Article
Environmental Sciences
Priyom Roy, Tapas R. Martha, Kirti Khanna, Nirmala Jain, K. Vinod Kumar
Summary: This paper presents a novel method for predicting the time and path of landslides using remote sensing data and a new technique. By analyzing ground deformation trends and displacement time series data, the time and flow path of landslides can be predicted. This study is significant for improving the accuracy of landslide early warning in hilly areas.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Remote Sensing
Xin Yao, Yiping Chen, Donglie Liu, Zhenkai Zhou, Veraldo Liesenberg, Jose Marcato Junior, Jonathan Li
Summary: The study utilized the average-DInSAR method to detect displacements on tableland escarpments and confirmed that these movements were induced by underground coal mining. This method proved to be simple and effective for detecting pre-failure displacements in areas with similar geological conditions, aiding in the formulation of early warning strategies for landslides.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Environmental Sciences
Jiehua Cai, Changcheng Wang, Lu Zhang
Summary: This study investigated the spatial-temporal displacement of the Slumgullion landslide using an adaptive pixel offset tracking method with multi-track UAVSAR images. The results revealed the distribution of surface mass depletion or accumulation and provided insights into the resistance and driving forces of the landslide. The study also highlighted the influence of geological structures on mass wasting in the landslide.
Article
Environmental Sciences
Yang Lei, Alex Gardner, Piyush Agram
Summary: This paper presents an efficient feature tracking algorithm for mass processing of satellite images, demonstrating its accuracy and effectiveness in measuring displacements. The study also compares error sources for radar and optical image pairs, and compares estimated velocities with reference data, showing the advantages of the new algorithm.
Article
Engineering, Electrical & Electronic
Xianjian Shi, Yifei Wu, Qing Guo, Ni Li, Zhiyong Lin, Hua Qiu, Bin Pan
Summary: This research introduces a new method called SPAUNet to address the challenges of noise and information redundancy in landslide detection using synthetic aperture radar (SAR) amplitude data. Empirical analysis shows that SPAUNet outperforms baseline models and highlights the importance of choosing the appropriate polarization combination for the region.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Engineering, Aerospace
K. C. Niraj, Sharad Kumar Gupta, Dericks Praise Shukla
Summary: This paper uses DInSAR and MTInSAR techniques to study surface displacement in the Kotrupi Region. DInSAR accurately measures displacements but is limited by various factors, while MTInSAR uses data from multiple periods to reduce atmospheric disturbances and unwrapping errors. The results show significant deformation in the Kotrupi area after a landslide, with MTInSAR providing more accurate results compared to DInSAR.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Environmental Sciences
Raj Sunil Kandregula, Girish Ch Kothyari, K. V. Swamy, Ajay Kumar Taloor, Abhishek Lakhote, Gaurav Chauhan, M. G. Thakkar, Vamdev Pathak, Kapil Malik
Summary: This study aims to estimate surface displacement and understand the active deformation pattern in the Kachchh region post the 2001 Bhuj Earthquake using PSI and DInSAR techniques. Results show high deformation near fault zones, with surface displacement peaking in 2009 post aftershocks, and gradually declining due to ongoing seismic settlement. The findings provide valuable insights for micro zonation studies, mitigation planning, and the preparation of an active tectonic map for the region.
GEOCARTO INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Rui Liu, Feng Wang, Niangang Jiao, Wei Yu, Hongjian You, Fangjian Liu
Summary: This study proposes a radiometric normalization method based on the radiometric principle, which considers the radiometric characteristic of SAR images and optimizes all images as a whole to reduce the radiometric differences between SAR mosaic images.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Engineering, Multidisciplinary
Yin YuePing, Liu XiaoJie, Zhao ChaoYing, Roberto Tomas, Zhang Qin, Lu Zhong, Li Bin
Summary: The translation discusses the importance of multi-dimensional, long-term time series displacement monitoring in generating early warnings for active landslides and mitigating geohazards. It introduces an improved cross-platform SAR offset tracking method that can estimate high-precision landslide displacements in two and three dimensions, as well as calculate long-term time series displacements over a decade. The method optimizes the traditional SAR offset tracking workflow by incorporating ortho-rectification, adaptive matching window, and displacement inversion network design. Mathematical equations are built to estimate the 2D and 3D long-term time series landslide displacements using cross-platform SAR observations. The proposed method is demonstrated using ALOS/PALSAR-1 and ALOS/PALSAR-2 images of the Laojingbian landslide in China, showing significant improvements over traditional methods. The method also allows for understanding the fine-scale landslide kinematics, performing early warning of hazard, and forecasting future displacement evolution.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2022)
Article
Environmental Sciences
Mahdi Khoshlahjeh Azar, Amir Hamedpour, Yasser Maghsoudi, Daniele Perissin
Summary: This study used space-based Synthetic Aperture Radar images to investigate the formation mechanism of sinkholes in two Iranian plains, revealing precursory ground subsidence before sinkhole formation. Time-series analysis showed a gradual acceleration in the sinkhole formation process, with significant ground displacement changes appearing in the days leading up to formation.
Article
Environmental Sciences
Weixian Tan, Yadong Wang, Pingping Huang, Yaolong Qi, Wei Xu, Chunming Li, Yuejuan Chen
Summary: Mine slope landslides pose serious threats to people's lives and property in mining areas. This paper proposes a landslide time prediction method based on the time series monitoring data of micro-deformation monitoring radar. The method calculates deformation displacement, coherence and deformation volume, and the parametric degree of deformation (DOD), and combines them with the tangent angle method. The effectiveness of the method is verified using measured data of a landslide in a mining area, showing that it can provide more reliable landslide prediction results for monitoring personnel.
Article
Construction & Building Technology
Gang Wang, Zheng Fang, Jiren Xie, Na Du
Summary: A new hybrid method for predicting slope surface deformation was proposed in this paper, derived from Wavelet Analysis, Genetic Algorithm, and Elman Algorithm. The method decomposes slope surface deformation into trend and periodic components using a time series model, trained and predicted by the GA-Elman model. The results showed that this method is reliable and accurate for forecasting slope surface deformation during landslides.
ADVANCES IN CIVIL ENGINEERING
(2021)
Article
Environmental Sciences
Xiaojun Su, Yi Zhang, Xingmin Meng, Mohib Ur Rehman, Zainab Khalid, Dongxia Yue
Summary: Using SBAS-InSAR technology, this study detected ground surface deformation in the Hunza Valley and completed a comprehensive inventory of 118 landslides. The analysis of three large landslides revealed the characteristics and mechanisms of landslide development.
Article
Chemistry, Multidisciplinary
Chenhui Wang, Yijiu Zhao, Libing Bai, Wei Guo, Qingjia Meng
Summary: The research proposes a landslide displacement prediction model based on Genetic Algorithm optimized Elman neural network, which shows high prediction accuracy through the prediction and verification of the displacement data of a slow-varying landslide in the Guizhou karst mountainous area.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Electrical & Electronic
Xinchen Li, Liang Guo, Yachao Li, Liang Han, Guangcai Sun, Tao Xiong, Mengdao Xing
Summary: The article introduces a novel fast rotation matching method for SAR and optical images, which uses absolute directional derivative histograms and cross correlation to eliminate rotation differences, achieving high-speed rotation matching.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Biodiversity Conservation
Behnam Abbasnejad, Ahmad Abbasnejad, Reza Derakhshani
Summary: The investigation of groundwater quality in the north of Jazmourian (Roudbar plain) revealed that some samples exceeded the WHO permissible limits for nitrate and fluoride content, posing potential health risks. The water quality was affected by the type of sediment and recharge rate, leading to variations in dissolved salt concentration and ion content. Based on the assessment, certain samples showed poor water quality and higher restrictions for irrigation.
HUMAN AND ECOLOGICAL RISK ASSESSMENT
(2023)
Article
Chemistry, Multidisciplinary
Yaser A. Nanehkaran, Zhu Licai, Jin Chengyong, Junde Chen, Sheraz Anwar, Mohammad Azarafza, Reza Derakhshani
Summary: This study conducted a comparative analysis to assess/predict the safety factor (F.S) of earth slopes using MLP, SVM, DT, and RF learning methods. The results showed that MLP provides the most accurate F.S predictions, followed by the SVM algorithm.
APPLIED SCIENCES-BASEL
(2023)
Article
Multidisciplinary Sciences
Mohammad Moghadari Poor, Mohammad Azarafza, Reza Derakhshani
Summary: Pressuremeter Test (PMT), Cone Penetration Test (CPT), and Standard Penetration Test (SPT) are key in-situ experiments for estimating the modulus of deformation and strength parameters of soils in coastal alluvium. They are also useful for onshore regions. Regression analysis is used to predict engineering properties from these tests. This study proposes formulas based on regression analysis for South Pars phase 14 coastal alluvium, providing fast and reliable data for Southwest Iran near the Persian Gulf.
Article
Multidisciplinary Sciences
Yaser A. Nanehkaran, Licai Zhu, Mohammad Azarafza, Sona Talaei, Jinxia Xu, Junde Chen, Reza Derakhshani
Summary: Pandemic plastics, such as masks, gloves, aprons, and sanitizer bottles, have caused a global increase in waste due to the COVID-19 pandemic. These hazardous wastes not only contribute to environmental pollution but also indirectly spread COVID-19. This study aimed to develop a deep learning-based predictive model to forecast the expansion of pandemic plastic pollution in megacities in Iran.
SCIENTIFIC REPORTS
(2023)
Review
Environmental Sciences
Mojtaba Zaresefat, Reza Derakhshani
Summary: Developing precise soft computing methods is crucial for groundwater management. In the past 20 years, significant progress has been made in this field using hybrid machine learning models. This review article aims to understand the current state-of-the-art hybrid ML models used for groundwater management and their achievements.
Article
Environmental Studies
Reza Derakhshani, Mojtaba Zaresefat, Vahid Nikpeyman, Amin GhasemiNejad, Shahram Shafieibafti, Ahmad Rashidi, Majid Nemati, Amir Raoof
Summary: This study proposes an artificial intelligence approach to assess watershed morphometry in the Makran subduction zones of South Iran and Pakistan. The approach integrates machine learning algorithms, including artificial neural networks (ANN), support vector regression (SVR), and multivariate linear regression (MLR), on a single platform. The results demonstrate high accuracy and the potential for utilizing the ANN algorithm in similar investigations.
Article
Geosciences, Multidisciplinary
Ahmad Rashidi, Majid Nemati, Shahram Shafieibafti, Shahrokh Pourbeyranvand, Reza Derakhshani, Carla Braitenberg
Summary: Field evidence, seismicity, and geodetic data are combined to study the tectonic evolution in the northern domain of the Western and Central Alborz ranges. The geometric-kinematic characteristics of active fault planes are analyzed to assess seismic hazards. The study reveals the presence of three active segments with reverse mechanisms and left-lateral strike-slip components, acting as boundaries in the tectonic stress domains in the region. The spatial distribution of faulting supports the understanding of geodynamic processes.
JOURNAL OF ASIAN EARTH SCIENCES
(2023)
Article
Engineering, Civil
Behnam Khorrami, Saied Pirasteh, Shoaib Ali, Onur Gungor Sahin, Babak Vaheddoost
Summary: This study investigates the feasibility of using downscaled GRACE data for flood monitoring in the Kizilirmak Basin in Turkiye by integrating GRACE and hydrological model outputs. The results show that the TWSA in the basin is ascending, with varying rates in different sub-basins. The study also identifies periods of high flood potential in the area. The validation of the downscaled GRACE data demonstrates a strong correlation between the Flood Potential Index and streamflow, particularly in the Seyfe Kapali Basin.
JOURNAL OF HYDROLOGY
(2023)
Review
Chemistry, Analytical
Behzad Mirzaei, Hossein Nezamabadi-pour, Amir Raoof, Reza Derakhshani
Summary: Object detection and tracking are crucial in computer vision and visual surveillance, but detecting and tracking small objects poses significant challenges due to their subtle appearance and limited distinguishing features. This review explores existing methods, datasets, evaluation metrics, and future trends in small object detection and tracking.
Article
Environmental Sciences
Laiying Fu, Xiaoyong Chen, Saied Pirasteh, Yanan Xu
Summary: With the continuous advancement of deep learning technology, this paper proposes a double-branch multi-scale residual network framework for hyperspectral image classification. By extracting spectral and spatial features, expanding the network width, increasing the network depth, and employing skip connections, the proposed framework achieves improved classification accuracy with limited training samples. Experimental results demonstrate that the framework outperforms existing advanced frameworks on multiple datasets.
Article
Environmental Sciences
Yimin Mao, Liang Chen, Yaser A. Nanehkaran, Mohammad Azarafza, Reza Derakhshani, Achim A. Beylich
Summary: This study explores the integration of artificial intelligence (AI) and empirical techniques for evaluating rock slope stability, using the Q(slope) system as a framework. By incorporating fuzzy set theory into the Q(slope) classification, a novel approach is proposed to effectively quantify and accommodate uncertainties in coastal regions. The preliminary tests conducted on slope instabilities within coastal zones indicate promising results, affirming the precision and dependability of the proposed AI-based approach. However, further validation efforts are required to establish the reliability and effectiveness of this innovative method across diverse scenarios.
Review
Environmental Sciences
Yaser A. A. Nanehkaran, Biyun Chen, Ahmed Cemiloglu, Junde Chen, Sheraz Anwar, Mohammad Azarafza, Reza Derakhshani
Summary: Riverside landslides pose significant threats to infrastructure and human lives globally. Professionals have developed various methodologies to analyze, assess, and predict landslides, with artificial neural networks (ANNs) emerging as the preferred method for these assessments. The application of ANNs aligns with the United Nations' Sustainable Development Goals (SDGs), particularly Goal 11: Sustainable Cities and Communities. By effectively assessing riverside landslide susceptibility using ANNs, communities can better manage risks and enhance resilience. This review aims to contribute to the development of improved risk management strategies, sustainable urban planning, and resilient communities in the face of riverside landslides, in line with the SDGs.
Article
Environmental Sciences
Mohammad Mohammadhasani, Fateme Kamali, Ahmad Rashidi, Mobin Bahrampour, Shahram Shafieibafti, Razieh Abbaspour, Reza Derakhshani
Summary: Geohazards, including earthquakes, pose a significant threat to human life and infrastructure globally, and Iran is particularly vulnerable due to its unique structural characteristics. Therefore, monitoring geohazards to mitigate their impacts is crucial. Radar interferometry has emerged as an accurate and cost-effective method, capable of operating under all weather conditions 24/7. This study utilized Sentinel 1A images and radar interferometry to investigate surface changes following earthquakes in the Persian Gulf region, revealing significant uplift and subsidence near Bandar Khamir and Bandar Charak.
Article
Remote Sensing
Alireza Habibi, Mahmoud Reza Delavar, Borzoo Nazari, Saeid Pirasteh, Mohammad Sadegh Sadeghian
Summary: This study proposes a novel approach for Flood Hazard (FH) prediction using hybrid Machine Learning (ML) models integrated with Feature Selection (FS) algorithms. The research determines the Flood Influential Factors (FIFs) using the Simulated Annealing and Information Gain algorithms, and finds that factors such as rainfall, distance to river, altitude, and lithology have a greater impact on FH assessment in a specific watershed. The proposed hybrid models are evaluated using robust indicators and the highest AUC value is obtained by the SA-ABM model.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Geochemistry & Geophysics
Wenfei Mao, Xiaowen Wang, Guoxiang Liu, Saied Pirasteh, Rui Zhang, Hui Lin, Yakun Xie, Wei Xiang, Zhangfeng Ma, Peifeng Ma
Summary: This study proposes a reformulating RSSI (Re-RSSI)-based method for correcting ionospheric errors in TS-InSAR by optimizing linear scale factors, aiming to improve the accuracy of TS-InSAR measurements. Evaluation using 121 ALOS-1 PALSAR images shows that the Re-RSSI method can effectively remove time series ionospheric errors, achieving an 86.59% improvement rate in the root-mean-square error (RMSE) compared to the traditional RSSI method.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
