Flood inundation evolution of barrier lake and evaluation of regional ecological spatiotemporal response—a case study of Sichuan-Tibet region

Jiang, Fengshan; Dai, Xiaoai; Xie, Zhiqiang; Xu, Tao; Yin, Siqiao; Qu, Ge; Yang, Shouquan; Zhang, Yanbin; Yang, Zhibing; Xu, Jiarui; Hou, Zhiqun

doi:10.1007/s11356-022-20866-y

Cited by 9 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies by [ 15 , 24 , 102 , 105 , 106 , 107 ] highlight the numerous benefits of self-calibration techniques. These advantages include real-time adjustment capabilities, allowing for continuous monitoring and adjustment of measurement equipment to maintain accuracy over time.…”

Section: Self-calibration Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors

Nalakurthi,

Abimbola,

Ahmed

et al. 2024

Sensors

View full text Add to dashboard Cite

The use of low-cost environmental sensors has gained significant attention due to their affordability and potential to intensify environmental monitoring networks. These sensors enable real-time monitoring of various environmental parameters, which can help identify pollution hotspots and inform targeted mitigation strategies. Low-cost sensors also facilitate citizen science projects, providing more localized and granular data, and making environmental monitoring more accessible to communities. However, the accuracy and reliability of data generated by these sensors can be a concern, particularly without proper calibration. Calibration is challenging for low-cost sensors due to the variability in sensing materials, transducer designs, and environmental conditions. Therefore, standardized calibration protocols are necessary to ensure the accuracy and reliability of low-cost sensor data. This review article addresses four critical questions related to the calibration and accuracy of low-cost sensors. Firstly, it discusses why low-cost sensors are increasingly being used as an alternative to high-cost sensors. In addition, it discusses self-calibration techniques and how they outperform traditional techniques. Secondly, the review highlights the importance of selectivity and sensitivity of low-cost sensors in generating accurate data. Thirdly, it examines the impact of calibration functions on improved accuracies. Lastly, the review discusses various approaches that can be adopted to improve the accuracy of low-cost sensors, such as incorporating advanced data analysis techniques and enhancing the sensing material and transducer design. The use of reference-grade sensors for calibration and validation can also help improve the accuracy and reliability of low-cost sensor data. In conclusion, low-cost environmental sensors have the potential to revolutionize environmental monitoring, particularly in areas where traditional monitoring methods are not feasible. However, the accuracy and reliability of data generated by these sensors are critical for their successful implementation. Therefore, standardized calibration protocols and innovative approaches to enhance the sensing material and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data.

show abstract

Section: Self-calibration Techniquesmentioning

confidence: 99%

“…However, linear regression cannot model this relationship since several non-linear and environmental variables can affect the accuracy of low-cost sensors [ 29 ]. Thus, to truly account for these variables, machine learning techniques may prove very useful [ 46 , 68 , 107 ].…”

Section: Calibration Techniques and Developments In Their Accuracymentioning

confidence: 99%

Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors

Nalakurthi,

Abimbola,

Ahmed

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…It is also the largest single project for post-earthquake restoration and reconstruction in Ludian City. The Hongshiyan dammed lake renovation project is an important project to control water hazards in China, and it is also one of the representative and important projects in the international water conservancy and hydropower industry [30][31][32]. The overview of the study area is shown in Figure 2.…”

Section: Study Areamentioning

confidence: 99%

An Improved Combination Model for the Multi-Scale Prediction of Slope Deformation

Lei

Qin

et al. 2022

Water

View full text Add to dashboard Cite

Slope collapse is one of the most severe natural disaster threats, and accurately predicting slope deformation is important to avoid the occurrence of disaster. However, the single prediction model has some problems, such as poor stability, lower accuracy and data fluctuation. Obviously, it is necessary to establish a combination model to accurately predict slope deformation. Here, we used the GFW-Fisher optimal segmentation method to establish a multi-scale prediction combination model. Our results indicated that the determination coefficient of linear combination model, weighted geometric average model, and weighted harmonic average model was the highest at the surface spatial scale with a large scale, and their determination coefficients were 0.95, 0.95, and 0.96, respectively. Meanwhile, RMSE, MAE and Relative error were used as indicators to evaluate accuracy and the evaluation accuracy of the weighted harmonic average model was the most obvious, with an accuracy of 5.57%, 3.11% and 3.98%, respectively. Therefore, it is necessary to choose the weighted harmonic average model at the surface scale with a large scale as the slope deformation prediction combination model. Meanwhile, our results effectively solve the problems of the prediction results caused by the single model and data fluctuation and provide a reference for the prediction of slope deformation.

show abstract

“…Annually, thousands of people lose their lives globally due to landslide disasters, resulting in economic losses ranging from $4 to $9 billion [3]. Secondary disasters triggered by landslides, such as mudslides and dammed lakes, further contribute to casualties and economic damages [4,5]. Therefore, it is crucial to provide timely and accurate landslide inventories and analyze landslide susceptibility in the affected regions.…”

Section: Introductionmentioning

confidence: 99%

Re-Net: Multibranch Network With Structural Reparameterization for Landslide Detection in Optical Imagery

Zhang,

Zhu,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Deep learning techniques have been widely adopted in landslide detection by offering powerful feature extraction capabilities and automated processes. However, the pursuit of higher accuracy has led to increasingly complex network structures, which limits the efficiency of models in landslide detection. To tackle this challenge, we have developed a dynamic module, called Five-branch Feature Extraction Module (FFEM), based on the theory of structural reparameterization. This module is designed to reconstruct the encoder of the U-shaped network. Our novel network, Re-Net, effectively integrates information from multiple scales during training by utilizing its multi-branch structure, which is facilitated by the FFEM. During inference, leveraging the structural reparameterization, the FFEM in Re-Net transforms as a convolutional layer, achieving an impressive 52.9% reduction in parameters, and 34.98% reduction in FLOPs. the efficiency improvement of Re-Net does not come at the expense of sacrificing landslide recognition accuracy. In the public dataset (Bijie dataset), Re-Net achieved improvements of 2.81% in IoU(Intersection over Union) and 1.93% in F1-Score. In post-earthquake landslide detection tasks (Luding Dataset), Re-Net exhibited respective improvements of 2.29% and 1.52%. Moreover, in the task of Landslide Detection, Re-Net demonstrates superior segmentation accuracy compared to other CNNs, such as Unet++. when compared to other reparameterization modules, FFEM shows significant improvements in IoU and F1-Score in the Bijie dataset, with an average increase of 0.65% and 0.45%, respectively. Similarly, in the Luding dataset, FFEM demonstrates average improvements of 1.56% in IoU and 1.04% in F1-Score.

show abstract

Flood inundation evolution of barrier lake and evaluation of regional ecological spatiotemporal response—a case study of Sichuan-Tibet region

Cited by 9 publications

References 11 publications

Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors

Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors

An Improved Combination Model for the Multi-Scale Prediction of Slope Deformation

Re-Net: Multibranch Network With Structural Reparameterization for Landslide Detection in Optical Imagery

Contact Info

Product

Resources

About