Remote sensing and geostatistics in urban water-resource monitoring: a review

Liu, Zhixin; Fan, Yubo; Liu, Mingzhe; Yin, Zhouping; Li, Xuan; Yin, Lirong; Zheng, Wenfeng

doi:10.1071/mf22167

Cited by 140 publications

(49 citation statements)

References 88 publications

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“…A total of three classes were also established for this factor as follows: (−2.82)-(−0.09), 1, 1-2.67 (Figure 3d). Further morphometric flood predictor is the TWI, which provides good insight into the flow accumulation process at the top of the terrain Liu et al, 2023;. As a result of this index, values can be classified into the following five groups: 3.…”

Section: Flood Predictorsmentioning

confidence: 99%

Flood hazard potential evaluation using decision tree state‐of‐the‐art models

et al. 2023

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Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research. With the development of modern computerized methods such as geographic information system and machine learning models, and as a result of the possibility of combining them, the determination of areas susceptible to floods has become increasingly accurate, and the algorithms used are increasingly varied. Some of the most used and highly accurate machine learning algorithms are the decision tree models. Therefore, in the present study focusing on flood susceptibility zonation mapping in the Trotus River basin, the following algorithms were applied: forest by penalizing attribute—weights of evidence (forest‐PA‐WOE), best first decision tree—WOE, alternating decision tree—WOE, and logistic regression—WOE. The best performant, characterized by a maximum accuracy of 0.981, proved to be forest‐PA‐WOE, whereas in terms of flood exposure, an area of over 16.22% of the Trotus basin is exposed to high and very high floods susceptibility. The performances applied models in the present work are higher than the models applied in the previous studies in the same study area. Moreover, it should be noted that the accuracy of the models is similar with the accuracies of the decision tree models achieved in the studies focused on other areas across the world. Therefore, we can state that the models applied in the present research can be successfully used in by the researchers in other case studies. The findings of this research may substantially map the flood risk areas and further aid watershed managers in limiting and remediating flood damage in the data‐scarce regions. Moreover, the results of this study can be a very useful for the hazard management and planning authorities.

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Section: Flood Predictorsmentioning

confidence: 99%

Flood hazard potential evaluation using decision tree state‐of‐the‐art models

et al. 2023

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“…Also, the liDAR-DEM (light detection and ranging-DEM) to the DEM was created using 1 × 1 m horizontal resolution and 0.2 m vertical precision from data collected using airborne laser scanning (ALS) [44][45][46][47][48]. In addition, remote sensing allows for massive mountain ranges, deep valleys, and ocean floor magnetic striping [49].…”

Section: Digital Elevation Modellingmentioning

confidence: 99%

Probabilistic Slope Stability Analysis of Mount St. Helens Using Scoops3D and a Hybrid Intelligence Paradigm

Kumar,

Choudhary,

Burman

et al. 2023

Mathematics

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In the past, numerous stratovolcanoes worldwide witnessed catastrophic flank collapses. One of the greatest risks associated with stratovolcanoes is a massive rock failure. On 18 May 1980, we witnessed a rock slope failure due to a volcano eruption, and a 2185.60 m high rock slope of Mount St. Helens was collapsed. Thus, from the serviceability perspective, this work presents an effective computational technique to perform probabilistic analyses of Mount St. Helens situated in Washington, USA. Using the first-order second-moment method, probability theory and statistics were employed to map the uncertainties in rock parameters. Initially, Scoops3D was used to perform slope stability analysis followed by probabilistic evaluation using a hybrid computational model of artificial neural network (ANN) and firefly algorithm (FF), i.e., ANN-FF. The performance of the ANN-FF model was examined and compared with that of conventional ANN and other hybrid ANNs built using seven additional meta-heuristic algorithms. In the validation stage, the proposed ANN-FF model was the best-fitted hybrid model with R2 = 0.9996 and RMSE = 0.0042. Under seismic and non-seismic situations, the reliability index and the probability of failure were estimated. The suggested method allows for an effective assessment of the failure probability of Mount St. Helens under various earthquake circumstances. The developed MATLAB model is also attached as a supplementary material for future studies.

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“…Remote sensing technology plays a key role in real-world applications, e.g., agriculture, 4,5 urban planning, 6,7 environmental monitoring. 8,9 Due to the limitations of the acquisition environment and equipment, remote sensing satellite platforms usually orbit around the earth at a high speed, and the possible movement of the target object. It is easy to cause the motion of the scene during the image acquisition, which often results in motion blur in remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Deblurring method for remote sensing image via dual scale parallel spatial fusion network

An,

Chen,

Wang

et al. 2024

J. Appl. Rem. Sens.

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Image deblurring has the goal of restoring a sharp image from a degraded one. Currently, most deblurring methods are designed for natural images; these methods may not perform well when applied to remote sensing images. There are many differences between remote sensing and natural images, e.g., shooting distance, content complexity, and clarity. Therefore, a blind motion deblurring method specifically designed for remote sensing images called dual scale parallel spatial fusion network (DSPF-Net) is proposed. It has three innovative aspects: a dual-scale connection module is added between the two scales of the bottleneck layer and the decoder to realize the fusion of spatial detail and semantic features. Second, an adaptive spatial selection module is designed, which adds the function of selecting global and local spatial features. Finally, the cross-scale fusion (CSF) module is designed to restore the edge details and main structures by fusing the multi-scale features between the encoder and decoder. Extensive experiments are established on the synthetic dataset Blur-RS, the averaged peak signal-to-noise ratio and structural similarity are improved by 0.7916% and 0.0265%, respectively, compared to the best-performing comparison method. It shows that DSPF-Net has advantages in the task of blind motion deblurring of remote sensing images.

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Remote sensing and geostatistics in urban water-resource monitoring: a review

Cited by 140 publications

References 88 publications

Flood hazard potential evaluation using decision tree state‐of‐the‐art models

Flood hazard potential evaluation using decision tree state‐of‐the‐art models

Probabilistic Slope Stability Analysis of Mount St. Helens Using Scoops3D and a Hybrid Intelligence Paradigm

Deblurring method for remote sensing image via dual scale parallel spatial fusion network

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