Applied Computing and Geosciences最新文献

{"title":"A machine learning approach for mapping susceptibility to land subsidence caused by ground water extraction","authors":"Diana Orlandi ,&nbsp;Esteban Díaz ,&nbsp;Roberto Tomás ,&nbsp;Federico A. Galatolo ,&nbsp;Mario G.C.A. Cimino ,&nbsp;Carolina Pagli ,&nbsp;Nicola Perilli","doi":"10.1016/j.acags.2024.100207","DOIUrl":"10.1016/j.acags.2024.100207","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100207"},"PeriodicalIF":2.6,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning","authors":"Rajib Maity,&nbsp;Aman Srivastava,&nbsp;Subharthi Sarkar,&nbsp;Mohd Imran Khan","doi":"10.1016/j.acags.2024.100206","DOIUrl":"10.1016/j.acags.2024.100206","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100206"},"PeriodicalIF":2.6,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating land gravity anomalies from satellite gravity observations using PIX2PIX GAN image translation","authors":"Bisrat Teshome Weldemikael ,&nbsp;Girma Woldetinsae ,&nbsp;Girma Neshir","doi":"10.1016/j.acags.2024.100205","DOIUrl":"10.1016/j.acags.2024.100205","url":null,"abstract":"<div><div>Generative Adversarial Networks (GANs), specifically the Pix2Pix GAN, are used to effectively map gravity anomalies from satellite to ground, and adapt the Pix2Pix GAN model for large-scale data transformation. The impact of varying patch sizes on model performance is investigated using key metrics to ensure improved accuracy in gravity anomaly mapping. The model used 2728 satellite, and 2728 ground Bouguer gravity anomaly images from northern and northeast part of Ethiopia. 5456 images were used for training and 552 for testing. The findings indicate that Intermediate patch sizes, particularly 70 x 70 pixels, significantly enhanced model accuracy by capturing global features and contextual information. Additionally, models incorporating L2 loss with LcGAN demonstrated superior performance across qualitative metrics compared to those with L1 loss. The study will contribute to improve geophysical exploration by providing an alternative method that generates more accurate gravity maps, thereby enhancing the precision of geological models and related applications.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100205"},"PeriodicalIF":2.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstruction of reservoir rock using attention-based convolutional recurrent neural network","authors":"Indrajeet Kumar,&nbsp;Anugrah Singh","doi":"10.1016/j.acags.2024.100202","DOIUrl":"10.1016/j.acags.2024.100202","url":null,"abstract":"<div><div>The digital reconstruction of reservoir rock or porous media is important as it enables us to visualize and explore their real internal structures. The reservoir rocks (such as sandstone and carbonate) contain both spatial and temporal characteristics, which pose a big challenge in their characterization through routine core analysis or x-ray microcomputer tomography. While x-ray micro-computed tomography gives us three-dimensional images of the porous media, it is often impossible to quantify the variability of the pore, grains, structure, and orientation experimentally. Recently, machine learning has successfully demonstrated the reconstruction ability of reservoir rock images or any porous media. These reservoir rock images are crucial for the digital characterization of the reservoir. We propose a novel algorithm consisting of the convolutional neural network, an attention mechanism, and a recurrent neural network for the reconstruction of reservoir rock or porous media images. The attention-based convolutional recurrent neural network (ACRNN) can reconstruct a representative sample of reservoir rocks. The reconstructed image quality was checked by comparing them with the original Parker sandstone, Leopard sandstone, carbonate shale, Berea sandstone, and sandy medium images. We evaluated the reconstruction by measuring pore and throat properties, two-point probability function, and structural similarity index. Results show that ACRNN can reconstruct reservoir rock or porous media of different scales with approximately the same geometrical, statistical, and topological parameters of the reservoir rock images. This deep learning method is computationally efficient, fast, and reliable for synthetic image realizations. The model was trained and validated on real images, and the reconstructed images showed excellent concordance with the real images having almost the same pore and grain structures. The deep learning-based digital rock reconstruction of reservoir rock or porous media images can aid in rapid image generation to better understand reservoir rock or subsurface formation.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100202"},"PeriodicalIF":2.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping landforms of a hilly landscape using machine learning and high-resolution LiDAR topographic data","authors":"Netra R. Regmi ,&nbsp;Nina D.S. Webb ,&nbsp;Jacob I. Walter ,&nbsp;Joonghyeok Heo ,&nbsp;Nicholas W. Hayman","doi":"10.1016/j.acags.2024.100203","DOIUrl":"10.1016/j.acags.2024.100203","url":null,"abstract":"<div><div>Landform maps are important tools in assessment of soil- and eco-hydrogeomorphic processes and hazards, hydrological modeling, and natural resources and land management. Traditional techniques of mapping landforms based on field surveys or from aerial photographs can be time and labor intensive, highlighting the importance of remote sensing products based automatic or semi-automatic approaches. In addition, the time-intensive manual labeling can also be subjective rather than an objective identification of the landform. Here we implemented such an objective approach applying a random forest machine learning algorithm to a set of observed landform data and 1m horizontal resolution bare-earth digital elevation model (DEM) developed from airborne light detection and ranging (LiDAR) data to rapidly map various landforms of a hilly landscape. The landform classification includes upland plateaus, ridges, convex slopes, planar slopes, concave slopes, stream channels, and valley bottoms, across a 400 km² hilly landscape of the Ozark Mountains in northeastern Oklahoma. We used 4200 landform observations (600 per landform) and eight topographic indices derived from 2 m, 5 m and 10 m resolution LiDAR DEM in random forest algorithm to develop 2 m, 5 m and 10 m resolution landform models. We test the effectiveness of DEM resolution in mapping landforms via comparison of observed landforms with modeled landforms. Results showed that the approach mapped ∼84% of observed landforms when covariates were at 2 m resolution to ∼89% when they were at 10 m resolution. However, predicted maps showed that the 2 m resolution covariates performed better at capturing accurate landform boundaries and details of small-sized landforms such as stream channels and ridges. The approach presented here significantly reduces the time required for mapping landforms compared to traditional techniques using aerial imagery and field observations and allows incorporation of a wide variety of covariates. The landform map developed using this approach has several potential applications. It could be utilized in hydrological modeling, natural resource management, and characterizing soil-geomorphic processes and hazards in a hilly landscape.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100203"},"PeriodicalIF":2.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India","authors":"Srikanth Bhoopathi,&nbsp;Nitish Kumar,&nbsp;Somesh,&nbsp;Manali Pal","doi":"10.1016/j.acags.2024.100204","DOIUrl":"10.1016/j.acags.2024.100204","url":null,"abstract":"<div><div>This research aims to forecast maximum temperatures and the frequency of heatwave days across four different temperature zones (Zone 1, 2, 3 and 4) in India. These four zones are categorized based on the 30-year average maximum temperatures (T_30AMT) during the summer months of April, May, and June (AMJ). Two Machine Learning (ML) algorithms eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) are employed to achieve this goal. The study utilizes nine key atmospheric variables namely air temperature, geopotential height, relative humidity, U-wind, V-wind, soil moisture, solar radiation, sea surface temperature, and mean sea level pressure at a daily scale spanning from 1991 to 2020 for the months of March, April, May, and June as predictors. The India Meteorological Department daily maximum temperature data spanning from 1991 to 2020 for the months of AMJ serves as the predictands. ML models are developed using spatially averaged atmospheric variables and daily maximum temperature across the grids falling within each temperature zone. Results indicate that for a 7-day lead time, SVR outperforms XGBoost in Zone-1 (T_30AMT &gt; 38 °C) and Zone-2 (T_30AMT: 35.01 °C–38 °C) by more accurately capturing peak temperatures during training and testing. Conversely, for a 15-day lead time in Zone-1, XGBoost better predicts temperature peaks in both phases. In Zone-3 (T_30AMT: 30 °C–35 °C) and Zone-4 (T_30AMT &lt; 30 °C) for both lead times, the performance of both models decline, indicating models and input variables are more effective in predicting higher temperatures typical of Zone-1 and 2 but less so in Zone-3 and 4. In a nutshell, the study attempts to highlight the capability of advanced ML techniques combined with spatial climate data to enhance the prediction of extreme heatwave events. These insights can aid in heatwave preparedness, climate management, and adaptation strategies for different Indian temperature zones.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100204"},"PeriodicalIF":2.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current progress in subseasonal-to-decadal prediction based on machine learning","authors":"Zixiong Shen ,&nbsp;Qiming Sun ,&nbsp;Xinyu Lu ,&nbsp;Fenghua Ling ,&nbsp;Yue Li ,&nbsp;Jiye Wu ,&nbsp;Jing-Jia Luo ,&nbsp;Chaoxia Yuan","doi":"10.1016/j.acags.2024.100201","DOIUrl":"10.1016/j.acags.2024.100201","url":null,"abstract":"<div><div>The application of machine learning (ML) techniques to climate science has received significant attention, particularly in the field of climate predictions, ranging from sub-seasonal to decadal time scales. This paper reviews recent progress of ML techniques employed in climate phenomena prediction and the enhancement of dynamic forecast models, which provide valuable insights into the great potentials of ML techniques to improve climate prediction capabilities with reduced computational time and resource consumption. This paper also discusses several major challenges in the application of ML to climate prediction, including the scarcity of datasets, physical inconsistency, and lack of model transparency and interpretability. Additionally, this paper sheds light on how climate change impacts ML model training and prediction, and explores three key areas with potential breakthroughs: large-scale climate models, knowledge discovery driven by ML, and hybrid dynamical-statistical models, underscoring the important role of the integration of “ML and dynamical models” in building a bridge between the artificial intelligence and climate science.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100201"},"PeriodicalIF":2.6,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel application of unsupervised machine learning for characterization of subsurface seismicity, tectonic dynamics and stress distribution","authors":"Mohammad Salam,&nbsp;Muhammad Tahir Iqbal,&nbsp;Raja Adnan Habib,&nbsp;Amna Tahir,&nbsp;Aamir Sultan,&nbsp;Talat Iqbal","doi":"10.1016/j.acags.2024.100200","DOIUrl":"10.1016/j.acags.2024.100200","url":null,"abstract":"<div><div>Our study pioneers an innovative use of unsupervised machine learning, a powerful tool for navigating unclassified data, to unravel the complexities of subsurface seismic activities and extract meaningful patterns. Our central objective is to comprehensively characterize seismicity within an active region by identifying distinct seismic clusters in spatial distribution, thereby gaining a deeper understanding of subsurface stress distribution and tectonic dynamics. Employing a diverse range of clustering algorithms, with particular emphasis on Fuzzy C-Means (FCM), our research meticulously dissects the intricate physical processes that govern a complex tectonic zone. This technique effectively delineates distinct tectonic zones, aligning seamlessly with established seismological knowledge and underscoring the transformative potential of Artificial Intelligence (AI) in analyzing regional subsurface phenomena, even under conditions of data scarcity. Moreover, associating earthquakes with specific seismogenic structures significantly enhances seismic hazard analyses, potentially paving the way for autonomous insights that inform engineering hazard assessments.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100200"},"PeriodicalIF":2.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative study on machine learning approaches for rock mass classification using drilling data","authors":"Tom F. Hansen ,&nbsp;Georg H. Erharter ,&nbsp;Zhongqiang Liu ,&nbsp;Jim Torresen","doi":"10.1016/j.acags.2024.100199","DOIUrl":"10.1016/j.acags.2024.100199","url":null,"abstract":"<div><div>Current rock engineering design in drill and blast tunnelling primarily relies on engineers' observational assessments. Measure While Drilling (MWD) data, a high-resolution sensor dataset collected during tunnel excavation, is underutilised, mainly serving for geological visualisation. This study aims to automate the translation of MWD data into actionable metrics for rock engineering. It seeks to link data to specific engineering actions, thus providing critical decision support for geological challenges ahead of the tunnel face. Leveraging a large and geologically diverse dataset of ∼500,000 drillholes from 15 tunnels, the research introduces models for accurate rock mass quality classification in a real-world tunnelling context. Both conventional machine learning and image-based deep learning are explored to classify MWD data into Q-classes and Q-values—examples of metrics describing the stability of the rock mass—using both tabular- and image data. The results indicate that the K-nearest neighbours algorithm in an ensemble with tree-based models using tabular data effectively classifies rock mass quality. It achieves a cross-validated balanced accuracy of 0.86 in classifying rock mass into the Q-classes A, B, C, D, E1, E2, and 0.95 for a binary classification with E versus the rest. Classification using a CNN with MWD-images for each blasting round resulted in a balanced accuracy of 0.82 for binary classification. Regressing the Q-value from tabular MWD-data achieved cross-validated R² and MSE scores of 0.80 and 0.18 for a similar ensemble model as in classification. High performance in regression and classification boosts confidence in automated rock mass assessment. Applying advanced modelling on a unique dataset demonstrates MWD data's value in improving rock mass classification accuracy and advancing data-driven rock engineering design, reducing manual intervention.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100199"},"PeriodicalIF":2.6,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A generative deep neural network as an alternative to co-kriging","authors":"Herbert Rakotonirina ,&nbsp;Paul Honeine ,&nbsp;Olivier Atteia ,&nbsp;Antonin Van Exem","doi":"10.1016/j.acags.2024.100198","DOIUrl":"10.1016/j.acags.2024.100198","url":null,"abstract":"<div><div>In geosciences, kriging is leading spatial interpolation, and co-kriging is the most commonly used method for accomplishing spatial interpolation of a target variable by incorporating information from a secondary variable. Co-kriging relies on the assumption of spatial stationarity, which may not hold true in all geospatial contexts, leading to potential inaccuracies in interpolation. The effectiveness of co-kriging can be compromised in areas with sparse data, impacting the reliability of interpolated results. Moreover, it can be resource-intensive when used for interpolation with a substantial volume of data, especially in the case of 3D interpolation. In this paper, we introduce a new method for spatial interpolation that considers two variables using a generative deep neural network. This approach utilizes a convolutional neural network with an encoder–decoder architecture, featuring a single encoder and two decoders to handle the two variables. Additionally, we introduce a loss function that facilitates the control over the relationships between the two variables. Traditional Deep Learning methods require prior training and labeled data, whereas the proposed approach eliminates this requirement and simplifies the interpolation process. In order to assess the performance of our method, we use two real-world datasets. The first one is a 2D dataset of total soil organic carbon combined with the Normalized Difference Vegetation Index. The second one is a 3D dataset that combines concentrations of Hydrocarbon and Fluoride obtained from hyperspectral analysis of soil cores with very limited number of boreholes. The experimental results demonstrate that the proposed method outperforms ordinary kriging and co-kriging, showing a significant improvement when both variables are used. We also demonstrate how the inclusion of the auxiliary variable serves as a means to mitigate the overfitting of the model.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100198"},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}