Prediction of the flooding area of the northeastern Caspian Sea from satellite images

Kamza, Anzhelika T.; Kuznetsova, Irina A.; Levin, Eugene

doi:10.1016/j.geog.2022.08.003

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…The data quality is influenced by spatial resolution and such factors as atmospheric and ionospheric interference, vegetation, snow cover etc. [17], [47], [48]. The images were processed using a method of sequential interferometry of permanent scatterers of the radar signal (PS) more than two times using different processing parameters in the Sarproz software (from 2019 to 2021).…”

Section: Sar Interferometrymentioning

confidence: 99%

Studying sinkholes of the earth’s surface involving radar satellite interferometry in terms of Zhezkazgan field, Kazakhstan

Baltiyeva,

Orynbassarova,

Zharaspaev

et al. 2023

Min. miner. depos.

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Purpose is to assess the potential of radar satellite interferometry (SAR interferometry) to analyze and forecast earth’s surface displacements. Methods. The study involves the analysis of previous instrumental observations, such as high-precision levelling and seismic monitoring. The observations using global navigation satellite systems (GNSS) were carried out; satellite images were processed applying a method of sequential interferometry of persistent radar signal scatters (PS). Findings. The research results have proved similarity between the data obtained with the help of ground instrumental methods and the data received using satellite interferometry. Two types of the earth’s surface sinkholes were identified: sinkholes with smooth subsidence and hidden deformations that are not accompanied by the preliminary deformational or seismic signs. Smooth subsidence is controlled and predicted successfully with the help of the SAR interferometry methods. An algorithm has been represented to predict linear displacement trends at different time intervals involving a finite element method. Originality is in a complex approach of the research performance covering the following: comparative analysis of different monitoring methods, studying different sinkhole types, identifying limitations of the available methods, and proposing new approaches for more accurate and objective analysis of the earth’s surface deformation within the field. Practical implications. The research results are of practical value for rock mechanic specialists and mining operators. They can use the data to monitor and control earth’s surface caving as well as provide staff safety and preserve ground infrastructure where it is possible.

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Section: Sar Interferometrymentioning

confidence: 99%

Studying sinkholes of the earth’s surface involving radar satellite interferometry in terms of Zhezkazgan field, Kazakhstan

Baltiyeva,

Orynbassarova,

Zharaspaev

et al. 2023

Min. miner. depos.

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“…In 2022, Kamza et al [23] developed remote sensing and geographic information system (GIS) technology to examine the changes in the northeastern Caspian Sea coastline and forecast the severity of flooding with rising water levels. The proposed method (remote sensing and GIS) for making dynamic maps was being used to track the coastline and predict how much flooding would occur in a certain area.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimized Deep Learning Model for Flood Detection Using Satellite Images

Stateczny,

Praveena,

Krishnappa

et al. 2023

Remote Sensing

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The increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection models, deep learning techniques are extensively used in flood control. Therefore, a novel deep hybrid model for flood prediction (DHMFP) with a combined Harris hawks shuffled shepherd optimization (CHHSSO)-based training algorithm is introduced for flood prediction. Initially, the input satellite image is preprocessed by the median filtering method. Then the preprocessed image is segmented using the cubic chaotic map weighted based k-means clustering algorithm. After that, based on the segmented image, features like difference vegetation index (DVI), normalized difference vegetation index (NDVI), modified transformed vegetation index (MTVI), green vegetation index (GVI), and soil adjusted vegetation index (SAVI) are extracted. The features are subjected to a hybrid model for predicting floods based on the extracted feature set. The hybrid model includes models like CNN (convolutional neural network) and deep ResNet classifiers. Also, to enhance the prediction performance, the CNN and deep ResNet models are fine-tuned by selecting the optimal weights by the combined Harris hawks shuffled shepherd optimization (CHHSSO) algorithm during the training process. This hybrid approach decreases the number of errors while improving the efficacy of deep neural networks with additional neural layers. From the result study, it clearly shows that the proposed work has obtained sensitivity (93.48%), specificity (98.29%), accuracy (94.98%), false negative rate (0.02%), and false positive rate (0.02%) on analysis. Furthermore, the proposed DHMFP–CHHSSO displays better performances in terms of sensitivity (0.932), specificity (0.977), accuracy (0.952), false negative rate (0.0858), and false positive rate (0.036), respectively.

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Determination of Caspian Sea Coastal Changes Using Remote Sensing and Geographic Information Systems

Safarov,

Sertel

2023

2023 10th International Conference on Recent Advances in Air and Space Technologies (RAST)

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Prediction of the flooding area of the northeastern Caspian Sea from satellite images

Cited by 4 publications

References 5 publications

Studying sinkholes of the earth’s surface involving radar satellite interferometry in terms of Zhezkazgan field, Kazakhstan

Studying sinkholes of the earth’s surface involving radar satellite interferometry in terms of Zhezkazgan field, Kazakhstan

Optimized Deep Learning Model for Flood Detection Using Satellite Images

Determination of Caspian Sea Coastal Changes Using Remote Sensing and Geographic Information Systems

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