Synergistic Use of Remote Sensing, Gis and Hydrological Models for Study of August 2018 Kerala Floods

Thakur, Praveen K.; Ranjan, Rajeev; Singh, Sachchidanand; Dhote, Pankaj R.; Sharma, Vishal; Srivastav, Vertika; Dhasmana, M. K.; Chauhan, Prakash; Nikam, Bhaskar R.; Garg, Vaibhav; Chouksey, Arpit

doi:10.5194/isprs-archives-xliii-b3-2020-1263-2020

Cited by 10 publications

(9 citation statements)

References 10 publications

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“…used for flood forecasting. Image segmentation has been applied to remotely sense images to determine water fluctuation levels to build an active real-time urban flood warning system [31]. Apart from that, many studies [45,49] have used hydrological models for flood forecasting using remotely sensed data.…”

Section: Discussionmentioning

confidence: 99%

“…This pie graph shows that the use of machine learning and hydrological models has been equally and most frequently observed in the literature for flood forecasting. A significant issue in remote sensing technologies is the constraint regarding orbital cycles and spaces between trajectories of satellites, which makes the continuous monitoring of fixed objects a difficult task [31,71].…”

Section: Discussionmentioning

confidence: 99%

“…Floods, being natural disasters, are uncertain and unpredictable, which makes flood modelling a complex task having numerous uncertainties. Hence, hydrological and nu- A significant issue in remote sensing technologies is the constraint regarding orbital cycles and spaces between trajectories of satellites, which makes the continuous monitoring of fixed objects a difficult task [31,71].…”

Section: Discussionmentioning

confidence: 99%

“…The bagging approach significantly improved the accuracy of the KNN-cube ensemble for flood management and mapping problems. Noymanee [31] experimented with linear regression, ANN, boosted decision trees, Bayesian linear model, and decision forest to forecast floods in the district of Pattani in Thailand [31]. Bayesian Linear model demonstrated the best performance among all selected models, and was, therefore recommended for flood detection.…”

Section: Multispectralmentioning

confidence: 99%

“…In the above equation, M represents the machine learning operation, W is the water level, the symbol * denotes predicted value, TX347 and TX33 are labels of the stations which are assigned to various river portions and R represent the rain value [31]. The flood mapping results in an input multispectral aerial image.…”

Section: Multispectralmentioning

confidence: 99%

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Remote Sensing Methods for Flood Prediction: A Review

Munawar

Hammad

Waller

2022

Sensors

117

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Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country’s economy. Floods, being natural disasters, cannot be prevented completely; therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. To minimize hazards and to provide an emergency response at the time of natural calamity, various measures must be taken by the disaster management authorities before the flood incident. This involves the use of the latest cutting-edge technologies which predict the occurrence of disaster as early as possible such that proper response strategies can be adopted before the disaster. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. Hence, improvement in the adoption of the latest technology to move towards automated disaster prediction and forecasting is a must. This study reviews the adoption of remote sensing methods for predicting floods and thus focuses on the pre-disaster phase of the disaster management process for the past 20 years. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). Further categorization is performed based on the method used for data analysis. The technologies are examined based on their relevance to flood prediction, flood risk assessment, and hazard analysis. Some gaps and limitations present in each of the reviewed technologies have been identified. A flood prediction and extent mapping model are then proposed to overcome the current gaps. The compiled results demonstrate the state of each technology’s practice and usage in flood prediction.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Multispectralmentioning

confidence: 99%

Section: Multispectralmentioning

confidence: 99%

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Remote Sensing Methods for Flood Prediction: A Review

Munawar

Hammad

Waller

2022

Sensors

117

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Land‐Use and Land‐Cover (LULC) Change Detection in Bilari, Moradabad, U.P., India

Singh,

Simalti,

Piyoosh

2024

Macromolecular Symposia

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The post‐globalization of the Indian economy marks the beginning of a new era for various industries, including manufacturing and the service sector. As a result, people begin to migrate from sub‐urban/rural area to nearby major cities in search of employment and access to modern conveniences. Land Use Land Cover (LULC) pattern change is caused by human and natural factors which include geology, elevation, and slope. LULC change assessment is best ways to manage and understand landscape transformation. Bilari block in Moradabad is India's brass city and one of Uttar Pradesh's major cities, its population growth has pushed the rapid urbanization of city in last two decades. In the present study Landsat 7 Enhanced Thematic Mapper (ETM) and Landsat 8 Operational Land Manager (OLI) data for years 2000 and 2023 have been employed to assess. ArcGIS Pro 2.9.0 software is used to perform layer stacking and on the raw Landsat data for pre‐processing. To find LULC changes in the study area between 2000 and 2023, supervised classification is done by adopting Maximum Likelihood Classification (MLC). From the results, it is observed that the area from pervious to impervious changes very quickly in Bilari. This research will aid in regional and urban planning, as well as agricultural management, in the years ahead.

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