Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2023, Номер 120, С. 103300 - 103300

Опубликована: Апрель 28, 2023

Geocomputation and geospatial artificial intelligence (GeoAI) have essential roles in advancing geographic information science (GIS) Earth observation to a new stage. GeoAI has enhanced traditional analysis mapping, altering the methods for understanding managing complex human–natural systems. However, there are still challenges various aspects of applications related natural, built, social environments, integrating unique features into models. Meanwhile, data critical components geocomputation studies, as they can effectively reveal patterns, factors, relationships, decision-making processes. This editorial provides comprehensive overview classifying them four categories: (i) buildings infrastructure, (ii) land use analysis, (iii) natural environment hazards, (iv) issues human activities. In addition, summarizes case studies seven categories, including in-situ data, datasets, crowdsourced (i.e., big data), remote sensing photogrammetry LiDAR, statistical data. Finally, presents opportunities future research.

Язык: Английский

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Improving rainfall-runoff modeling in the Mekong river basin using bias-corrected satellite precipitation products by convolutional neural networks

Journal of Hydrology, Год журнала: 2024, Номер 630, С. 130762 - 130762

Опубликована: Янв. 24, 2024

Accurate rainfall-runoff (RR) modeling is crucial for effective Mekong River Basin (MRB) water resource management. Satellite precipitation products (SPPs) can offer valuable data such modeling; however, these often exhibit biases that may adversely affect hydrological simulations. This study aimed to improve RR using bias-corrected SPPs and the Soil Water Assessment Tool (SWAT) model MRB. A convolutional neural network-based deep learning framework was employed correct in four (TRMM, PERSIANN-CDR, CHIRPS, CMORPH), resulting respective (ADJ_TRMM, ADJ_CDR, ADJ_CHIR, ADJ_CMOR). The were compared against a gauge-based dataset terms of rainfall analysis, their performance within SWAT assessed over calibration (2004-2013) validation (2014-2015). Bias-corrected demonstrated superior with ADJ_TRMM outperforming other products. results showed satisfactory across all stations, Nash-Sutcliffe Efficiency (NSE) ranging from [0.76-0.87]. Integrating into significantly increased simulations MRB, indicated by higher NSE values [0.72-0.85] uncorrected [-0.37 0.85] at Kratie station. Besides, inconsistent between analysis observed, ADJ_CDR model. These highlight significance applications, especially areas limited ground-based data, need further research refine bias correction methods address limitations

Язык: Английский

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Enhancing wildfire mapping accuracy using mono-temporal Sentinel-2 data: A novel approach through qualitative and quantitative feature selection with explainable AI

Ecological Informatics, Год журнала: 2024, Номер 81, С. 102601 - 102601

Опубликована: Апрель 16, 2024

Accurate wildfire severity mapping (WSM) is crucial in environmental damage assessment and recovery strategies. Machine learning (ML) remote sensing technologies are extensively integrated employed as powerful tools for WSM. However, the intricate nature of ML algorithms often leads to 'black box' systems, obscuring decision-making process significantly limiting stakeholders' ability comprehend basis predictions. This opacity hinders efforts enhance performance risks exacerbating overfitting. present study proposes an innovative WSM approach that incorporates qualitative quantitative feature selection techniques within Explainable AI (XAI) framework. The methodology aims precision provide insights into factors contributing model decisions, thereby increasing interpretability predictions streamlining models improve performance. To achieve this objective, we SHapley Additive exPlanations (SHAP)-Forward Stepwise Selection (FSS) method demonstrate its efficacy elucidating impacts predictors on algorithm performance, accuracy, designed Utilizing post-fire imagery from Sentinel-2 (S2), analyzed ten bands generate 225 unique spectral indices utilizing five different calculations: normalized, algebraic sum, difference, ratio, product forms. Combined with original S2 bands, resulted 235 potential classifications. A random forest was subsequently developed using these optimized through extensive hyperparameter tuning, achieving overall accuracy (OA) 0.917 a Kappa statistic 0.896. most influential were identified SHAP values, FSS narrowing them down 12 critical effective WSM, evidenced by stabilized OA values (0.904 0.881, respectively). Further validation ninefold spatial cross-validation technique demonstrated method's consistent across data partitions, ranging 0.705 0.894 0.607 0.867. By providing more accurate comprehensible XAI-based research contributes broader field monitoring disaster response, underscoring analysis models' capabilities.

Язык: Английский

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From bias to accuracy: Transforming satellite precipitation data in arid regions with machine learning and topographical insights

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132801 - 132801

Опубликована: Фев. 1, 2025

Язык: Английский

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Development of Multi-Source Weighted-Ensemble Precipitation: Influence of bias correction based on recurrent convolutional neural networks

Journal of Hydrology, Год журнала: 2024, Номер 629, С. 130621 - 130621

Опубликована: Янв. 6, 2024

Accurate precipitation information is the cornerstone of regional hydroclimatic studies, and merging data from various sources provides a means to enhance accuracy. This study aims apply technique referred as Multi-Source Weighted-Ensemble Precipitation (MSWEP) merge gauge-, satellite-, model-based products for Taiwan (MSWEP_TW). To correct known biases in satellite precipitation, long short-term memory (LSTM) emerging convolutional (ConvLSTM) networks are employed. Afterward, how correction influences performance merged assessed. The reveals that LSTM with spatial coherence scheme can show similar effectiveness ConvLSTM increasing correlations gauge-based by ∼10%. MSWEP_TW proven outperform original product (i.e., MSWEP version 2.8), higher weights satellite- over gauge-scarce regions verified. Further, this confirms provide more accurate than gauge-only interpolation, promoting advantage ungauged areas. Lastly, enhancement made directly contributes development satellite-only low latency, suggesting their usefulness near real-time applications.

Язык: Английский

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Bias correction of the hourly satellite precipitation product using machine learning methods enhanced with high-resolution WRF meteorological simulations

Atmospheric Research, Год журнала: 2024, Номер 310, С. 107637 - 107637

Опубликована: Авг. 13, 2024

Язык: Английский

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Deep neural network-based discharge prediction for upstream hydrological stations: a comparative study

Earth Science Informatics, Год журнала: 2023, Номер 16(4), С. 3113 - 3124

Опубликована: Авг. 21, 2023

Язык: Английский

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Unveiling environmental drivers of soil erosion in South Korea through SHAP-informed machine learning

Land Use Policy, Год журнала: 2025, Номер 155, С. 107592 - 107592

Опубликована: Май 9, 2025

Язык: Английский

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Investigating the Performance of CMIP6 Seasonal Precipitation Predictions and a Grid Based Model Heterogeneity Oriented Deep Learning Bias Correction Framework

Journal of Geophysical Research Atmospheres, Год журнала: 2023, Номер 128(23)

Опубликована: Дек. 7, 2023

Abstract Climate change is expected to alter the magnitude and spatiotemporal patterns of hydro‐climate variables such as precipitation, which has significant impacts on ecosystem, human societies water security. Global Models are major tools simulate historical well future precipitation. However, due imperfect model structures, parameters boundary conditions, direct outputs subject large uncertainty, needs serious evaluation bias correction before usage. In this study, seasonal precipitation predictions from 30 Coupled Model Inter‐comparison Project Phase 6 (CMIP6) models Research Unit observations used evaluate climatology in global continents during 1901–2014. A grid based heterogeneity oriented Convolutional Neural Network (CNN) proposed correct ensemble mean ratio. Besides, regression Linear Scaling (LS), distribution Quantile Mapping (QM) spatial correlation CNN approaches employed for comparison. Results performance indicate that generally prediction more reliable JJA than DJF scale. Most tend have larger ratio extreme addition, current CMIP6 still certain issues accurate simulation mountainous regions affected by complex climate systems. Moreover, better LS, QM, CNN, could consider relative capture features similar actual dynamics.

Язык: Английский

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A Novel Framework for Correcting Satellite-Based Precipitation Products for Watersheds with Discontinuous Observed Data, Case Study in Mekong River Basin

Remote Sensing, Год журнала: 2023, Номер 15(3), С. 630 - 630

Опубликована: Янв. 20, 2023

Satellite-based precipitation (SP) data are gaining scientific interest due to their advantage in producing high-resolution products with quasi-global coverage. However, since the major reliance of is on distinctive geographical features each location, they remain at a considerable distance from station-based data. This paper examines effectiveness convolutional autoencoder (CAE) architecture pixel-by-pixel bias correction SP for Mekong River Basin (MRB). Two satellite-based (TRMM and PERSIANN-CDR) gauge-based product (APHRODITE) gridded rainfall mined this experiment. According estimated statistical criteria, CAE model was effective reducing gap between benchmark both terms spatial temporal correlations. The two corrected (CAE_TRMM CAE_CDR) performed competitively, TRMM appearing have slight over CDR, however, difference minor. study’s findings proved deep learning-based models (here CAE) products. We believe that technique will be feasible alternative delivering an up-to-current reliable dataset MRB studies, given sole available area has been out date long time.

Язык: Английский

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