Environmental Modelling & Software最新文献_第6页

Ice-jam flood predictions using an interpretable machine learning approach 使用可解释的机器学习方法进行冰堵塞洪水预测

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-23 DOI: 10.1016/j.envsoft.2025.106534

Ananya Kowshal , Apurba Das , Karl-Erich Lindenschmidt

引用次数: 0

Harnessing Twitter (X) with AI-enhanced natural language processing for disaster management: Insights from California wildfire 利用Twitter (X)与人工智能增强的自然语言处理进行灾害管理：来自加州野火的见解

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-23 DOI: 10.1016/j.envsoft.2025.106545

Mohammadsepehr Karimiziarani, Ehsan Foroumandi, Hamid Moradkhani

{"title":"Harnessing Twitter (X) with AI-enhanced natural language processing for disaster management: Insights from California wildfire","authors":"Mohammadsepehr Karimiziarani, Ehsan Foroumandi, Hamid Moradkhani","doi":"10.1016/j.envsoft.2025.106545","DOIUrl":"10.1016/j.envsoft.2025.106545","url":null,"abstract":"<div><div>Social media usage surges during natural disasters, offering critical insights into public sentiment and needs. This study leverages artificial intelligence (AI) and advanced natural language processing (NLP) techniques to analyze Twitter (X) data from the 2018 California Camp Fire. By combining sentiment analysis, emotion classification, and humanitarian topic classification, we provide a nuanced understanding of social responses. Tweets were categorized into six humanitarian topics and twelve emotion classes, revealing significant regional and temporal variations. Our findings show shifts from immediate safety concerns to recovery and support as the disaster progressed. Results highlight key differences in emotional responses between California and other U.S. states, emphasizing the role of proximity in shaping social media discourse. These AI-driven insights can inform disaster management strategies by optimizing communication, resource allocation, and real-time decision-making. This research underscores the value of AI-powered social media analysis in enhancing disaster preparedness, response, and recovery efforts.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106545"},"PeriodicalIF":4.8,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170528","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

Unveiling uncertainties in soil organic carbon modeling: the critical role of climate response functions 揭示土壤有机碳模型中的不确定性：气候响应函数的关键作用

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-22 DOI: 10.1016/j.envsoft.2025.106537

Huiwen Li , Yue Cao , Jingfeng Xiao , Wenxin Zhang , Yiping Wu , Arshad Ali , Zuoqiang Yuan

{"title":"Unveiling uncertainties in soil organic carbon modeling: the critical role of climate response functions","authors":"Huiwen Li , Yue Cao , Jingfeng Xiao , Wenxin Zhang , Yiping Wu , Arshad Ali , Zuoqiang Yuan","doi":"10.1016/j.envsoft.2025.106537","DOIUrl":"10.1016/j.envsoft.2025.106537","url":null,"abstract":"<div><div>Accurately simulating soil organic carbon (SOC) dynamics is essential for carbon-related assessments. Process-oriented SOC models employ temperature (<em>f</em>(T)) and soil moisture (<em>f</em>(W)) response functions derived from specific conditions to simulate SOC responses to climate change, yet are widely applied in regional and global-scale studies. How these functions affect regional SOC simulations remains unclear. We evaluated the impacts of ten <em>f</em>(T) and nine <em>f</em>(W) functions using the Double Layer Carbon Model (DLCM) in the Qinling Mountains from 1982 to 2018. After calibration by Particle Swarm Optimization, DLCM estimated initial SOC with high spatial consistency (<em>R</em><sup>2</sup> > 0.9) and less than 1 % bias against machine learning based baseline maps over 85 % of the area. Different functions led to large SOC variations (up to 37 % in topsoil and 30 % in subsoil). Their combined impacts vary significantly under climate fluctuations, highlighting the need for accurate functions to improve SOC prediction in a changing climate.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106537"},"PeriodicalIF":4.8,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124922","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

Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM 研究降雨径流关系和LSTM内部的水文概念

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-22 DOI: 10.1016/j.envsoft.2025.106527

Yuqian Hu , Heng Li , Chunxiao Zhang , Tianbao Wang , Wenhao Chu , Rongrong Li

{"title":"Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM","authors":"Yuqian Hu , Heng Li , Chunxiao Zhang , Tianbao Wang , Wenhao Chu , Rongrong Li","doi":"10.1016/j.envsoft.2025.106527","DOIUrl":"10.1016/j.envsoft.2025.106527","url":null,"abstract":"<div><div>Recent studies have shown that LSTM performs well in runoff prediction in large sample regional modeling and can estimate hydrological concepts based on its internal information. However, compared to process-based models, it still produces erroneous predictions that violate the physical laws. To explore the reasons for the above phenomenon, this study analyzes the evolution of LSTM's performance in predicting runoff and estimating hydrological concepts when trained on a large basinscale dataset. Findings demonstrated that LSTM's representation of the rainfall-runoff relationship lags behind the formation of hydrological concepts. The representations of relations and concepts do not consistently increase with the number of training basins. There is a model that achieves the best representation of the rainfall-runoff relationship and hydrological concepts, ensuring physical consistency even under extreme conditions. These results suggest that LSTM, like process-based models, learns the rainfall-runoff relationship and hydrological concepts, but its confusion about these concepts may lead to inaccurate predictions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106527"},"PeriodicalIF":4.8,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138693","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

Modeling wildfire dynamics through a physics-based approach incorporating fuel moisture and landscape heterogeneity 通过结合燃料湿度和景观异质性的基于物理的方法模拟野火动力学

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-22 DOI: 10.1016/j.envsoft.2025.106511

Adrián Navas-Montilla , Cordula Reisch , Pablo Diaz , Ilhan Özgen-Xian

引用次数: 0

DigiAgriApp: a client-server application to monitor field activities DigiAgriApp：监控现场活动的客户机-服务器应用程序

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-19 DOI: 10.1016/j.envsoft.2025.106528

Marco Moretto , Luca Delucchi , Roberto Zorer , Damiano Moser , Franco Micheli , Andrea Paoli , Pietro Franceschi

{"title":"DigiAgriApp: a client-server application to monitor field activities","authors":"Marco Moretto , Luca Delucchi , Roberto Zorer , Damiano Moser , Franco Micheli , Andrea Paoli , Pietro Franceschi","doi":"10.1016/j.envsoft.2025.106528","DOIUrl":"10.1016/j.envsoft.2025.106528","url":null,"abstract":"<div><div>Farming is increasingly data-driven, leveraging high-frequency and precision data from IoT devices, sensors, and remote tools. Effective data collection, organization, and management are essential to link datasets with agronomic details, forming the foundation for predictive models. These models, using AI and machine learning, optimize decision-making, forecast crop yields, predict pest outbreaks, and enhance resource use. High-quality, diverse data integration is key to building accurate tools that address agriculture's complexity, boosting productivity and resilience. We introduce DigiAgriApp, an open-source client-server application for centralized farming data management. It tracks crop details, sensor readings, irrigation, field operations, production statistics, and emissions for Life Cycle Assessment. Initially developed for the Fondazione Edmund Mach, DigiAgriApp has evolved into a versatile tool. Users can access a public server or deploy a private instance via Docker, making it ideal for institutions, farmers, and corporations alike.</div><div>DigiAgriApp is available at <span><span>https://digiagriapp.gitlab.io/digiagriapp-website/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106528"},"PeriodicalIF":4.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124921","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

Position paper: Common mistakes and solutions for a better use of correlation- and regression-based approaches in environmental sciences 立场文件：在环境科学中更好地利用基于相关和回归的方法的常见错误和解决办法

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-19 DOI: 10.1016/j.envsoft.2025.106526

Damien Tedoldi , Boram Kim , Santiago Sandoval , Nicolas Forquet , Bruno Tassin

{"title":"Position paper: Common mistakes and solutions for a better use of correlation- and regression-based approaches in environmental sciences","authors":"Damien Tedoldi , Boram Kim , Santiago Sandoval , Nicolas Forquet , Bruno Tassin","doi":"10.1016/j.envsoft.2025.106526","DOIUrl":"10.1016/j.envsoft.2025.106526","url":null,"abstract":"<div><div>While empirical modelling remains a popular practice in environmental sciences, an alarming number of misuses of correlation- and regression-based techniques are encountered in recent research, although these techniques are described in courses and textbooks. This position paper reviews the most common issues, and provides theoretical background for understanding the interests and limitations of these methods, based on their underlying assumptions. We call for a reconsideration of misleading practices, including: the application of linear regression to data points that do not display a linear pattern, the failure to pinpoint influential points, the inappropriate extrapolation of empirical relationships, the overrated search for “statistical significance”, the pooling of data belonging to different populations, and, most importantly, calculations without data visualization. We urge reviewers to be vigilant on these aspects. We also recall the existence of alternative approaches to overcome the highlighted shortcomings, and thus contribute to a more accurate interpretation of the results.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106526"},"PeriodicalIF":4.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144194031","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

Artificial intelligence-incorporated prediction for urban flooding processes in the past 20 years: A critical review 人工智能对过去20年城市洪水过程的预测：综述

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-17 DOI: 10.1016/j.envsoft.2025.106525

Zhili Li , Zhiwei Zhou , Hao Wang , Xing Li , Xiaoyu Shi , Jiayi Xiao , Zhiyu Yang , Mingzhuang Sun , Xiaolong Li , Haifeng Jia

{"title":"Artificial intelligence-incorporated prediction for urban flooding processes in the past 20 years: A critical review","authors":"Zhili Li , Zhiwei Zhou , Hao Wang , Xing Li , Xiaoyu Shi , Jiayi Xiao , Zhiyu Yang , Mingzhuang Sun , Xiaolong Li , Haifeng Jia","doi":"10.1016/j.envsoft.2025.106525","DOIUrl":"10.1016/j.envsoft.2025.106525","url":null,"abstract":"<div><div>Urban flood forecasting is crucial for timely public warnings and effective flood management. Traditional mechanistic models face challenges such as high computational costs and limited real-time capabilities. Recent advancements in Artificial Intelligence (AI), including machine learning (ML), deep learning (DL), and large language models (LLMs), address these limitations by improving data handling, feature engineering, and forecasting accuracy. This review examines AI applications and evolution in urban flood forecasting, and features about commonly applied models such as convolutional neural networks (CNN), random forest (RF), long short-term memory (LSTM), and support vector machines (SVM). A comprehensive analysis compares various AI algorithms based on input parameters, output variables, forecasting lead time, and prediction accuracy. Key input parameters (\"Rainfall,\" \"Water depth,\" \"Elevation\") and output variables (\"Inundation depth,\" \"Inundation area,\" \"Flow\") were identified. Future research directions aim to enhance AI-driven forecasting precision for improved emergency response.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106525"},"PeriodicalIF":4.8,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115340","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

Improving localized weather predictions for precision agriculture: A Time-Series Mixer approach for hazardous event detection 改进精准农业的局部天气预报：用于危险事件检测的时间序列混合方法

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-15 DOI: 10.1016/j.envsoft.2025.106509

Marco Zanchi , Stefano Zapperi , Stefano Bocchi , Oxana Drofa , Silvio Davolio , Caterina A.M. La Porta

{"title":"Improving localized weather predictions for precision agriculture: A Time-Series Mixer approach for hazardous event detection","authors":"Marco Zanchi , Stefano Zapperi , Stefano Bocchi , Oxana Drofa , Silvio Davolio , Caterina A.M. La Porta","doi":"10.1016/j.envsoft.2025.106509","DOIUrl":"10.1016/j.envsoft.2025.106509","url":null,"abstract":"<div><div>Natural environmental systems and human activities are deeply interconnected, especially in agriculture. Despite advancements in agricultural techniques, weather remains a critical factor influencing crop yields and livestock health. Precision agriculture relies on weather predictions to mitigate environmental risks caused by weather. However, numerical weather predictions are generated by global or regional numerical models, lacking the resolution to capture site-specific conditions. Artificial intelligence can address this gap by integrating numerical weather predictions data with local station observations. This study employs the Time-Series Mixer (TSMixer) neural network to forecast temperature, wind speed, relative humidity, and precipitation over a 45-hour horizon. Trained with predictions from the MOLOCH model and data from ARPA stations near six agricultural sites in Northern Italy, TSMixer achieves greater accuracy than the MOLOCH model. Additionally, TSMixer excels in detecting hazardous events for precision agriculture, including frost damage, heat stress, and germination block, highlighting its value for environmental risk management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"191 ","pages":"Article 106509"},"PeriodicalIF":4.8,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067547","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

Forecasting flood inundations in the dam-regulated Mahanadi River delta using integrated hydrologic-hydrodynamic-deep learning model 基于水文-水动力-深度学习综合模型的坝控马哈纳迪河三角洲洪涝预报

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-15 DOI: 10.1016/j.envsoft.2025.106523

Amina Khatun , Prachi Pratyasha Jena , Bhabagrahi Sahoo , Chandranath Chatterjee

{"title":"Forecasting flood inundations in the dam-regulated Mahanadi River delta using integrated hydrologic-hydrodynamic-deep learning model","authors":"Amina Khatun , Prachi Pratyasha Jena , Bhabagrahi Sahoo , Chandranath Chatterjee","doi":"10.1016/j.envsoft.2025.106523","DOIUrl":"10.1016/j.envsoft.2025.106523","url":null,"abstract":"<div><div>The efficacy of a deep learning error-updating model in predicting the hydrological model-simulated errors influenced by reservoir regulation is assessed. Two daily discharge forecasting model frameworks without (Case I) and with (Case II) error-updating of the discharge forecasts at a downstream location are developed. The best discharge forecasts are forced as inputs to a hydrodynamic model to simulate the forecasted flood inundations in the downstream region. The findings reveals that the discharge forecasts with the forecasted releases from the reservoir as upstream inflow boundary, post-error updating at the delta head (Case II) outperforms Case I with an <span><math><mrow><mi>N</mi><mi>S</mi><mi>E</mi></mrow></math></span> value of 0.83–0.94 at 1–5 days lead times. Moreover, this model (Case II) captures the flood peaks with the least error and narrowest uncertainty bands. Further, with a 49–52 % coincidence of observed and simulated flood inundation extent, the hydrodynamic model simulates the inundation forecasts with reasonable accuracy up to 5-days lead times.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"191 ","pages":"Article 106523"},"PeriodicalIF":4.8,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089072","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