Environmental Modelling & Software最新文献_第9页

QGIS Shoreline Change Analysis Tool (QSCAT): A fast, open-source shoreline change analysis plugin for QGIS QGIS 海岸线变化分析工具 (QSCAT)：QGIS 的快速、开源海岸线变化分析插件

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-19 DOI: 10.1016/j.envsoft.2024.106263

Louis Philippe Facun , Ma. Yvainne Sta Maria , Rodel Ducao , Jamela Jirah Clemente , Ellen Mae Carmelo , Angelo Maon , Ara Rivina Malaya , Floribeth Cuison , Fernando Siringan

{"title":"QGIS Shoreline Change Analysis Tool (QSCAT): A fast, open-source shoreline change analysis plugin for QGIS","authors":"Louis Philippe Facun , Ma. Yvainne Sta Maria , Rodel Ducao , Jamela Jirah Clemente , Ellen Mae Carmelo , Angelo Maon , Ara Rivina Malaya , Floribeth Cuison , Fernando Siringan","doi":"10.1016/j.envsoft.2024.106263","DOIUrl":"10.1016/j.envsoft.2024.106263","url":null,"abstract":"<div><div>Coastal erosion poses a significant threat to most coastal communities. This necessitates a better understanding of coastal erosion dynamics, and thus, shoreline change analysis (SCA) tools would be handy. However, many available tools require commercial softwares and/or a faster computing platform. To address these issues, QGIS’ Shoreline Change Analysis Tool (QSCAT), a new QGIS plugin built with Python, was developed. QSCAT can perform transect-based and area-based analyses. The transect-based algorithm of QSCAT was patterned after the Digital Shoreline Analysis System (DSAS). Whereas, the area-based algorithm is similar to the change polygon method. Running QSCAT and DSAS together demonstrated that QSCAT generated the same results as DSAS but its overall speed is 8 times faster than DSAS. QSCAT can estimate beach area loss and length of eroding shorelines, which can identify erosion hotspots. These features attest to QSCAT’s potential as a more efficient and an equally reliable SCA tool.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"184 ","pages":"Article 106263"},"PeriodicalIF":4.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probability analysis of shallow landslides in varying vegetation zones with random soil grain-size distribution 不同植被带中土壤粒度随机分布的浅层滑坡概率分析

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-15 DOI: 10.1016/j.envsoft.2024.106267

Hu Jiang , Qiang Zou , Yong Li , Yao Jiang , Junfang Cui , Bin Zhou , Wentao Zhou , Siyu Chen , Zihao Zeng

{"title":"Probability analysis of shallow landslides in varying vegetation zones with random soil grain-size distribution","authors":"Hu Jiang , Qiang Zou , Yong Li , Yao Jiang , Junfang Cui , Bin Zhou , Wentao Zhou , Siyu Chen , Zihao Zeng","doi":"10.1016/j.envsoft.2024.106267","DOIUrl":"10.1016/j.envsoft.2024.106267","url":null,"abstract":"<div><div>The physically-based landslide susceptibility models are widely used to guide disaster prevention and mitigation in mountainous areas due to their significant predictive capability. However, this method faces limitations in regions with complex topography and vegetation types, primarily due to a lack of consideration for the spatial uncertainty of planted soil caused by variations in soil particle size composition. Therefore, a new model is established to predict shallow landslide occurrence considering the impact of the uncertainty of soil particle size composition on soil shear strength parameters. This model optimizes the assignment strategy for soil physical strength parameters with the support of the random soil grain-size field theory. Subsequently, it organically integrates the impact of plants on slope stability, involving root reinforcing, moisture regulation (preferential flow and root water uptake), and the canopy's interception and weight loading effects, based on the infinite slope model. The model is validated in a region with significant vegetation zonality in Sichuan Province, China. The results show: (i) the testing indicator AUC values range from 0.862 to 0.873, indicating that the model can effectively predict the spatial occurrence probability of shallow landslides, (ii) the proposed LSM-VEG-GSD model exceeds by 17.50% the traditional pseudo-static model according to the AUC score, and (iii) regardless of water height ratio interval, the probability of slope failure in different vegetation zones increases with slope angle, following an S-shaped curve regression pattern. Overall, the findings of this study contribute to predicting the stability of shallow landslides in terrain transition zones with high potential landslide concealment and uncertainty under the influence of vegetation.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106267"},"PeriodicalIF":4.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Variable sensitivity analysis in groundwater level projections under climate change adopting a hybrid machine learning algorithm 采用混合机器学习算法对气候变化下的地下水位预测进行变量敏感性分析

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-13 DOI: 10.1016/j.envsoft.2024.106264

Ali Sharghi, Mehdi Komasi, Masoud Ahmadi

{"title":"Variable sensitivity analysis in groundwater level projections under climate change adopting a hybrid machine learning algorithm","authors":"Ali Sharghi, Mehdi Komasi, Masoud Ahmadi","doi":"10.1016/j.envsoft.2024.106264","DOIUrl":"10.1016/j.envsoft.2024.106264","url":null,"abstract":"<div><div>Studies on climate change have largely overlooked the delayed response of Ground Water Levels (GWL) to atmospheric conditions. This gap is critical because fluctuations in GWL can lead to hazards like land subsidence. This study addresses the issue by identifying optimal delay times for key variables, which improves GWL projection accuracy. The input data process consists of introducing meteorological and hydrological variables in the form of 42 combinations. Meteorological data under climate change scenarios were obtained by downscaling outputs from the General Circulation Models (GCMs) within the Shared Socio-economic Pathways (SSP) scenarios. So far, no similar study has attempted to rank such a wide array of delay time combinations. This study improves hybrid Random Forest and Genetic Algorithm (RF-GA) projections by introducing the best combination of input variables. The investigation assessed the performance of both the conventional Random Forest (RF) and the RF-GA in simulating groundwater fluctuations. The variable sensitivity analysis results indicated that watershed discharge holds a higher Variable Importance (VI) compared to meteorological variables. The findings in the validation section also demonstrated that the RF-GA outperformed an RF that runs on default hyperparameters. Temperature and evaporation show a 3 and 2-month delay time, respectively. It was discovered that precipitation was the only variable with two possible delay times of 2 and 4-month. Also, combinations with many and few variables performed poorly. The projection results indicate an increase of 6.8 and 7.1 cm in the average GWL in the Silakhor plain under the low-emission SSP1-2.6 and high-emission SSP5-8.5 scenarios, respectively.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106264"},"PeriodicalIF":4.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Canopy height Mapper: A google earth engine application for predicting global canopy heights combining GEDI with multi-source data 树冠高度绘图仪：结合 GEDI 和多源数据预测全球树冠高度的谷歌地球引擎应用程序

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-12 DOI: 10.1016/j.envsoft.2024.106268

Cesar Alvites , Hannah O'Sullivan , Saverio Francini , Marco Marchetti , Giovanni Santopuoli , Gherardo Chirici , Bruno Lasserre , Michela Marignani , Erika Bazzato

{"title":"Canopy height Mapper: A google earth engine application for predicting global canopy heights combining GEDI with multi-source data","authors":"Cesar Alvites , Hannah O'Sullivan , Saverio Francini , Marco Marchetti , Giovanni Santopuoli , Gherardo Chirici , Bruno Lasserre , Michela Marignani , Erika Bazzato","doi":"10.1016/j.envsoft.2024.106268","DOIUrl":"10.1016/j.envsoft.2024.106268","url":null,"abstract":"<div><div>Spatially and temporally discontinuous canopy height footprints collected by NASA's GEDI (Global Ecosystem Dynamics Investigation) mission are accessible on the Google Earth Engine (GEE) cloud computing platform. This study introduces an open-source, user-friendly, code-free GEE web application called Canopy Height Mapper (CH-GEE), available at <span><span>https://ee-calvites1990.projects.earthengine.app/view/ch-gee</span><svg><path></path></svg></span>, which automatically generates (10 m) high-resolution canopy height maps for a specific area by integrating GEDI with multi-source remote sensing data: Copernicus and topographical data from the GEE data catalogue. CH-GEE generates local-to-country scale calibrated canopy height maps worldwide using machine learning algorithms and leveraging the GEE platform's big data and cloud computing capabilities. CH-GEE allows customization of geographic area, algorithms and time windows for GEDI and predictors. Canopy heights generated by CH-GEE were validated using the Italian National Forest Inventory across 110,000 km<sup>2</sup> at multiple scales (Country-based R-squared = 0.89, RMSE = 17%). CH-GEE's accuracy and scalability make it suitable for forest monitoring.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106268"},"PeriodicalIF":4.8,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Taxonomy of purposes, methods, and recommendations for vulnerability analysis 脆弱性分析的目的、方法和建议分类学

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-12 DOI: 10.1016/j.envsoft.2024.106269

Nathan Bonham , Joseph Kasprzyk , Edith Zagona

引用次数: 0

Integrated STL-DBSCAN algorithm for online hydrological and water quality monitoring data cleaning 用于在线水文和水质监测数据清理的 STL-DBSCAN 集成算法

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-10 DOI: 10.1016/j.envsoft.2024.106262

Chenyu Song , Jingyuan Cui , Yafei Cui , Sheng Zhang , Chang Wu , Xiaoyan Qin , Qiaofeng Wu , Shanqing Chi , Mingqing Yang , Jia Liu , Ruihong Chen , Haiping Zhang

{"title":"Integrated STL-DBSCAN algorithm for online hydrological and water quality monitoring data cleaning","authors":"Chenyu Song , Jingyuan Cui , Yafei Cui , Sheng Zhang , Chang Wu , Xiaoyan Qin , Qiaofeng Wu , Shanqing Chi , Mingqing Yang , Jia Liu , Ruihong Chen , Haiping Zhang","doi":"10.1016/j.envsoft.2024.106262","DOIUrl":"10.1016/j.envsoft.2024.106262","url":null,"abstract":"<div><div>Online hydrological and water quality monitoring data has become increasingly crucial for water environment management such as assessment and modeling. However, online monitoring data often contains erroneous or incomplete datasets, consequently affecting its operational use. In the study, we developed an automated data cleaning algorithm grounded in Seasonal-Trend decomposition using Loess (STL) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). STL identifies and corrects more obvious anomalies in the time series, followed by DBSCAN for further refinement, in which the reverse nearest neighbor method was employed to enhance the clustering accuracy. To improve anomaly detection, a two-level residual judgment threshold was applied. The algorithm has been successfully applied to three reservoirs in Shanghai, China, achieving the precision rate of 0.91 and recall rate of 0.81 for dissolved oxygen and pH. The proposed algorithm can be potentially applied for cleaning of environment monitoring data with high accuracy and stability.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106262"},"PeriodicalIF":4.8,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enabling coastal analytics at planetary scale 在地球尺度上实现沿岸分析

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-08 DOI: 10.1016/j.envsoft.2024.106257

Floris Reinier Calkoen , Arjen Pieter Luijendijk , Kilian Vos , Etiënne Kras , Fedor Baart

引用次数: 0

Transformer-embedded 1D VGG convolutional neural network for regional landslides detection boosted by multichannel data inputs 利用多通道数据输入促进区域山体滑坡检测的变压器嵌入式一维 VGG 卷积神经网络

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-08 DOI: 10.1016/j.envsoft.2024.106261

Bangjie Fu , Yange Li , Chen Wang , Zheng Han , Nan Jiang , Wendu Xie , Changli Li , Haohui Ding , Weidong Wang , Guangqi Chen

{"title":"Transformer-embedded 1D VGG convolutional neural network for regional landslides detection boosted by multichannel data inputs","authors":"Bangjie Fu , Yange Li , Chen Wang , Zheng Han , Nan Jiang , Wendu Xie , Changli Li , Haohui Ding , Weidong Wang , Guangqi Chen","doi":"10.1016/j.envsoft.2024.106261","DOIUrl":"10.1016/j.envsoft.2024.106261","url":null,"abstract":"<div><div>Up-to-date studies have proved the effectiveness of Convolutional Neural Networks (CNN) in landslide detection. With the rapid development of Remote Sensing and Geographic Information System technologies, an increasing amount of spectral and non-spectral information is available for CNN modeling. It offering a comprehensive perspective for landslide detection, but also presents challenges to CNNs, especially in efficiently learning long-range feature associations. Therefore, we proposed a novel Transformer-improved VGG network (Trans-VGG). It takes spectral (RGB images) and non-spectral information (elevation, slope, and PCA components) as data inputs and integrating both local and global feature in modeling. The method is tested in two landslide cluster areas in Litang County, China. The results in site a show that the Trans-VGG model demonstrates an improvement in F1-score, ranging from 4% to 21%, compared with the conventional machine learning and CNN models. The validation result in site b further proved the validity of our proposed method.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106261"},"PeriodicalIF":4.8,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven fire modeling: Learning first arrival times and model parameters with neural networks 数据驱动的火灾建模：利用神经网络学习首批到达时间和模型参数

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-06 DOI: 10.1016/j.envsoft.2024.106253

Xin Tong , Bryan Quaife

引用次数: 0

Combining residual convolutional LSTM with attention mechanisms for spatiotemporal forest cover prediction 将残差卷积 LSTM 与注意力机制相结合，用于时空森林覆盖率预测

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-04 DOI: 10.1016/j.envsoft.2024.106260

Bao Liu , Siqi Chen , Lei Gao

{"title":"Combining residual convolutional LSTM with attention mechanisms for spatiotemporal forest cover prediction","authors":"Bao Liu , Siqi Chen , Lei Gao","doi":"10.1016/j.envsoft.2024.106260","DOIUrl":"10.1016/j.envsoft.2024.106260","url":null,"abstract":"<div><div>Understanding spatiotemporal variations in forest cover is crucial for effective forest resource management. However, existing models often lack accuracy in simultaneously capturing temporal continuity and spatial correlation. To address this challenge, we developed ResConvLSTM-Att, a novel hybrid model integrating residual neural networks, Convolutional Long Short-Term Memory (ConvLSTM) networks, and attention mechanisms. We evaluated ResConvLSTM-Att against four deep learning models: LSTM, combined convolutional neural network and LSTM (CNN-LSTM), ConvLSTM, and ResConvLSTM for spatiotemporal prediction of forest cover in Tasmania, Australia. ResConvLSTM-Att achieved outstanding prediction performance, with an average root mean square error (RMSE) of 6.9% coverage and an impressive average coefficient of determination of 0.965. Compared with LSTM, CNN-LSTM, ConvLSTM, and ResConvLSTM, ResConvLSTM-Att achieved RMSE reductions of 31.2%, 43.0%, 10.1%, and 6.5%, respectively. Additionally, we quantified the impacts of explanatory variables on forest cover dynamics. Our work demonstrated the effectiveness of ResConvLSTM-Att in spatiotemporal data modelling and prediction.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106260"},"PeriodicalIF":4.8,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0