標(biāo)題: Titlebook: Geomorphic Risk Reduction Using Geospatial Methods and Tools; Raju Sarkar,Sunil Saha,Rajib Shaw Book 2024 The Editor(s) (if applicable) an [打印本頁(yè)] 作者: malignant 時(shí)間: 2025-3-21 17:14
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書(shū)目名稱(chēng)Geomorphic Risk Reduction Using Geospatial Methods and Tools影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Geomorphic Risk Reduction Using Geospatial Methods and Tools網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Geomorphic Risk Reduction Using Geospatial Methods and Tools網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Geomorphic Risk Reduction Using Geospatial Methods and Tools被引頻次
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書(shū)目名稱(chēng)Geomorphic Risk Reduction Using Geospatial Methods and Tools年度引用
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書(shū)目名稱(chēng)Geomorphic Risk Reduction Using Geospatial Methods and Tools讀者反饋
書(shū)目名稱(chēng)Geomorphic Risk Reduction Using Geospatial Methods and Tools讀者反饋學(xué)科排名
作者: SIT 時(shí)間: 2025-3-21 20:41 作者: 名字的誤用 時(shí)間: 2025-3-22 03:31
Artificial Neural Network Ensemble with General Linear Model for Modeling the Landslide Susceptibiliare highly susceptible to landslide. In the present study ensemble of ANN, general linear model (GLM), and ensemble ANN-GLM machine learning methods were applied for producing the landslide susceptibility maps (LSMs) of the Mirik region. A total of 373 landslide locations and twelve landslide condit作者: acrophobia 時(shí)間: 2025-3-22 07:50
An Advanced Hybrid Machine Learning Technique for Assessing the Susceptibility to Landslides in the ble NBT-RTF, Naive Bayes tree (NBT), and rotation forest (RTF). For landslide susceptibility modelling, 189 landslide sites and 12 landslide conditioning factors (LCFs) were gathered. Multi-collinearity analysis was done among the LCFs to determine the best LCFs to use. The metrics utilized to asses作者: landmark 時(shí)間: 2025-3-22 12:36 作者: 革新 時(shí)間: 2025-3-22 16:36 作者: 革新 時(shí)間: 2025-3-22 17:57
An Ensemble of J48 Decision Tree with AdaBoost and Bagging for Flood Susceptibility Mapping in the So limit its destructive effects, proper planning, cope up ideas, and mitigation strategies are required. So the present study deals with the preparation of flood susceptibility mapping in the Sundarban region of West Bengal, India. The study prepares a flood inventory map and also identifies the col作者: 性學(xué)院 時(shí)間: 2025-3-22 23:46 作者: 不朽中國(guó) 時(shí)間: 2025-3-23 04:38
Quantitative Assessment of Interferometric Synthetic Aperture Radar (INSAR) for Landslide Monitoring-surface ground motion in deep-seated landslides. We also consider the uncertainties that may arise out of using a remote sensing tool to track ground motion, as opposed to traditional boreholes, and how InSAR can be used to understand this?uncertainty.?The landslide case study of interest in this w作者: collagenase 時(shí)間: 2025-3-23 08:19
Geospatial Study of River Shifting and Erosion–Deposition Phenomenon Along a Selected Stretch of Riva. Measurement of braiding index (>1.5) and sinuosity (<1.5) with the aim of analyzing river morphometric parameters along with river shifting related with erosion–deposition for sinuosity throughout the study time duration indicate that the river has a braiding and straight or sinuous nature. Islan作者: Congestion 時(shí)間: 2025-3-23 09:53 作者: 省略 時(shí)間: 2025-3-23 16:26 作者: anagen 時(shí)間: 2025-3-23 19:56
Measuring Landslide Susceptibility in Jakholi Region of Garhwal Himalaya Using Landsat Images and Ens. To begin, landslides were established and a landslide inventory map was prepared. The total locations considered for this study were divided into 70% for the training datasets and 30% for the validation datasets. Following that, a total of 15 landslide conditioning factors were chosen. Furthermor作者: Promotion 時(shí)間: 2025-3-24 02:09
Landslide Susceptibility Mapping Using Satellite Images and GIS-Based Statistical Approaches in Partture, thereby requiring researchers to delve into more in-depth landslide-related studies with a focus on finding effective methods to better delineate landslide-prone areas as well as better understand the various underlying causative factors. The district of Kullu, situated in the Indian state of 作者: 難管 時(shí)間: 2025-3-24 02:37 作者: 持久 時(shí)間: 2025-3-24 06:41 作者: gregarious 時(shí)間: 2025-3-24 12:52 作者: Tortuous 時(shí)間: 2025-3-24 18:50
Plane Electromagnetic Wave Propagation,ation of policy and practices. Soil erosion through gully development is a natural geomorphic process that controlled by human activities and?highly effected environmental quality, ecosystem, natural resources, and agricultural activities; and it promotes hazards. So, gully erosion management is a k作者: ostrish 時(shí)間: 2025-3-24 23:04
Time-Varying Boundary-Value Problems,are highly susceptible to landslide. In the present study ensemble of ANN, general linear model (GLM), and ensemble ANN-GLM machine learning methods were applied for producing the landslide susceptibility maps (LSMs) of the Mirik region. A total of 373 landslide locations and twelve landslide condit作者: PSA-velocity 時(shí)間: 2025-3-25 00:36
Metallic Waveguides and Resonant Cavities,ble NBT-RTF, Naive Bayes tree (NBT), and rotation forest (RTF). For landslide susceptibility modelling, 189 landslide sites and 12 landslide conditioning factors (LCFs) were gathered. Multi-collinearity analysis was done among the LCFs to determine the best LCFs to use. The metrics utilized to asses作者: 一再困擾 時(shí)間: 2025-3-25 04:59 作者: Spartan 時(shí)間: 2025-3-25 10:47 作者: Accede 時(shí)間: 2025-3-25 14:52 作者: frozen-shoulder 時(shí)間: 2025-3-25 19:47
Electromagnetic and Quantum MeasurementsRiver Basin of Himachal Pradesh using a random forest (RF) data-driven model, prioritie flash flood conditioning factors using this methodology, and compare it with a multivariate adaptive regression spline (MARS).To provide the best prediction performance, their ensemble (MARS-RF) is used. The find作者: 的事物 時(shí)間: 2025-3-25 20:30 作者: scotoma 時(shí)間: 2025-3-26 03:29
Nathan Ida Ph.D.,Jo?o P. A. Bastos Ph.D.a. Measurement of braiding index (>1.5) and sinuosity (<1.5) with the aim of analyzing river morphometric parameters along with river shifting related with erosion–deposition for sinuosity throughout the study time duration indicate that the river has a braiding and straight or sinuous nature. Islan作者: 記成螞蟻 時(shí)間: 2025-3-26 05:44
Introduction to the Finite Element Method,the river was observed from 1990 to 2020. This study used Satellite images and GIS tools to understand river shifting. It can be stated that the River Ganga is shifting towards its left bank. This means left bank erosion is much more than right bank erosion. The river shifting has increased with tim作者: 緩和 時(shí)間: 2025-3-26 09:08
M. M. Tentzeris,L. Yang,A. Traille,A. Ridaof hydrological databases. Due to the complicated network of drainage systems, proper forecasting needs to be studied and analyzed, so that effective management of floods/drought events on a daily basis can be implemented in the coming future. However, hydrological data in Bhutan are scarce informat作者: 人造 時(shí)間: 2025-3-26 14:46 作者: audiologist 時(shí)間: 2025-3-26 18:08
Stationary Magnetic Fields 1 (In A Vacuum),ture, thereby requiring researchers to delve into more in-depth landslide-related studies with a focus on finding effective methods to better delineate landslide-prone areas as well as better understand the various underlying causative factors. The district of Kullu, situated in the Indian state of 作者: extinct 時(shí)間: 2025-3-26 21:57 作者: 概觀 時(shí)間: 2025-3-27 01:34 作者: 預(yù)測(cè) 時(shí)間: 2025-3-27 07:03 作者: liaison 時(shí)間: 2025-3-27 13:08 作者: 一夫一妻制 時(shí)間: 2025-3-27 14:14
https://doi.org/10.1007/978-981-99-7707-9Geomorphic hazard; Machine learning technique; Satellite image; Resilience process; Risk reduction techn作者: Cosmopolitan 時(shí)間: 2025-3-27 21:44 作者: 類(lèi)似思想 時(shí)間: 2025-3-27 22:01
2196-4106 rent geomorphic hazards risk.Applies advanced machine learniThis book explores the use of advanced geospatial techniques in geomorphic hazards modelling and risk reduction. It also compares the accuracy of traditional statistical methods and advanced machine learning methods and addresses the differ作者: 頭腦冷靜 時(shí)間: 2025-3-28 03:02
Metallic Waveguides and Resonant Cavities,RMSE and the greatest ROC-AUC (0.867) and kappa index (0.884) during the validation phase (0.234). The anticipated model is reliable for minimizing the impact of landslides in the research region and planning land development.作者: 拖債 時(shí)間: 2025-3-28 07:20
Electromagnetic waves in conductors, were used to prepare the flood susceptibility maps. The models used were J48DT, J48DT-AdaBoost, and J48DT-Bagging. Finally, the outputs obtained were validated using six techniques namely, Sensitivity, Specificity, AUC (%), Kappa, MAE, and RMSE.作者: Vulnerary 時(shí)間: 2025-3-28 14:22 作者: 防水 時(shí)間: 2025-3-28 17:24 作者: 反復(fù)無(wú)常 時(shí)間: 2025-3-28 21:30 作者: Occupation 時(shí)間: 2025-3-29 00:39 作者: 處理 時(shí)間: 2025-3-29 04:31 作者: 嘲笑 時(shí)間: 2025-3-29 08:42
Introduction to the Finite Element Method, deposition of sediments. People in Manikchak, Kaliachak-II, and Kaliachak-III blocks of Malda district West Bengal were highly affected due to this river shifting in the lower course of the Ganga River. A few portions of the Rajmahal block of Jharkhand, located on the right side of the river are also affected.作者: POINT 時(shí)間: 2025-3-29 14:28 作者: rectum 時(shí)間: 2025-3-29 18:59
Landslide Susceptibility Assessment Based on Machine Learning Techniquese datasets were recommended. A total of 9 machine learning methods applied in LSA were simply introduced. The advantages and future work of LSA based on machine learning techniques were summarized from the aspects of scale, performance, modeling, and interpretability.作者: BLANK 時(shí)間: 2025-3-29 22:55 作者: arsenal 時(shí)間: 2025-3-30 02:40
The Adoption of Random Forest (RF) and Support Vector Machine (SVM) with Cat Swarm Optimization (CSOs that would be used for both training and testing by using a random sampling technique. This allowed us to have complete control over the models. It was discovered that CSO not only improved the fitting of the model and the quality of the results, but it also speed up the procedure.作者: 冷峻 時(shí)間: 2025-3-30 05:44
Book 2024itional statistical methods and advanced machine learning methods and addresses the different ways to reduce the impact of geomorphic hazards..In recent years with the development of human infrastructures, geomorphic hazards are gradually increasing, which include landslides, flood and soil erosion,作者: chronology 時(shí)間: 2025-3-30 09:51 作者: Consensus 時(shí)間: 2025-3-30 16:06
Geomorphic Risk Reduction Using Geospatial Methods and Tools作者: PALMY 時(shí)間: 2025-3-30 17:55 作者: sacrum 時(shí)間: 2025-3-30 21:21 作者: LANCE 時(shí)間: 2025-3-31 03:06
Artificial Neural Network Ensemble with General Linear Model for Modeling the Landslide Susceptibililowest RMSE and the highest ROC-AUC (0.864) and kappa index (0.889) during the validation phase (0.086). As per the result of ensemble model 20.99% area of the Mirik region is very highly susceptible for landslide. The anticipated model is reliable in lowering the danger of landslide risks for prosp作者: 陶瓷 時(shí)間: 2025-3-31 07:23 作者: Enliven 時(shí)間: 2025-3-31 10:29 作者: Exonerate 時(shí)間: 2025-3-31 14:43
