Chennai Floods Research Articles

A contrastive topic-aware attentive framework with label encodings for post-disaster resource classification

Social media has emerged as a critical platform for disseminating real-time information during disasters. However, extracting actionable resource data, such as needs and availability, from this vast and unstructured content remains a significant challenge, leading to delays in identifying and allocating resources, with severe consequences for affected populations. This study addresses this challenge by investigating the potential of label and topic features, combined with text embeddings, to enhance the performance and efficiency of resource identification from social media data. We propose Crisis Resource Finder (CRFinder), a novel framework that leverages label encoding and topic features to extract richer contextual information, uncover hidden patterns, and reveal the true context of disaster resources. CRFinder incorporates novel techniques such as multi-level text-label attention and contrastive text-topic attention to capture semantic and thematic nuances within the textual data. Additionally, our approach employs topic injection and selective contextualization techniques to enhance thematic relevance and focus on critical information, which is pivotal for targeted relief efforts. Extensive experiments demonstrate the significant improvements achieved by CRFinder over existing state-of-the-art methods, with average weighted F1-score gains of 7.12%, 6.44%, and 7.89% on datasets from the Nepal earthquake, Italy earthquake, and Chennai floods, respectively. By providing timely and accurate insights into resource needs and availabilities, CRFinder has the potential to revolutionize disaster response efforts.

A Comparative Study on 2015 and 2023 Chennai Flooding: A Multifactorial Perspective

Floods are highly destructive natural disasters. Climate change and urbanization greatly impact their severity and frequency. Understanding flood causes in urban areas is essential due to significant economic and social impacts. Hydrological data and satellite imagery are critical for assessing and managing flood effects. This study uses satellite images, climate anomalies, reservoir data, and cyclonic activity to examine the 2015 and 2023 floods in Chennai, Kanchipuram, and Thiruvallur districts, Tamil Nadu. Synthetic-aperture radar (SAR) satellite data were used to delineate flood extents, and this information was integrated with reservoir data to understand the hydrological dynamics of floods. The classification and regression tree (CART) model delineates flood zones in Chennai, Kanchipuram, and Thiruvallur during the flood years. The study region is highly susceptible to climatic events such as monsoons and cyclones, leading to recurrent flooding. The region’s reservoirs discharged floodwaters exceeding 35,000 cubic meters per second in 2015 and 15,000 cubic meters per second in 2023. Further, the study examines the roles of the Indian Ocean Dipole (IOD), which reached its peak values of 0.33 and 3.96 (positive IOD), and El Niño in causing floods here. The complex network of waterways and large reservoirs poses challenges for flood management. This research offers valuable insights for improving the region’s flood preparedness, response strategies, and overall disaster management.

Role of Natural Resource Conservation and Disaster Management in Flood Mitigation Measures: A Case Study of Chennai Flood

Role of Natural Resource Conservation and Disaster Management in Flood Mitigation Measures: A Case Study of Chennai Flood

Consumption response to a natural disaster: Evidence of price and income shocks from Chennai flood

We utilize monthly individual-level financial data and item-level supermarket sales data to study how consumption responds to one of the costliest natural disasters in India. We find that consumption dropped by 11% during the disaster, 65% of which was recovered after the disaster. On average, consumption per capita dropped by $312 per year, which costs about 5% of the GDP. We also show that natural disasters depressed consumption through income shocks instead of price shocks. Consumers smooth consumption using credit card, banks loans and wealth in coping with the shocks.

Unravelling the causes of 2015 winter monsoon extreme rainfall and floods over Chennai: Influence of atmospheric variability and urbanization on the hydrological cycle

The present study investigates the causes of extreme precipitation events over the coastal Chennai city in India during the winter monsoon season of 2015. Our investigation reveals that atmospheric and anthropogenic variability influenced the hydrological cycle in producing the Chennai floods. Atmospheric perspective linked the extreme rainfall to the natural variability over tropics in the form of convectively coupled equatorial waves (CCEWs). This finding is entirely new compared to Chennai floods' earlier attribution to Bay of Bengal warming and El Nino related easterly bursts. Along with the natural atmospheric variability, anthropogenic variability due to unprecedented urbanization multiplied the catastrophe scale. A prominent land cover change over the Chennai region was found in 2015 with respect to 1992 land cover and land use classification satellite dataset. A kilometre-scale regional climate simulation clearly demonstrated the urbanization impact on the extreme rainfall over Chennai. Also, we found unprecedented changes in hydrologic conditions like surface discharge of rivers in 2015 over the Adyar basin of Chennai using the Global Flood Awareness System simulated dataset. We revealed a daily mean surface discharge >1000 m 3 s − 1 of rivers at every 100 km 2 area over the Chennai city after the extreme rainfall events.

POS-052 CLINICAL PROFILE AND OUTCOME OF ACUTE KIDNEY INJURY DURING AN OUTBREAK OF ACUTE GASTROENTERITIS FOLLOWING MONSOON FLOODS IN CHENNAI CITY, INDIA

Acute Gastroenteritis (AGE) is one of the important causes of acute kidney injury (AKI) in tropical regions. Climatic and socioeconomic conditions predispose to high incidence of infectious acute gastroenteritis in tropical countries. A sudden surge of acute gastroenteritis presenting with AKI was seen following Chennai floods which occurred recently in November 2021. Here we present the clinical profile and patient outcome of AKI occurring due to acute gastroenteritis

Consumption Response to a Natural Disaster: Evidence of Price and Income Shocks from Chennai Flood

Consumption Response to a Natural Disaster: Evidence of Price and Income Shocks from Chennai Flood

Chennai Floods 2021: Sentiment Analysis of Twitter Data using Tweepy and TextBlob

Abstract: The rise in the usage of Twitter for the exclamation of the problems worldwide and also as a ‘review system,’ where the customers can directly hold an entity responsible in front of the public by tweeting and tagging them, gives them immense power and counts towards being an advantage for researchers to analyze such data that can be scraped and used through APIs for a variety of purposes. Through this research, our motive is to analyze the 2021 Chennai floods with data sourced from twitter to understand the public sentiment during the 14-day span. The same is achieved with the help of Tweepy to authenticate data extraction from Twitter and TextBlob, for the classification of sentiment tags - positive, negative, and neutral. The result of this study focuses on the visualization of our findings, with various charts and metrics indicating the sentiment of the tweets we have scraped and analyzed. Keywords: Sentiment Analysis, WordCloud, Subjectivity, Polarity, Chennai Floods

Near real time flood inundation mapping using social media data as an information source: a case study of 2015 Chennai flood

During and just after flash flood, data regarding water extent and inundation will not be available as the traditional data collection methods fail during disasters. Rapid water extent map is vital for disaster responders to identify the areas of immediate need. Real time data available in social networking sites like Twitter and Facebook is a valuable source of information for response and recovery, if handled in an efficient way. This study proposes a method for mining social media content for generating water inundation mapping at the time of flood. The case of 2015 Chennai flood was considered as the disaster event and 95 water height points with geographical coordinates were derived from social media content posted during the flood. 72 points were within Chennai and based on these points water extent map was generated for the Chennai city by interpolation. The water depth map generated from social media information was validated using the field data. The root mean square error between the actual water height data and extracted social media data was ± 0.3 m. The challenge in using social media data is to filter the messages that have water depth related information from the ample amount of messages posted in social media during disasters. Keyword based query was developed and framed in MySQL to filter messages that have location and water height mentions. The query was validated with tweets collected during the floods that hit Mumbai city in July 2019. The validation results confirm that the query reduces the volume of tweets for manual evaluation and in future will aid in mapping the water extent in near real time at the time of floods.

Making Indian cities climate and water resilient

The article highlights flood risk and governance challenges in India. With reference to the recent Chennai floods, it elaborates on past mistakes and the way forward to improve the resilience of Indian cities to manage climate disasters.

Consumption Response to a Natural Disaster: Evidence of Price and Income Shocks from Chennai Flood

We utilize monthly individual-level financial data and item-level supermarket sales data to study how consumption responds to one of the costliest natural disasters in India. We find that consumption dropped by 11% during the disaster, 65% of which was recovered after the disaster. On average, consumption per capita dropped by $312 per year, which costs about 5% of the GDP. We also show that natural disasters depressed consumption through income shocks instead of price shocks. Consumers smooth consumption using credit card, banks loans and wealth in coping with the shocks.

Spatio-temporal distribution of negative emotions on Twitter during floods in Chennai, India, in 2015: a post hoc analysis

BackgroundNatural disasters are known to take their psychological toll immediately, and over the long term, on those living through them. Messages posted on Twitter provide an insight into the state of mind of citizens affected by such disasters and provide useful data on the emotional impact on groups of people. In 2015, Chennai, the capital city of Tamil Nadu state in southern India, experienced unprecedented flooding, which subsequently triggered economic losses and had considerable psychological impact on citizens. The objectives of this study are to (i) mine posts to Twitter to extract negative emotions of those posting tweets before, during and after the floods; (ii) examine the spatial and temporal variations of negative emotions across Chennai city via tweets; and (iii) analyse associations in the posts between the emotions observed before, during and after the disaster.MethodsUsing Twitter’s application programming interface, tweets posted at the time of floods were aggregated for detailed categorisation and analysis. The different emotions were extracted and classified by using the National Research Council emotion lexicon. Both an analysis of variance (ANOVA) and mixed-effect analysis were performed to assess the temporal variations in negative emotion rates. Global and local Moran’s I statistic were used to understand the spatial distribution and clusters of negative emotions across the Chennai region. Spatial regression was used to analyse over time the association in negative emotion rates from the tweets.ResultsIn the 5696 tweets analysed around the time of the floods, negative emotions were in evidence 17.02% before, 29.45% during and 11.39% after the floods. The rates of negative emotions showed significant variation between tweets sent before, during and after the disaster. Negative emotions were highest at the time of disaster’s peak and reduced considerably post disaster in all wards of Chennai. Spatial clusters of wards with high negative emotion rates were identified.ConclusionsSpatial analysis of emotions expressed on Twitter during disasters helps to identify geographic areas with high negative emotions and areas needing immediate emotional support. Analysing emotions temporally provides insight into early identification of mental health issues, and their consequences, for those affected by disasters.

Planning the 2015 Chennai floods

This paper approaches the floods of 2015 in Chennai, Tamil Nadu, India, as the consequence of policies, plans and procedures that, over many years, had erased monsoon water and wetness from the city and its imaginary. In order to do this, it examines a number of plans that authorized spatial development in Chennai from the early 20th century onwards. It approaches them as urban cosmograms, in which heterogeneous entities were accommodated, congealed, concealed or expelled in the description of the urban territory and the composition of the urban world. The paper undertakes this analysis in order to deepen understanding of the relations between spatial planning, capitalist urbanization and the more-than-human vitalities of the monsoon. It approaches the flood waters that rose and fell in 2015 as a cosmopolitical situation and cause for thinking, which, putting people in the presence of the monsoon and its potency in new ways, forced them to confront the precariousness of their co-existence with it and experiment with ways to re-compose the urban monsoonal world differently. This discussion draws from Stenger’s notion of cosmopolitics as a mode of collective practice that proceeds in the company of those who would otherwise be likely to be disqualified as having idiotically nothing to propose, including the more-than-human. The paper makes some critical observations about these experiments and concludes by speculating on whether planning itself might be envisaged as a more inclusive, cosmopolitical project.

On the Recent Floods in India

Floods in the Indian subcontinent have affected habitat, population, economy, etc. Due to the detrimental effects of recent floods on the economy, governance, etc., it is imperative to understand the associated dynamics, manifestations and fallouts for proper policy planning recommendations. The present study endeavours to provide an integrated rationale of meteorological and geomorphological aspects associated with four recent extreme floods in Uttarakhand (2013), Srinagar (2014), Chennai (2015) and Gujarat (2017). It is important to mention here that these floods occurred under different atmospheric circulations and geomorphological setting, and had an entirely different gambit for policy planning and governance. Consolidation of these issues will help policy planners and technologists, in case advance warning system based on these findings can be developed.

Impact of urban sprawl on future flooding in Chennai city, India

The 2015 Chennai (India) flood event claimed many lives and incurred an economic loss of $2.2 billion. Simulation of such a destructive flood event in an ever growing urban sprawl and changing climate is of great importance for better flood preparedness and management of the city. In that light, this study aims to (a) study the 2015 Chennai flood event and (b) simulate the occurrence of a similar event in future, considering both urban sprawl and increased rainfall resulting from climate change effects. The baseline scenario is generated using freely available hydrologic (HEC-HMS)-hydraulic (HEC-RAS) models and the results are validated with observed flood-mark data. Historical land use and land cover maps of the study area are used to train an Artificial Neural Network model for constructing spatially varying urban sprawl scenarios. The hydrologic model is run for the increased rainfall and urban sprawl scenarios to generate flood hydrographs, which are then used as inputs to the hydraulic model for simulating possible flood inundation patterns. The simulations indicate that the average increase in inundation extent considering the worst possible urban sprawl solely, and that taken together with extreme rainfall, are 20 and 33 percent, respectively. Furthermore, the analysis shows that on an average 1.7 times more number of buildings are exposed to flood hazard under the worst case combined scenario in comparison to the baseline scenario. This study, therefore, demonstrates the importance of considering urban sprawl, in addition to changes in rainfall, for evaluating the worst possible consequences of flooding of a city. Considering the uncertainties involved in the study, the presented results may be considered as “what-if” scenarios for planning suitable flood mitigation measures for the city of Chennai.

Facebook and Whatsapp as disaster management tools during the Chennai (India) floods of 2015

The digital world we live in has transformed the way we communicate, network, seek help, access information, gain knowledge, and has shaped every aspect of our lives. A wide choice of communication platforms facilitates our indulgence from texting to posting on social media, which allows us to transcend geographic limitations. Natural disasters necessitate immediate communication to know the well-being of the people concerned and to seek rescue and relief measures. One such ‘black swan’ event was the Chennai floods of 2015, in south India, where social media such as Facebook and WhatsApp became disaster management tools for social activism. This research aims to analyse how Facebook and WhatsApp were used in the management of the Chennai floods of 2015, particularly by residents of Kotturpuram and Mudichur, two of the worst-affected areas in the city. The study used a quantitative approach, carrying out a survey with judgement sampling (n = 400). The satisfaction level of using Facebook and WhatsApp among the residents of Kotturpuram and Mudichur were analyzed. The factors of sense of empowerment – information, real-time operational information, emotional appeal, situational updates, and trustworthiness – were further analyzed. The implication of social media over traditional media and how those affected gained influence and power to set reverse agenda through social media during the Chennai floods of 2015 was also investigated. The use of both Facebook and WhatsApp increased and even surpassed the use of more conventional tools of communication such as radio and television during the Chennai floods. Apart from being mere tools for communication, social media facilitated disaster management during the floods. Our analysis reveals that the information sourced from Facebook and WhatsApp chats can be an eye-opener to specific areas of resource needs and gaps in resource distribution which will help in decision-making in real disasters.

Decasticization, Dignity, and ‘Dirty Work’ at the Intersections of Caste, Memory, and Disaster

ABSTRACT:In this qualitative study we examine the role of caste, class, and Dalit janitorial labor in the aftermath of floods in Chennai, India, in 2015. Drawing from a variety of sources including interviews, social media, and news coverage, we studied how Dalit (formerly known as ‘untouchable’) janitors were treated during the performance of janitorial labor for cleaning the city. Our study focuses on two theoretical premises: (a) caste-based social relations reproduce inequalities by devaluing Dalit labor as ‘dirty work’; and (b) Dalit subjectivities, labor, and sufferings including occupational hazards become invisible and ungrievable forcing Dalits to provide a counter narrative to preserve the memory of their trauma and dignity injuries. We find that the discursive construction of janitorial labor as dirty work forced Dalit janitors to work in appalling and unsafe working conditions. Janitors suffered several dignity injuries in terms of social exclusion and a lack of recognition for their efforts and accomplishments. Specifically, we examine various ways through which caste, dirty work, and dignity intersected in the narrative accounts of Dalit janitors. We also explore memory and how processes of remembering and forgetting affected the dignity claims of Dalit janitors.

Resilient Cities for the Future :A case of Chennai

Fast pace of urbanisation and population growth is an imminent global challenge. The World Urbanisation Prospects of the United Nations suggest that more than 75% of the global population will concentrate in cities by the year 2050. The pressure of this population rise on our cities and the natural systems are bound to increase in the near future. Floods, cyclones, earthquakes, wildfires and heat waves made the year 2015 a devastating one for millions around the world, with 150 major natural disasters being recorded. Asia bore the brunt of these disasters with massive earthquakes in Nepal, floods in Chennai, heat waves hitting South India and Typhoon Komen inundating the Indian subcontinent, to cite a few examples. The changes in climate and the failures of physical systems make our cities vulnerable to disasters of various kinds leading to physical collapse of the city. This paper deals with the concept of disaster resilience in this context and how this can be applied at the city, neighbourhood as well as the individual level. The intent of the paper is to develop a framework of strategies for an emergency response programme, taking the case of the Indian city of Chennai. The initial approach surveys in detail the way the city functions with respect to the natural systems and looks into the city’s growth through the ages. The paper proposes an initial theory around the creation of an ideal emergency response model consisting of physical and technological networks that will come into play once a risk situation arises within a city. This model was first produced at a generic level where it can be applied on to any city of any context. The paper articulates the model in the city of Chennai considering its intricate labyrinth and functions.

Remote sensing and GIS in the recent Chennai flood study

Flood in Chennai is still indelible in the minds of the people. The most anguish and torment nature of affected people is colossal despair to the society and panic worldwide. This paper focuses on prediction of runoff in Chennai Adyar watershed, Tamil Nadu in India during the storm events in 2005 and 2015 floods. The runoff water available for artificial recharge in the basin is estimated through Soil Conservation Service (SCS) Curve number method. The land use/land cover and hydrologic soil classification are processed in ArcGIS for each of the 19 micro-watersheds and the volume of runoff is estimated. This paper represents the rainfall–runoff in that particular micro-watershed level, and curve fitting relationship of rainfall–runoff is shown. The remote sensing and GIS play a vital role in the study, and the results were obtained. The total volumes of rainfall drained as runoff are 326.67 million m3, 280.99 million m3 and 207.69 million m3 in October, November and December 2005 flood, respectively, and 71.33 million m3, 681.88 million m3 and 312.92 million m3 in October, November and December 2015 flood, respectively. This runoff results will create awareness to save the excess water flow during flood season and be helpful for planning the artificial groundwater recharge. Thus, some useful management measures have to be implemented at the earliest to avoid the impacts of Chennai floods.

A Study of Near-Surface Boundary Layer Characteristics During the 2015 Chennai Flood in the Context of Urban-Induced Land Use Changes

Chennai metropolitan region along with the northern part of Tamil Nadu and southern part of Andhra Pradesh witnessed extreme rainfall events leading to urban flooding during November–December 2015. In order to understand the near-surface and boundary layer (BL) characteristics during the event in the context of land use and land cover (LULC) change, three decades of satellite images are analysed. In addition, the Weather Research Forecasting (WRF) model is used to perform finer-scale simulations, considering different land use (LU) data sets as input. For this purpose, LU datasets from the United Sates Geological Survey (USGS), Moderate Resolution Imaging Spectro-radiometer (MODIS), and Indian Space Research Organization (ISRO) are considered along with the Noah and Noah multi-physics (NMP) land surface model (LSM). Impact of Noah-based LSMs on the near-surface and BL characteristics over Chennai are examined besides the change in LULC during the flood event. Significant improvement of 1–2 °C is obtained in case of near-surface temperature in the simulation considering recent LU and the NMP LSM. Some WRF-simulated variables like near-surface temperature, relative humidity (RH) and convective available potential energy (CAPE) are compared with available observations for qualitative and quantitative analysis. The distorted variations of the near-surface and boundary layer parameters including temperature, BL height, sensible heat flux and CAPE, are mostly observed during phases with prevalent low-pressure systems due to the presence of large-scale forcing. In other phases, (where low-pressure systems are absent), with dominance of localised effects, noticeably higher values of the variables viz. near-surface air temperature, wind speed, RH and moisture flux, CAPE and BL height are attributed to the increased impervious layers inside the city boundary due to urbanization and its growth.