Environmental Modelling & Software最新文献_第10页

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

Modeling the impact of smoke from prescribed fire on road visibility 模拟规定火灾产生的烟雾对道路能见度的影响

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.2025.106510

Sara Brambilla, Diego Rojas, David J. Robinson, Alexander J. Josephson, Matthew A. Nelson, Rodman R. Linn

引用次数: 0

Stochastic generator for rainfall with a Hawkes process marked by an extended generalized Pareto and a vine copula 具有扩展广义Pareto和藤蔓联结的Hawkes过程的降雨随机发生器

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.2025.106490

Antoine Chapon , Taha B.M.J. Ouarda , Nathalie Bertrand

{"title":"Stochastic generator for rainfall with a Hawkes process marked by an extended generalized Pareto and a vine copula","authors":"Antoine Chapon , Taha B.M.J. Ouarda , Nathalie Bertrand","doi":"10.1016/j.envsoft.2025.106490","DOIUrl":"10.1016/j.envsoft.2025.106490","url":null,"abstract":"<div><div>A stochastic generator for rainfall is built from a Hawkes process, which is modeling the occurrence and serial correlation of non-zero rainfall values. Hawkes processes are suited to model intermittent signals, which is the case of rainfall at a fine enough observation frequency. This Hawkes process has a two-scale intensity function accounting for two orders of clustering in rainfall time series. The rainfall amount of each non-zero value is modeled by an extended generalized Pareto (EGP) distribution with the whole range of rainfall as support, from low to extreme values. New parametric EGP forms adapted to high frequency rainfall time series are defined. The Hawkes process only models the serial correlation of occurrences but not that of the amounts. A conditional version of the EGP is hence developed by adding a copula, modeling the temporal dependence of rainfall amounts. A subsettable canonical vine copula models this dependency for multiple time lags, while accounting for the intermittency of non-zero rainfall values. An application to a 40 yr time series of hourly rainfall in France is presented. Simulations from the model reproduce adequately the marginal distribution of rainfall, the temporal clustering of events, and the autocorrelation. The simulations are also able to reproduce the intensity-duration-frequency relation of the IDF extreme value model, showing that this stochastic generator is suitable for risk assessment of duration-dependent extremes.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"191 ","pages":"Article 106490"},"PeriodicalIF":4.8,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084517","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

An adaptive rainfall-runoff model for daily runoff prediction under the changing environment: Stream-LSTM 变化环境下日径流预报的自适应降雨径流模型：Stream-LSTM

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.2025.106524

Feichi Hu , Qinli Yang , Junran Yang , Junming Shao , Guoqing Wang

{"title":"An adaptive rainfall-runoff model for daily runoff prediction under the changing environment: Stream-LSTM","authors":"Feichi Hu , Qinli Yang , Junran Yang , Junming Shao , Guoqing Wang","doi":"10.1016/j.envsoft.2025.106524","DOIUrl":"10.1016/j.envsoft.2025.106524","url":null,"abstract":"<div><div>The rainfall-runoff relationship frequently undergoes changes and exhibits a non-stationary state due to the impacts of climate and human activities. This non-stationarity often results in performance degradation of most existing runoff prediction models, which were designed and applied under the assumption of a stationary rainfall-runoff relationship. This study proposes an adaptive rainfall-runoff model (Stream-LSTM (Stream-Long Short-Term Memory)) based on data stream mining and deep learning for 1-day-ahead daily runoff prediction. The model consists of two main components: (1) a dynamic threshold adjustment strategy that automatically detects changes in the rainfall-runoff relationship, and (2) a network fine-tuning approach that preserves long-term memory while adapting to new patterns. Results in the Source Region of the Yellow River basin demonstrate that the proposed model achieves Nash-Sutcliffe Efficiency (NSE) of 0.91 with a decay period of 10 in a changing environment, outperforming the widely used data-driven models such as Long Short-Term Memory (LSTM) (0.71), Random Forest (RF) (0.66), eXtreme Gradient Boosting (XGBoost) (0.69) and Support Vector Regression (SVR) (0.61). The Stream-LSTM model performed well on peak runoff prediction with NSE values generally exceeding 0.70. Additionally, preliminary inter-basin testing on two selected basins in the CAMELS dataset indicates the potential applicability of the method. This study provides a promising method for dynamic daily runoff prediction in a non-stationary environment, which is of great significance for flood mitigation and regional water resource management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"191 ","pages":"Article 106524"},"PeriodicalIF":4.8,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089316","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 the consistency of hydrologic event identification 提高水文事件识别的一致性

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-13 DOI: 10.1016/j.envsoft.2025.106521

Mohammad Masoud Mohammadpour Khoie , Danlu Guo , Conrad Wasko

{"title":"Improving the consistency of hydrologic event identification","authors":"Mohammad Masoud Mohammadpour Khoie , Danlu Guo , Conrad Wasko","doi":"10.1016/j.envsoft.2025.106521","DOIUrl":"10.1016/j.envsoft.2025.106521","url":null,"abstract":"<div><div>Identifying rainfall-runoff events is routinely performed in many hydrologic applications. Absence of a ground-based truth makes rainfall-runoff event identification largely subjective. As a result, current algorithms often disagree on the start and end of events, leading to events within a given set of rainfall and runoff time-series with inconsistent properties – referred to hereafter as ‘uncertainty in rainfall-runoff event identification’. In this study, the uncertainty associated with identifying rainfall-runoff events is assessed across Australia. A considerable uncertainty exists in the characteristics of identified rainfall-runoff events, including in their Runoff Coefficients (RCs). We propose a new objective metric to narrow the plausible set of parameters for identifying rainfall-runoff events. The metric demonstrates a substantial reduction in the uncertainty in rainfall-runoff event identification while improving the plausibility of the rainfall-runoff events chosen (up to a 25 % reduction in RCs >1) making the metric applicable for large-sample analyses of rainfall-runoff events.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"191 ","pages":"Article 106521"},"PeriodicalIF":4.8,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067559","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

Regulation of efficient water use in paddy fields via the simulation of the water cycle in cold regions under random precipitation conditions 随机降水条件下寒区水循环模拟对水田高效用水的调控

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-12 DOI: 10.1016/j.envsoft.2025.106520

Mo Li , Kun Hu , Qiang Fu , Aizheng Yang , Xiaofang Wang , Pingan Zhang , Wenhao Dong , Zhenyi Sun

{"title":"Regulation of efficient water use in paddy fields via the simulation of the water cycle in cold regions under random precipitation conditions","authors":"Mo Li , Kun Hu , Qiang Fu , Aizheng Yang , Xiaofang Wang , Pingan Zhang , Wenhao Dong , Zhenyi Sun","doi":"10.1016/j.envsoft.2025.106520","DOIUrl":"10.1016/j.envsoft.2025.106520","url":null,"abstract":"<div><div>The unique freeze‒thaw cycle in cold regions complicates irrigation. Field monitoring and experiments simulated the water cycle during thawing and growing periods, analyzing hydraulic connections. This led to coupling a hydrological balance model, the Environmental Policy Integrated Climate (EPIC) model, and a carbon emission model into a multi-objective optimization framework for rice irrigation, aiming to enhance production, save water, and reduce emissions. Using Monte Carlo simulation and the Non-dominated Sorting Genetic Algorithm III (NSGA-III), dynamic water distribution plans were developed considering precipitation variability. Modeling the 0–60 cm soil layer as continuous improved soil moisture simulation, resulted in soaking irrigation with 16 %–21.8 % water savings. Optimized irrigation increased maximum yield by 1.6 %–4.7 %, reduced carbon emissions per unit yield by 16.4 %–18.6 %, and saved 7.7 %–9.5 % water compared to conventional methods. Key allocation periods are tillering and jointing-booting initiation, optimizing distributions for 57 %–72 % of the growth period, supporting sustainable water management in cold-region rice paddies.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106520"},"PeriodicalIF":4.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115237","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

Applying user-centred design to climate and environmental tools 将以用户为中心的设计应用于气候和环境工具

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-11 DOI: 10.1016/j.envsoft.2025.106519

Joske Houtkamp, Sander Janssen, Rob Lokers, Hugo de Groot

引用次数: 0

Geometric approach based tool for shallow landslides propagation assessment (ShaLPA) at basin scale 基于几何方法的盆地尺度浅层滑坡传播评价工具

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-10 DOI: 10.1016/j.envsoft.2025.106512

Luca Maria Falconi , Lorenzo Moretti , Claudio Puglisi , Gaia Righini

{"title":"Geometric approach based tool for shallow landslides propagation assessment (ShaLPA) at basin scale","authors":"Luca Maria Falconi , Lorenzo Moretti , Claudio Puglisi , Gaia Righini","doi":"10.1016/j.envsoft.2025.106512","DOIUrl":"10.1016/j.envsoft.2025.106512","url":null,"abstract":"<div><div>Hazard maps for shallow landslides at the basin or regional scale often provide information solely about past events and/or potential source areas. Despite the availability of several propagation assessment software tools, runout maps for potential shallow landslides at the basin scale remain scarce.</div><div>To address this gap, the ShaLPA runout GIS tool was developed as an easy-to-use and efficient solution. Based on a geometric approach, the tool consists of five distinct, sequential scripts that begin with defined source areas. By processing a detailed Digital Terrain Model, the first script identifies the starting points and the second traces the potential paths of shallow landslides. The third script calculates the runout, while the fourth estimates velocity distribution and kinetic energy along the paths. The fifth script assess the reliability of the model results using two different indicators.</div><div>ShaLPA was first tested in the Giampilieri and Briga area (Sicily, Italy), providing encouraging results. The simplicity of the ShaLPA tool promotes the integration of runout and failure susceptibility analyses, enhancing the comprehensiveness of hazard and risk assessment and improving the effectiveness of landslide mitigation measures.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"191 ","pages":"Article 106512"},"PeriodicalIF":4.8,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934899","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