Advances in Space Research最新文献

{"title":"Orbit determination analysis of IGSO satellite onboard GPS/BDS pseudorange data corrected by different code hardware delays products","authors":"Zhenghao Zhang ,&nbsp;Yong Huang ,&nbsp;Peng Yang ,&nbsp;Yanling Chen ,&nbsp;Xiaolin Jia","doi":"10.1016/j.asr.2024.11.047","DOIUrl":"10.1016/j.asr.2024.11.047","url":null,"abstract":"<div><div>The navigation signal received by the Inclined Geo-Synchronous Orbit (IGSO) satellite is often blocked by the Earth, and the signal strength is weak due to the long propagation distance, which makes the carrier phase data processing complicated. Using pseudorange data is a convenient and efficient method for determining the orbit of IGSO satellites. Code hardware delays are non-negligible errors in pseudorange data processing. This study investigates the precise orbit determination (POD) result using only pseudorange data corrected by four types of Differential Code Bias (DCB) products and Observable-specific Signal Biases (OSB) products, with the LT4A satellite as a case study. The results show that the correction of code hardware delay can effectively improve the orbit quality. After correcting the code hardware delay, using only GPS pseudorange observations, using only BDS pseudorange observations, and using GPS + BDS combined pseudorange observations to determine the orbit, the pseudorange residuals RMS are about 1.82 m, 1.09 m, and 1.53 m, respectively. Compared with the uncorrected code hardware delays results, the residuals RMS is better by 21.8 %, 79.5 %, and 53.0 %, respectively. As the length of the POD arc increases from 24 h to 72 h, both the quality and stability of the orbit are observed to improve. For the 72 h arc, the orbit overlap RMS with the three data types is 3.4 m, 3.0 m, and 2.8 m, respectively, with the improvement of about 10.5 %, 77.6 %, and 59.4 %, respectively, compared with no code hardware delays correction. Compared with the precise reference orbit, the comparison RMS are 6.3 m, 4.8 m, and 4.4 m, increased by 24.0 %, 78.9 %, and 51.6 %, respectively. The results demonstrate that the comparison RMS of the IGSO satellite with only pseudorange data can be better than 5 m in position, and correcting the code hardware delays can improve orbit quality obviously, especially for BDS pseudorange data.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 3","pages":"Pages 3050-3062"},"PeriodicalIF":2.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning classification of winter wheat from Sentinel optical-radar image time series in smallholder farming areas","authors":"Xiaofang Sun ,&nbsp;Meng Wang ,&nbsp;Junbang Wang ,&nbsp;Guicai Li ,&nbsp;Xuehui Hou","doi":"10.1016/j.asr.2024.11.038","DOIUrl":"10.1016/j.asr.2024.11.038","url":null,"abstract":"<div><div>As crop yield stagnation, climate change, and the rising demand for agricultural products pose increasing challenges, mapping crop systems is becoming more and more important. Winter wheat is one of the major cereal crops cultivated in China, ranking as the third largest crop in terms of production and harvested area. Accurately mapping winter wheat is necessary for implementing effective farm management practices. While many studies have successfully produced high spatiotemporal resolution land cover maps, relatively few map products of crop types are available in China. The growing archive of satellite image time series provides enormous opportunities to map crops more closely. This research presents a two-step method to map winter wheat based on Sentinel-1 and Sentinel-2 time-series data from Shandong Province using the deep learning approaches. The winter crops were firstly mapped using time-series optical vegetation indices employing the deep learning methods. Then winter wheat was extracted from the winter crops mask by coupling optical and synthetic aperture radar time-series images. The results indicated that the precision of mapping winter wheat using Temporal Convolution Neural Networks (TempCNN) achieved the highest precision in mapping winter wheat, with an overall accuracy of 93.7 %, a kappa coefficient of 0.907, and an F1-score of 0.989. This was followed sequentially by the Residual 1D convolutional neural networks (ResNet), the Multi-Layer Perceptron (MLP), and the Lightweight Temporal Self-Attention Encoder (L-TAE). The Temporal Attention Encoder (TAE) model demonstrated the lowest precision among the compared models. The results agree well with independent county-level official census winter wheat area data (<em>R²</em> = 0.936). The proposed framework can also be applied in other regions to generate maps of different crops, so future work can extend the proposed model to other agricultural regions, where an increased number of crop types and natural vegetation types can be included and tested.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 3","pages":"Pages 2683-2695"},"PeriodicalIF":2.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling and prediction of atmospheric drag coefficients in LEO satellite orbit determination and prediction with Bi-LSTM approach","authors":"Xiang Chen ,&nbsp;Chengpan Tang ,&nbsp;Wujiao Dai ,&nbsp;Xiaogong Hu ,&nbsp;Liucheng Chen ,&nbsp;Zhongying Zhang ,&nbsp;Xinhui Zhu ,&nbsp;Mingzhe Li","doi":"10.1016/j.asr.2024.10.063","DOIUrl":"10.1016/j.asr.2024.10.063","url":null,"abstract":"<div><div>In the precise orbit determination (POD) of Low Earth Orbit (LEO) satellites with onboard Global Navigation Satellite System (GNSS) observations, atmospheric drag coefficients (Cd) are estimated piece-wise to absorb atmosphere density modeling errors, attitude modeling errors and windward area errors when the satellite physical metadata is not available. This study focuses on modeling and prediction of atmospheric drag coefficient in LEO satellite orbit determination and prediction. Orbit determination was conducted to determine atmospheric drag coefficients for eight LEO satellites with the orbital altitudes from 500 km to 1300 km. The Bidirectional Long Short-Term Memory (Bi-LSTM) neural network was used to model and predict the atmospheric drag coefficient estimations. The average Mean Absolute Percentage Error (MAPE) and average relative error between the predicted and estimated values of Cd for the eight satellites, were 0.09 and 0.11, respectively, indicating a satisfactory prediction performance of Cd. Prediction of the Cd is applied in orbit prediction and 30-minute short arc orbit determination (SOD). The results of the orbit prediction show that the modeling of Cd plays a key role in improving the accuracy of orbit prediction. The accuracy of the orbit prediction method based on the Cd prediction is better than that of the method without Cd prediction, and the average accuracy improves by 67.5 % and 73.7 % for the eight satellites in 2019 and 2023, respectively. The highest accuracy improvement rate is 94.5 % for GRACE-C satellite in 2019 and 86.6 % for Swarm-B satellite in 2023. Among them, the RMS of the average 3D error of the 3-day orbit prediction of the Swarm-B satellite is the lowest in both 2019 and 2023, at 2.11 m and 8.79 m, respectively. The results show that the SOD method with constrained Cd for eight satellites has different degrees of accuracy improvement in most arcs relative to the method without constrained Cd. The average orbital accuracy with constrained Cd improves by 14.8 % and 17.1 % for the eight satellites in 2019 and 2023, respectively, with the highest accuracy improvement of 24.7 % for GRACE-C satellite in 2019 and 24.2 % for GRACE-D satellite in 2023. The average orbit error of GRACE-C satellite is reduced from 9.23 cm to 5.95 cm, and the average orbit error of GRACE-D satellite is reduced from 13.45 cm to 8.22 cm.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 3","pages":"Pages 2874-2888"},"PeriodicalIF":2.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the identification of the spatiotemporal locations of solar wind structures during an intense geomagnetic storm","authors":"Victor U. Chukwuma ,&nbsp;Bolarinwa J. Adekoya ,&nbsp;Eugene O. Onori ,&nbsp;Oluwafunmilayo O. Ometan ,&nbsp;Aghogho Ogwala","doi":"10.1016/j.asr.2024.11.034","DOIUrl":"10.1016/j.asr.2024.11.034","url":null,"abstract":"<div><div>Available literature indicates that various studies do not adequately provide information on the temporal locations of solar wind structures, which leaves a gap that needs to be filled. In response, a study of the geomagnetic storm of Oct 13, 2016, is carried out to fill this gap by identifying the spatiotemporal locations of solar wind structures responsible for the geomagnetic storm to effectively elucidate ionospheric responses during a geomagnetic storm, as well as enable the determination by further studies of the global relative geo-effectiveness of these structures as it were. Primarily, the investigation aimed to confirm if the typical simplified structure of all CMEs consists of a forward shock followed by the sheath and the magnetic cloud. Now, this study has brought forth some distinct/previously unseen results: A typical simplified structure of a CME consists of a forward shock followed by the sheath and the magnetic cloud. Our findings revealed a sandwich structure of sheath-magnetic cloud-sheath following the forward shock with the following spatiotemporal arrangement for the solar wind structures: (a) Sheath I, observed from 2 Oct 13 to 3 Oct 14, spanning: (i) the initial phase: 2–7 UT, Oct 13 (ii) the main phase:7–23 UT, Oct 13 (iii) the early parts of the recovery phase: 0–3 UT, Oct 14 (b) The Magnetic Cloud, observed through 03–09 UT, Oct 14, during the recovery phase. (c) Sheath II, observed during the recovery phase from 9 to 23 Oct 14.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 3","pages":"Pages 3197-3209"},"PeriodicalIF":2.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative case study of delayed ionospheric response to a superposed 27-day solar rotation signal","authors":"Hanna Dühnen ,&nbsp;Rajesh Vaishnav ,&nbsp;Christoph Jacobi ,&nbsp;Erik Schmölter ,&nbsp;Jens Berdermann","doi":"10.1016/j.asr.2024.12.004","DOIUrl":"10.1016/j.asr.2024.12.004","url":null,"abstract":"<div><div>Major ionization processes in the upper atmosphere are driven by the solar extreme ultraviolet (EUV) radiation causing corresponding responses in ionospheric observables. The response to the 27-day solar rotation period is of particular interest, as this variation occurs with a delay, which depends on the level of solar and geomagnetic activity as well as atmospheric processes. The 27-day signature is also frequently superimposed with long-term variations, which further impact the length of the delay. For a better understanding of these interactions, the present study investigates the delayed response of ionospheric total electron content (TEC) and the concentrations of major neutral and ionized species. Using high-resolution simulations from the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) and the Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model, two distinct 27-day solar rotation periods from the high solar activity year of 2014 are analyzed. This comparison allows us to examine the effects of an ideal 27-day solar activity cycle alongside one characterized by increasing solar activity, while also comparing model results with observed IGS data. Our detailed analysis, based on TIE-GCM simulations, presents the ionospheric response for various ionized and neutral species across different altitudes and latitudes. Notably, we find that the accumulation of ionized species, such as <span><math><mrow><msup><mrow><mi>O</mi></mrow><mrow><mo>+</mo></mrow></msup></mrow></math></span> and <span><math><mrow><msubsup><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow><mrow><mo>+</mo></mrow></msubsup></mrow></math></span>, in the lower ionosphere, particularly at approximately 230 km, where ionized oxygen density peaks—is significantly influenced by the long-term increase in solar activity. However, the 27-day solar rotation period predominantly governs ionization processes at altitudes above 230 km in both, ideal and complex model runs. Thus, our results are in good agreement with previous studies and extend the understanding of the delayed ionospheric response to more complex cases.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 3","pages":"Pages 3115-3132"},"PeriodicalIF":2.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attitude synchronization of chaotic satellites with unknown dynamics using a neural network based fixed time sliding mode controller","authors":"Özhan Bingöl","doi":"10.1016/j.asr.2024.11.009","DOIUrl":"10.1016/j.asr.2024.11.009","url":null,"abstract":"<div><div>This study investigates the synchronization and anti-synchronization of both identical and non-identical chaotic satellite systems. A fixed-time sliding mode control framework, based on a radial basis function (RBF) neural network, has been developed to synchronize the chaotic dynamics of master–slave satellite configurations. The proposed control scheme operates under the assumption that the dynamics of the satellites are not entirely known. The proposed control method guarantees that system errors will converge to negligible levels within a fixed time. Furthermore, the controller exhibits robustness in the presence of parametric uncertainties and external disturbances. The stability of the controlled systems is rigorously validated through Lyapunov analysis, and the controller’s effectiveness is confirmed through extensive simulation studies. These simulations are conducted on both identical and non-identical satellite models, with performance comparisons made against recent findings reported in the literature.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 3","pages":"Pages 3242-3267"},"PeriodicalIF":2.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting rainfall events based on zenith wet delay time series utilizing eXtreme gradient boosting (XGBoost)","authors":"Masoud Dehvari ,&nbsp;Saeed Farzaneh ,&nbsp;Ehsan Forootan","doi":"10.1016/j.asr.2024.11.013","DOIUrl":"10.1016/j.asr.2024.11.013","url":null,"abstract":"<div><div>Accurate rainfall prediction is vital for mitigating flood and storm disasters as well as for planning agricultural activities and water resources management. GNSS observations enable the estimation of atmospheric water vapor content through the Zenith Wet Delay (ZWD) value, where previous studies indicate a strong correlation between the ZWD-derived indicators and rainfall events. However, specifying these indicators is challenging due to the spatial variability of precipitation and the location of GNSS stations. While many studies have integrated meteorological parameters with GNSS-derived Zenith Total Delay (ZTD) values to enhance prediction accuracy, the scarcity of meteorological instruments at GNSS stations remains a limitation. In this study, we employed ZWD-derived features and utilized the eXtreme Gradient Boosting (XGBoost) classification method to predict rainfall events. Ten parameters (including station latitude, longitude, elevation, ZWD monthly anomaly, ZWD slope, ZWD maximum, maximum ZWD derivative, month, hour, and precipitation flag) were used as features in the input layer of the considered XGBoost model. For training, data from 40 GNSS stations spanning five consecutive years (2016 to 2020) in the eastern United States of America were analyzed to derive the required features from 4-hour ZWD time series. To evaluate the proposed method, estimated rainfall was compared with the observations of weather stations during 2021. Furthermore, the results of five GNSS stations (not included in the training) were compared with the regional rainfall events of 2016 to 2021. Our results indicate that the proposed method achieves a mean True Forecast Rate (TFR) and a mean False Forecast Rate (FFR) of approximately 0.75 and 0.15, respectively, demonstrating performance comparable to studies incorporating meteorological parameters.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 3","pages":"Pages 2584-2598"},"PeriodicalIF":2.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the influence of land cover on land surface temperature","authors":"Changkuan Shui,&nbsp;Baoyan Shan,&nbsp;Wenjing Li,&nbsp;Lina Wang,&nbsp;Yangyang Liu","doi":"10.1016/j.asr.2024.11.016","DOIUrl":"10.1016/j.asr.2024.11.016","url":null,"abstract":"<div><div>The increasingly serious urban heat island (UHI) effect is unfavorable to urban development. This study utilized land cover data and land surface temperature (LST) data of China in 2020 by using correlation analysis and spatial regression models to analyze the relationships between LST and two influencing factors (land cover and digital elevation model (DEM)). The results showed the following: (1) The correlation between LST and forest was highest in the Northeast China Plain (NCP), Huang-Huai-Hai Plain (HHP), Qinghai Tibet Plateau (QTP), and Loess Plateau (LP). DEM mean displayed its highest correlation in the Northern arid and semiarid region (NAR), Sichuan Basin and surrounding regions (SCR), Yunnan-Guizhou Plateau (YGP), and Middle-lower Yangtze Plain (MYP). Southern China (SC) had the highest correlation between LST and construction land. (2) There was spatial heterogeneity between land cover and LST. Unused land in LP had larger impact on LST. For every 1 % increase in the proportion of unused land area, the LST increased by 0.250 °C. LST in some central and western regions of China (the NAR, the LP, the SCR, and the YGP) was mainly affected by local land cover; LST in eastern coastal regions (the HHP, MYP, NCP, SC) and QTP was not only affected by local land cover, but also by LST or land cover of neighboring regions. The warming effect of construction land on LST was more significant, with LST increasing by 0.079 °C to 0.338 °C for every 1 % increase in the proportion of construction land area. Coordination of land use planning and synergistic remediation in different regions and rational planning of construction land are essential to mitigate the UHI effect. (3) Water bodies in the NCP, NAR, and MYP had the greatest cooling impact on LST, with LST decreasing by 0.277 °C, 0.246 °C, and 0.079 °C, respectively, for every 1 % increase in the proportion of water bodies area. Forest in the QTP, LP, SC, and YGP had the greatest cooling impact on LST, and for every 1 % increase in the proportion of forest area, LST decreased by 0.144 °C, 0.089 °C, 0.086 °C, and 0.038 °C, respectively. Actively planting trees and increasing the area of forests and water bodies are of positive significance in alleviating the UHI effect and improving the ecological environment.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 3","pages":"Pages 2614-2631"},"PeriodicalIF":2.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuromorphic robust estimation of nonlinear dynamical systems applied to satellite rendezvous","authors":"Reza Ahmadvand,&nbsp;Sarah Sharif,&nbsp;Yaser Banad","doi":"10.1016/j.asr.2024.11.037","DOIUrl":"10.1016/j.asr.2024.11.037","url":null,"abstract":"<div><div>State estimation of nonlinear dynamical systems has long been driven by the goals of accuracy, computational efficiency, robustness, and reliability. With the rapid evolution of various industries, the demand for estimation frameworks that can simultaneously fulfill these factors has grown significantly. Leveraging recent advances in neuromorphic computing architectures, this study presents a neuromorphic approach for robust filtering of nonlinear dynamical systems called SNN-EMSIF (spiking neural network-extended modified sliding innovation filter). SNN-MSIF benefits the computational efficiency and scalability of SNNs with the robustness of EMSIF, which is an estimation framework for nonlinear systems with zero-mean Gaussian noises. Notably, the weight matrices of the networks are designed according to the system model, eliminating the need for a learning process. The efficacy of the approach is evaluated by proposing a spiking framework based on an<!--> <!-->extended Kalman filter (EKF). Through comprehensive Monte-Carlo simulations, the performance of EKF and EMSIF. Additionally, SNN-EMSIF is compared with SNN-EKF in the presence of modeling uncertainties and neuron loss by means of obtained RMSEs. Results demonstrate the validity of the proposed methods and highlight the superior performance of SNN-EMSIF in terms of accuracy and robustness. Furthermore, investigations into obtained runtimes and spiking patterns generated by the SNN-EMSIF provide compelling evidence of the achieved computational efficiency, with an impressive reduction of approximately 85% in emitted spikes compared to possible spikes. The SNN-MSIF framework presents a promising solution to address the challenges of robust estimation in nonlinear dynamical systems, opening new avenues for efficient and reliable estimation in various industries benefiting neuromorphic computing advantages.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 3","pages":"Pages 3010-3024"},"PeriodicalIF":2.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geospatial assessment and integrated multi-model approach for landslide susceptibility mapping in Meghalaya, India","authors":"Naveen Badavath,&nbsp;Smrutirekha Sahoo","doi":"10.1016/j.asr.2024.11.052","DOIUrl":"10.1016/j.asr.2024.11.052","url":null,"abstract":"<div><div>Landslides in Meghalaya, India, inflict severe property and life damage due to mountains, steep slopes, and heavy rains. Landslide Susceptibility (LS) maps are extremely useful for disaster management. The primary goal of this work is to generate an LS map for the state of Meghalaya. In the first phase, a Landslide Inventory (LI) map was created, which included 855 landslides that occurred from 2019 to 2023. The LI map was then split into 70 % (601) and 30 % (254) for training and testing, respectively. In the second phase, the study selected fourteen conditioning factors as thematic layers for LS mapping and performed multicollinearity and Pearson’s correlation analysis; all the parameters were identified as optimal for the prediction model. Eight different scenario models (Frequency Ratio (FR), Evidence Belief Function (EBF), FR + EBF, FR*EBF, (FR*EBF)/2, (2*FR) + EBF, (2*EBF) + FR and (EBF + FR)/3) have been used to generate LS maps. The created maps were validated using Receiver Operating Characteristics (ROC) curve and the corresponding Area Under the Curve (AUC) value, statistical measures (recall, precision, F1 score, overall accuracy, and balanced accuracy) and on-site verification with recent landslides. Finally, the result of the best scenario was compared with the outcome of the Analytical Hierarchy Process (AHP) method. Results showed that scenario 4 (EBF*FR) has an overall accuracy of 82.3 %, whereas AHP has an overall accuracy of 77.6 %. It is indicated that scenario 4 achieved 4.7 % higher overall accuracy than that of the AHP method. Recent landslides selected for on-site verification occurred in an area classified as very highly susceptible by the scenario 4 model. These maps provide vital conceptions of landslide mechanisms, assisting land use planning and disaster management. This approach can be applied in similar areas by investigators, which indicates the originality of the study, and the result of this study will be beneficial for the Meghalaya region.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 3","pages":"Pages 2764-2791"},"PeriodicalIF":2.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}