Fourth Most Populous Area in India, Chennai

Quick Predictions of Onset Times and Rain Amounts from Monsoon Showers over Urban Built Environments

Dynamic 3D Simulation of Flood Risk Based on the Integration of Spatio-Temporal GIS and Hydrodynamic Models

Contact Info

Product

Resources

About