Applied Computing and Geosciences最新文献_第2页

Predictive regressive models of recent marsh sediment thickness improve the quantification of coastal marsh sediment budgets

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-02-01 DOI: 10.1016/j.acags.2024.100215

Christopher G. Smith , Julie Bernier , Alisha M. Ellis , Kathryn E.L. Smith

Coastal marsh wetlands experience variations in vertical gains and losses through time, which have allowed them to infill relict topography and record variations in drivers. The stratigraphic unit associated with the development of the marsh also reflects the long-term importance of key ecosystem services supplied by the marsh environment, including carbon storage and storm mitigation. Mapping these coastal wetland sediments and the marsh unit thickness is challenging as traditional coastal geophysical tools are not easily deployable (acoustic methods) or are unreliable in saline-soil environments (e.g., ground-penetrating radar), leaving core-based methods the most viable mapping method. In the present study, we utilized prior information on the geologic architecture of the region to select spatial and physical metrics that likely persisted throughout evolution of the marsh during the late Holocene. We then assessed the individual and collective power of these metrics to predict marsh thickness observed from cores. Employing regressive predictive models powered by these data, we improve the quantification of marsh thickness for a coastal fringing marsh within the Grand Bay estuary in Mississippi and Alabama (USA). The information gained from this approach yields improved estimates of the carbon stocks in this environment. Additionally, the stored sediment masses reflect the past, and potential future, persistence of the Grand Bay marsh under historical and present marsh-estuarine sediment exchange fluxes. Such improvements to both the sediment budget of recent marsh stratigraphic units and the spatial extent provide new resources for comparison with large-scale landscape models, the latter of which may be used, when validated, to predict future change and ecosystem transformations.

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引用次数: 0

X-ray Micro-CT based characterization of rock cuttings with deep learning

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-02-01 DOI: 10.1016/j.acags.2025.100220

Nils Olsen , Yifeng Chen , Pascal Turberg , Alexandre Moreau , Alexandre Alahi

Rock cuttings from destructive boreholes are a common and cheaper source of drilling materials that can be used to determine underground geology compared to rock core samples. Classifying manually the series of cuttings can be a long and tedious process and can also be prone to subjectivity leading to errors. In this paper, a framework for the classification of multiple types of rock structures is introduced based on rock cutting images from X-ray micro-CT technology. The classification is performed using a simple yet effective deep learning model (a ResNet-18 architecture) to categorize five different lithologies: micritic limestone, bioclastic limestone, oolithic limestone, molassic sandstone and gneiss. The proposed network is trained on 2 datasets (laboratory and borehole) both containing the five lithologies and comprise over 10 000 images. The laboratory dataset consists of a well-controlled experiments with homogeneous samples and the borehole dataset with heterogeneous samples corresponding to a real case application. Among all the considered models, including ResNet-34, and SPP-CNN and human experts manual classification, ResNet-18 demonstrates superior performance across multiple evaluation metrics, including precision, recall, and F1-score. It is to our best knowledge, the first time a test comparing deep neural network and human performance is performed for this task. To optimize the performance of the proposed model, the transfer learning method is implemented. Furthermore, the experiments demonstrate that when employing transfer learning, the size of the dataset significantly impacts the performance of the model. In the studied design, the experimental results confirm that the proposed approach is a cost-effective and efficient method for automated rock cutting classification using the micro-CT technique, and it can be easily modified to adapt the rock cutting classification from various types and sources.

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引用次数: 0

Streamlining geoscience data analysis with an LLM-driven workflow

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-02-01 DOI: 10.1016/j.acags.2024.100218

Jiyin Zhang, Cory Clairmont, Xiang Que, Wenjia Li, Weilin Chen, Chenhao Li, Xiaogang Ma

Large Language Models (LLMs) have made significant advancements in natural language processing and human-like response generation. However, training and fine-tuning an LLM to fit the strict requirements in the scope of academic research, such as geoscience, still requires significant computational resources and human expert alignment to ensure the quality and reliability of the generated content. The challenges highlight the need for a more flexible and reliable LLM workflow to meet domain-specific analysis needs. This study proposes an LLM-driven workflow that addresses the challenges of utilizing LLMs in geoscience data analysis. The work was built upon the open data API (application programming interface) of Mindat, one of the largest databases in mineralogy. We designed and developed an open-source LLM-driven workflow that processes natural language requests and automatically utilizes the Mindat API, mineral co-occurrence network analysis, and locality distribution heat map visualization to conduct geoscience data analysis tasks. Using prompt engineering techniques, we developed a supervisor-based agentic framework that enables LLM agents to not only interpret context information but also autonomously addressing complex geoscience analysis tasks, bridging the gap between automated workflows and human expertise. This agentic design emphasizes autonomy, allowing the workflow to adapt seamlessly to future advancements in LLM capabilities without requiring additional fine-tuning or domain-specific embedding. By providing the comprehensive context of the task in the workflow and the professional tool, we ensure the quality of LLM-generated content without the need to embed geoscience knowledge into LLMs through fine-tuning or human alignment. Our approach integrates LLMs into geoscience data analysis, addressing the need for specialized tools while reducing the learning curve through LLM-driven interactions between users and APIs. This streamlined workflow enhances the efficiency of exploratory data analysis, as demonstrated by the several use cases presented. In our future work we will explore the scalability of this workflow through the integration of additional agents and diverse geoscience data sources.

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引用次数: 0

Skillful prediction of Indian Ocean Dipole index using machine learning models

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-02-01 DOI: 10.1016/j.acags.2025.100228

J.V. Ratnam, Swadhin K. Behera, Masami Nonaka, Kalpesh R. Patil

In this study, we evaluated six machine learning models for their skill in predicting the Indian Ocean Dipole (IOD). The results based on the IOD index predictions at 1–8 month lead time indicate that the AdaBoost model with Multi-Layer Perceptron as the base estimator, AdaBoost(MLP), to perform better than the other five models in predicting the IOD index at all lead times. Interestingly, the IOD predictions of AdaBoost(MLP) had an anomaly correlation coefficient above 0.6 at almost all lead times. The results suggest that the AdaBoost(MLP) machine learning model to be a promising tool for predicting the IOD index with a long lead time of 8 months. Analysis revealed that the machine learning model predictions are aided by the signals from the Pacific region, owing to co-occurrences of some of the IODs with El Nino-Southern Oscillations.

引用次数: 0

Integrating empirical analysis and deep learning for accurate monsoon prediction in Kerala, India

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2024-12-01 DOI: 10.1016/j.acags.2024.100211

Yajnaseni Dash, Ajith Abraham

Kerala, a coastal state in India characterized by its humid tropical monsoon climate, is profoundly influenced by the Western Ghats and the Arabian Sea. Kerala receives significant rainfall during both the southwest monsoon (June to September, JJAS) and the northeast monsoon (October to December, OND) seasons. Given the substantial impact of rainfall on the state's economy and livelihoods, accurate precipitation forecasting is of critical importance. Although Kerala's annual rainfall is approximately 2.5 times higher than the national average, the state frequently experiences water scarcity due to rapid runoff into the Arabian Sea. This study builds upon previous research concerning Kerala's rainfall patterns and introduces a novel approach to improving rainfall predictions. Usage of a hybrid model that integrates Empirical Mode Decomposition (EMD) with Detrended Fluctuation Analysis (DFA) and deep Long Short-Term Memory (LSTM) neural networks, demonstrates enhanced precision in forecasting. Thus, by integrating empirical data analysis with advanced deep learning techniques, this research offers a robust framework for predicting rainfall in Kerala, making a significant contribution to the field of climate informatics and providing practical benefits for the region's economy and environmental management.

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引用次数: 0

CloudSense: A model for cloud type identification using machine learning from radar data CloudSense：一个利用雷达数据进行机器学习的云类型识别模型

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2024-12-01 DOI: 10.1016/j.acags.2024.100209

Mehzooz Nizar , Jha K. Ambuj , Manmeet Singh , S.B. Vaisakh , G. Pandithurai

The knowledge of type of precipitating cloud is crucial for radar based quantitative estimates of precipitation. We propose a novel model called CloudSense which uses machine learning to accurately identify the type of precipitating clouds over the complex terrain locations in the Western Ghats (WG) of India. CloudSense uses vertical reflectivity profiles collected during July–August 2018 from an X-band radar to classify clouds into four categories namely stratiform, mixed stratiform-convective, convective and shallow clouds. The machine learning (ML) model used in CloudSense was trained using a dataset balanced by Synthetic Minority Oversampling Technique (SMOTE), with features selected based on physical characteristics relevant to different cloud types. Among various ML models evaluated Light Gradient Boosting Machine (LightGBM) demonstrate superior performance in classifying cloud types with a BAC (Balanced Accuracy) of 0.79 and F1-Score of 0.8. CloudSense generated results are also compared against conventional radar algorithms and we find that CloudSense performs better than radar algorithms. For 200 samples tested, the radar algorithm achieved a BAC of 0.69 and F1-Score of 0.68, whereas CloudSense achieved a BAC of 0.8 and F1-Score of 0.79. Our results show that ML based approach can provide more accurate cloud detection and classification which would be useful to improve precipitation estimates over the complex terrain of the WG.

降水云类型的知识对于基于雷达的降水定量估计至关重要。我们提出了一个名为CloudSense的新模型，该模型使用机器学习来准确识别印度西高止山脉（WG）复杂地形位置上的降水云类型。CloudSense使用2018年7月至8月从x波段雷达收集的垂直反射率剖面将云分为四类，即层状云、层-对流混合云、对流云和浅云。CloudSense中使用的机器学习（ML）模型使用由合成少数派过采样技术（SMOTE）平衡的数据集进行训练，并根据与不同云类型相关的物理特征选择特征。在评估的各种ML模型中，光梯度增强机（LightGBM）在云类型分类方面表现优异，BAC（平衡精度）为0.79，F1-Score为0.8。CloudSense生成的结果也与传统雷达算法进行了比较，我们发现CloudSense的性能优于雷达算法。在测试的200个样本中，雷达算法的BAC为0.69，F1-Score为0.68，而CloudSense的BAC为0.8，F1-Score为0.79。我们的研究结果表明，基于机器学习的方法可以提供更准确的云检测和分类，这将有助于改善WG复杂地形上的降水估计。

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引用次数: 0

Enhancing prediction of fluid-saturated fracture characteristics using deep learning super resolution

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2024-12-01 DOI: 10.1016/j.acags.2024.100208

Manju Pharkavi Murugesu , Vignesh Krishnan , Anthony R. Kovscek

Utilization of subsurface resources is essential to achieve energy sustainability including large-scale CO

_{2}

sequestration, H

_{2}

storage, geothermal energy extraction, and hydrocarbon recovery. In-situ visualization of fluid flow in geological media is essential to understand complex, coupled, physical and chemical processes underlying fluid injection, storage, extraction. X-ray Computed Tomography (CT) in the laboratory has proven beneficial to visualize changes in the flow field with rapid temporal resolution (10’s s) and moderate spatial resolution (100’s

μ m

). There is a trade-off between spatial and temporal resolution that limits accurate characterization of dynamics in rock features that are below spatial resolution of CT. While past literature has offered solutions to improve resolution of CT rock images, including deep learning-based algorithms, our study uniquely focuses on improving dynamic, partially and fully fluid-saturated geological images. Fluid-saturated CT images offer additional information, through augmented signals provided by the presence of fluid. Among challenges, CT images of geological media inherently possess limited information due to their single-channel gray-scale source. Additionally, fluid flows through partially saturated media frustrate existing super resolution techniques because unsaturated CT images are an inaccurate proxy for saturated dynamic rock images. The novelty of this work is the expansion of a generative adversarial network (GAN) for applications involving super resolution of partially saturated low resolution CT images using end-member, unsaturated high resolution

μ

CT images. To this end, we acquired multiscale low- and high-resolution CT rock images in unsaturated and saturated states. Among GAN and convolutional neural networks, GAN’s produce realistic high-resolution reconstructions of saturated geological media when trained using high-resolution, unsaturated images and lower resolution images in various saturation states. The model has direct usefulness for interpretation of real-time images.

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引用次数: 0

An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing images

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2024-12-01 DOI: 10.1016/j.acags.2024.100210

Yanbo Sun , Wenxing Bao , Wei Feng , Kewen Qu , Xuan Ma , Xiaowu Zhang

Change detection (CD) is a meaningful and challenging task for remote sensing (RS) image analysis. Deep learning (DL) based methods have shown great potential in change detection tasks, there are still two problems with existing deep learning methods such as CNN and Transformer: (1) They do not target different depths to extract global semantics in the network; (2) The increase in network depth will lead to uncertainty in the edge pixels of changing targets and the absence of small targets. First, to address this challenge and address these issues, this work proposes a global pyramid aggregation UNet3+ (GPA-UNet3+) change detection model, that uses UNet3+ as the backbone network and connects the encoder and decoder with a pyramid structure. Secondly, a Global Atrous Spatial Pooling Pyramid Module (GASPPM) is proposed. Refined features at different depths and aggregated them to enhance the network’s ability to extract global semantics. Finally, the Edge Enhancement Channel Attention Module (EECAM) is specifically proposed to alleviate the edge pixel uncertainty and spatial position information loss caused by the increase in network depth. Multiple experiments are conducted on two common change detection datasets and a real dataset. Extensive experimental results show that the proposed method outperforms other state-of-the-art methods, achieving the highest F1-score of 90.95%, 95.31%, and 88.32% on the LEVIR-CD dataset, SVCD dataset and Shizuishan Mining Area dataset, respectively.

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引用次数: 0

A machine learning approach for mapping susceptibility to land subsidence caused by ground water extraction 绘制地下水开采导致土地沉降易感性地图的机器学习方法

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2024-11-16 DOI: 10.1016/j.acags.2024.100207

Diana Orlandi , Esteban Díaz , Roberto Tomás , Federico A. Galatolo , Mario G.C.A. Cimino , Carolina Pagli , Nicola Perilli

Land subsidence is a worldwide threat that may cause irreversible damage to the environment and the infrastructures. Thus, identifying and mapping areas prone to land subsidence with accurate methods such as Land Subsidence Susceptibility Index (LSSI) mapping is crucial for mitigating the adverse impacts of this geohazard. Also, Machine Learning (ML) is now becoming a powerful tool to analyze vast and different datasets such as those necessary for LSSI mapping. In this study, we use the conventional Frequency Ratio (FR) method and ML models to generate LSSI maps of the region of Murcia (Spain) where land subsidence occurred in the past due to groundwater overdraft. A LSSI map was initially generated with known FR. Then, additional Conditioning Factors (CFs) with increased spatial resolution were used to train several ML models and generate a new LSSI map. The Extra-Trees Classifier (ETC) outperformed the other approaches, achieving the best performance with a weighted average precision and F1-Score of 0.96, after optimizing its hyperparameters. Then, a third LSSI map was calculated using the FR method and observations of land subsidence from InSAR (Interferometric Synthetic Aperture Radar). This study shows that the effectiveness of using several CFs depends on the added information of each layer. Moreover, the comparison between the different LSSI maps and InSAR data highlights the crucial role of the spatial resolution for accurate mapping, thus enhancing land subsidence risk assessment.

土地沉降是一种全球性威胁，可能对环境和基础设施造成不可逆转的破坏。因此，采用土地沉降易感指数（LSSI）绘图等精确方法识别和绘制易发生土地沉降的地区，对于减轻这种地质灾害的不利影响至关重要。此外，机器学习（ML）目前已成为分析大量不同数据集（如 LSSI 测绘所需的数据集）的有力工具。在本研究中，我们使用传统的频率比（FR）方法和 ML 模型来生成穆尔西亚地区（西班牙）的 LSSI 地图，该地区过去曾因地下水超采而发生过地面沉降。LSSI 地图最初是根据已知 FR 生成的。然后，使用空间分辨率更高的附加条件因子（CF）来训练多个 ML 模型，生成新的 LSSI 地图。优化超参数后，树外分类器（ETC）的表现优于其他方法，加权平均精度和 F1 分数均达到 0.96。然后，利用 FR 方法和 InSAR（干涉合成孔径雷达）的地面沉降观测数据计算了第三幅 LSSI 地图。这项研究表明，使用多个 CF 的有效性取决于每个层的附加信息。此外，不同的 LSSI 地图和 InSAR 数据之间的比较凸显了空间分辨率对精确制图的关键作用，从而加强了土地沉降风险评估。

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引用次数: 0

Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning 彻底改变水文科学的未来：机器学习和深度学习在新兴可解释人工智能和迁移学习中的影响

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2024-11-09 DOI: 10.1016/j.acags.2024.100206

Rajib Maity, Aman Srivastava, Subharthi Sarkar, Mohd Imran Khan

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are revolutionizing hydrology, driving significant advancements in water resource management, modeling, and prediction. This review synthesizes cutting-edge developments, methodologies, and applications of AI-ML-DL across key hydrological processes. By critically evaluating these techniques against traditional models, we highlight their superior ability to capture complex, nonlinear relationships and adapt to diverse environments. We further explore AI applications in precipitation forecasting, evapotranspiration estimation, groundwater dynamics, and extreme event prediction (floods, droughts, and compound events), showcasing their timely potential in addressing critical water-related challenges. A particular emphasis is placed on Explainable AI (XAI) and transfer learning as essential tools for improving model transparency and applicability, enabling broader stakeholder trust and cross-regional adaptability. The review also addresses persistent challenges, including data limitations, computational demands, and model interpretability, proposing solutions that integrate emerging technologies like quantum computing, the Internet of Things (IoT), and interdisciplinary collaboration. This review charts a strategic course for future research and practice by bridging AI advancements with practical hydrological applications. Our findings highlight the importance of embracing AI-driven approaches for next-generation hydrological modeling and provide actionable understandings for researchers, practitioners, and policymakers. As hydrology faces escalating challenges due to human-induced climate change and growing water demands, the continued evolution of AI-integrated models and innovations in data handling and stakeholder engagement will be imperative. In conclusion, the findings emphasize the critical role of AI-driven hydrological modeling in addressing global water challenges, including climate change adaptation, sustainable water resource management, and disaster risk reduction.

人工智能（AI）、机器学习（ML）和深度学习（DL）正在彻底改变水文学，推动水资源管理、建模和预测领域的重大进步。本综述综合了人工智能-ML-DL 在关键水文过程中的前沿发展、方法和应用。通过对这些技术与传统模型进行严格评估，我们强调了这些技术在捕捉复杂的非线性关系和适应不同环境方面的卓越能力。我们进一步探讨了人工智能在降水预报、蒸散估计、地下水动力学和极端事件预测（洪水、干旱和复合事件）中的应用，展示了它们在应对与水有关的重大挑战方面的及时潜力。其中特别强调了可解释人工智能（XAI）和迁移学习，将其作为提高模型透明度和适用性的重要工具，从而实现更广泛的利益相关者信任和跨区域适应性。综述还探讨了长期存在的挑战，包括数据限制、计算需求和模型可解释性，并提出了整合量子计算、物联网（IoT）和跨学科合作等新兴技术的解决方案。本综述将人工智能的进步与实际水文应用相结合，为未来的研究和实践指明了战略方向。我们的研究结果强调了采用人工智能驱动的方法进行下一代水文建模的重要性，并为研究人员、从业人员和决策者提供了可操作的理解。由于人类引起的气候变化和日益增长的水资源需求，水文学面临着不断升级的挑战，因此，人工智能集成模型的持续发展以及数据处理和利益相关者参与方面的创新势在必行。总之，研究结果强调了人工智能驱动的水文建模在应对全球水资源挑战（包括适应气候变化、可持续水资源管理和减少灾害风险）方面的关键作用。

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引用次数: 0