Artificial Intelligence in Geosciences最新文献

{"title":"Spatial mapping and modelling of soil organic carbon using random forest and remote sensing variables in part of Kaduna, Northern Nigeria","authors":"Mays Taha Yaqub ,&nbsp;Fatihu Kabir Sadiq ,&nbsp;Mohammed Abdal-Mnam Hassan ,&nbsp;AbdulKarem Ahmed Meklef Alalwany","doi":"10.1016/j.aiig.2026.100198","DOIUrl":"10.1016/j.aiig.2026.100198","url":null,"abstract":"<div><div>Reliable and up-to-date digital soil data is crucial for achieving Sustainable Development Goal 13 (Climate Action) by enabling improved monitoring of soil carbon and land degradation, thereby supporting climate-smart agriculture and ensuring stable crop yields in sub-Saharan Africa. This study focuses on the spatial mapping of soil organic carbon (SOC) comparing predictive models that integrate Landsat 8 variables and DEM derivatives within a Random Forest framework. Three models were evaluated: Model A, which incorporates only Landsat 8 derivatives; Model B, based solely on DEM variables; and Model C, which integrates both Landsat 8 and DEM datasets. The results indicate that Model A achieved an RMSE of 0.15 (%) and an R² of 0.67, while Model B achieved an RMSE of 0.19 (%) and an R² of 0.54. Model C (the combined model) achieved the highest explanatory power with an R² of 0.69. The findings highlight the significant influence of DEM-derived variables, such as profile and plan curvature, on SOC distribution, emphasizing the role of terrain attributes in SOC mapping. This study demonstrates the potential of RF modeling for SOC prediction, reinforcing the importance of integrating spectral and topographic variables to enhance accuracy. To achieve sustainable farming and resilient crop production in sub-Saharan Africa, accurate digital soil mapping is essential. These datasets empower climate action by tracking soil health and carbon sequestration, providing the necessary evidence base for effective land management strategies.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100198"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical machine learning for the automatic classification of surface deformation from SAR observations","authors":"Jhonatan Rivera-Rivera ,&nbsp;Héctor Aguilera ,&nbsp;Marta Béjar-Pizarro ,&nbsp;Carolina Guardiola-Albert ,&nbsp;Pablo Ezquerro ,&nbsp;Anna Barra","doi":"10.1016/j.aiig.2025.100171","DOIUrl":"10.1016/j.aiig.2025.100171","url":null,"abstract":"<div><div>Ground deformation processes, such as landslides and subsidence, cause significant social, economic, and environmental impacts. This study aims to automatically classify ground deformation processes in Spain using a machine learning approach applied to InSAR-based datasets. The database integrates InSAR measurement points (MPs) from 20 case studies in Spain, obtained from various institutional sources, and 32 geoenvironmental variables related to ground deformation, morphometry, geology, climate, and land use. The proposed classification strategy follows a hierarchical structure with two levels: first, distinguishing between landslides and subsidence; then, identifying the specific type within each main class (mining landslide, environmental landslide, constructive subsidence, mining subsidence, and piezometric subsidence). Several machine learning algorithms (Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, Extra Trees, Gradient Boosting Machine, XGBoost, LightGBM, and CatBoost) and data configurations were tested, combining different spatial resolutions and class balancing techniques. The best performance (Cohen's Kappa = 0.78) was achieved with the hierarchical approach using the 200 m grid dataset, applying XGBoost for the parental and landslide models, and CatBoost for the subsidence model. Using this approach, 70 % de test sites achieved over 88 % correctly classified cells, 20 % had between 50 % and 83 %, and only one test case was entirely misclassified. The analysis of the most relevant variables indicates that annual mean precipitation, mining activity, buildings, landslide susceptibility, and slope are key factors. These results demonstrate the potential of the hierarchical approach to improve classification and lay the groundwork for future application at national and European scales, incorporating new training cases, process types, and continental data sources. In conclusion, this study presents, for the first time, a hierarchical machine learning model capable of accurately classifying ground deformation processes in Spain, with the aim of supporting territorial management and geohazard mitigation.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100171"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid unsupervised-supervised deep learning framework for sandstone thickness prediction from seismic data","authors":"Yixue Xiong ,&nbsp;Bing Tan ,&nbsp;Qiannan Wang ,&nbsp;Bing Li ,&nbsp;Zegang Wang ,&nbsp;Xiaoyi Zhou ,&nbsp;Xingyu Liu ,&nbsp;Wenqiang Ma ,&nbsp;Lan Huang ,&nbsp;Zhiguo Wang","doi":"10.1016/j.aiig.2026.100199","DOIUrl":"10.1016/j.aiig.2026.100199","url":null,"abstract":"<div><div>Accurate sandstone thickness prediction from seismic data is vital for reservoir characterization and well placement optimization. However, conventional deep learning methods are often hindered by inefficient sequential processing or excessive computational costs when handling long seismic traces. To overcome these limitations, we propose a two-stage deep learning framework. First, an unsupervised feature extraction network derives high-dimensional latent representations directly from seismic data. Second, a novel reservoir sequence prediction network—utilizing efficient ProbSparse self-attention and self-attention distilling—maps these features to sandstone thickness, even with limited well-log training data. When applied to a field dataset with limited borehole control, our method resolved sandstone bodies thickness about 15∼20 m and achieved a Mean Absolute Percentage Error of just 3.7% at blind validation wells. This hybrid approach offers a robust, computationally efficient solution for high-precision reservoir prediction in data-constrained environments.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100199"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Fossil Frontier: An answer to the 3-billion fossil question","authors":"Iver Martinsen ,&nbsp;Benjamin Ricaud ,&nbsp;David Wade ,&nbsp;Odd Kolbjørnsen ,&nbsp;Fred Godtliebsen","doi":"10.1016/j.aiig.2025.100184","DOIUrl":"10.1016/j.aiig.2025.100184","url":null,"abstract":"<div><div>Microfossil analysis is important in subsurface mapping, for example to match strata between wells. This analysis is currently conducted by specialist geoscientists who manually investigate large numbers of physical samples with the aim of identifying informative microfossil species and genera. The current digitalization of large volumes of microfossil samples that is being conducted by the Norwegian Offshore Directorate, paired with AI development, opens up new opportunities for automating parts of the analysis to help the geologist in the analysis. Unsupervised representation learning is a research area in Artificial Intelligence (AI) that lies at the core of this challenge, as this way of learning can create useful image representations by utilizing large volumes of data without requiring labels. Previous work has presented good results for the classification of a limited number of classes, but there are still challenges related to classification in realistic settings where additional unknown species are present. In this paper, we connect unsupervised representation learning and uncertainty estimation and create a comprehensive tool to automate microfossil analysis. We present our methodology and results in three parts. In the first part, we train several AI models from scratch using state-of-the-art self-supervised learning methods, obtaining excellent results compared against state-of-the-art foundation models for image classification and content-based image retrieval. In the second part, we develop a method based on conformal prediction which enables our classifier to handle a large pool of images containing both in-distribution and out-of-distribution data, while at the same time allowing us to create error estimates to control the uncertainty of the prediction sets. In the third part, we use our method to create distribution charts of fossils for a range of genera in multiple wells.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100184"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepSeg-based noise reduction algorithm trained on a hybrid synthetic dataset for signals from acoustic logging-while-drilling","authors":"Xin Fu ,&nbsp;Junyi Song ,&nbsp;Yang Gou","doi":"10.1016/j.aiig.2026.100194","DOIUrl":"10.1016/j.aiig.2026.100194","url":null,"abstract":"<div><div>Acoustic logging-while-drilling (ALWD) enables real-time acoustic measurements during drilling operations. However, challenging downhole conditions introduce considerable noise into ALWD signals. This study applied a numerical method to simulate clean ALWD array waveforms. A synthetic noisy dataset was subsequently generated by superimposing high-quality noise data acquired during laboratory measurements onto the simulated clean dataset. Time–frequency spectral representations of noisy signals were obtained via short-time discrete cosine transformation and were divided into past, present, and future intervals. These were used as input for a U-Net-based neural network—DeepSeg—that was employed for frequency-domain denoising. The trained model outputted denoised frequency segments for the intermediate current time interval. This sliding-window strategy applied to frequency slices substantially reduced the required dataset size. The network effectively removed complex downhole noise, even with limited training data, and demonstrated a strong generalization capability on field data. The network also significantly enhanced the quality of acoustic array signals and improved the accuracy of slowness-time-coherence processing in a logging-while-drilling application example. Requiring as little as one tenth of the amount of data used in methods from previous studies, the proposed method is particularly advantageous for ALWD applications, where data acquisition is challenging.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100194"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of the soil–water retention curve of compacted clays using PSO–GA XGBoost","authors":"Reza Taherdangkoo ,&nbsp;Thomas Nagel ,&nbsp;Vladimir Tyurin ,&nbsp;Chaofan Chen ,&nbsp;Faramarz Doulati Ardejani ,&nbsp;Christoph Butscher","doi":"10.1016/j.aiig.2025.100173","DOIUrl":"10.1016/j.aiig.2025.100173","url":null,"abstract":"<div><div>Soil–water retention (SWR) is fundamental for understanding the hydro-mechanical behavior of unsaturated clay soils. The soil–water retention curve is typically obtained through extensive and costly laboratory testing. To offer a more efficient alternative, an extreme gradient boosting (XGBoost) model, optimized using a hybrid particle swarm optimization and genetic algorithm (PSO–GA), was developed. This hybrid model estimates the SWR across a broad suction range, accounting for both drying and wetting paths, along with key soil parameters. The performance of the model was evaluated through various statistical analyses and by comparing the predicted gravimetric water content with experimental data. A backward feature elimination method was employed to assess the impact of various input parameters on model accuracy and to offer a simplified model for scenarios with limited data availability. Additionally, Monte Carlo simulations were conducted to quantify the inherent uncertainties associated with the dataset, XGBoost hyperparameters, and model performance. The hybrid PSO–GA XGBoost model effectively estimates the water retention of clayey soils during both drying and wetting cycles, proving to be an alternative to traditional soil mechanics correlations.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100173"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote sensing estimation of rice chlorophyll content based on UAV image feature selection and PSO-optimized ensemble learning","authors":"Tuo Wang ,&nbsp;Guijun Yang ,&nbsp;Xingang Xu ,&nbsp;Jiaqi Sun ,&nbsp;Yang Meng ,&nbsp;Xiaodong Yang ,&nbsp;Haikuan Feng ,&nbsp;Hanyu Xue ,&nbsp;Xinlei Xu ,&nbsp;Yuekun Song","doi":"10.1016/j.aiig.2026.100190","DOIUrl":"10.1016/j.aiig.2026.100190","url":null,"abstract":"<div><div>Chlorophyll content is one crucial indicator of evaluating crop growth and physiological status. Rapid, accurate, and large-scale monitoring of chlorophyll content is vital for the precise diagnosis of crop nutritional status and developmental dynamics. In this study, rice chlorophyll content was estimated using remote sensing data derived from field samples collected under varying nitrogen application levels. The proposed monitoring framework integrated spectral feature selection methods, data augmentation techniques with ensemble learning models, using multi-temporal multispectral imagery acquired by unmanned aerial vehicles (UAVs). Firstly, one two-stage Otsu adaptive thresholding method was utilized to efficiently extract rice pixels from UAV images captured at different growth stages, thereby eliminating non-rice noise in paddy field imagery. Subsequently, spectral vegetation indices, and texture features of rice UAV images were extracted. Three feature selection methods—GRA, mRMR, and SHAP—were employed to identify chlorophyll-sensitive features. Finally, based on Gaussian Mixture Model (GMM)-based data augmentation strategy and the selected sensitive features, one two-layer ensemble learning framework optimized by using Particle Swarm Optimization (PSO) algorithm was proposed to achieve accurate and efficient estimation of rice chlorophyll content across different growth stages. The results demonstrate that the SHAP-based feature selection method consistently achieved the best prediction performance across all growth stages, highlighting its superiority in retaining key feature information and enhancing model stability. Compared with individual machine learning models, the SHAP–PSO-based ensemble learning model exhibited higher accuracy and robustness, with <em>R</em>^<em>2</em> of 0.641, 0.555, and 0.527, and corresponding rRMSE of 0.055, 0.099, and 0.104 across the different stages. These findings indicate that the proposed ensemble learning framework, which integrates SHAP-based feature selection, GMM-based data augmentation, and PSO optimization, possesses strong generalization capability and stability for the remote sensing estimation of rice chlorophyll content. Moreover, the methodology proposed in this study can serve as a valuable reference for remote sensing-based monitoring of physicochemical parameters in other crops.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100190"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of YOLOv11 deep learning model for classification and counting ice-rafted debris (IRD) in core sediments in the Arctic Ocean","authors":"Sunhwa Bang ,&nbsp;Jae-Yoon Keum ,&nbsp;Yoon Ji ,&nbsp;Yang Jae Kang ,&nbsp;Byung-Dal So ,&nbsp;Jong Kuk Hong ,&nbsp;Hyo-Im Kim","doi":"10.1016/j.aiig.2026.100191","DOIUrl":"10.1016/j.aiig.2026.100191","url":null,"abstract":"<div><div>The classification and quantification of ice-rafted debris (IRD) in marine sediments are key to reconstructing glacial-interglacial dynamics and sediment provenance. However, traditional IRD analysis, based on manual grain identification under binocular microscopes, is time-consuming and dependent on expertise, which acted as a barrier to entry for IRD research. Here, we present a deep learning-based framework for the automated detection and lithological classification of IRD from high-resolution microscopic images with grains from natural Arctic sediments. Using the YOLOv11 algorithm, we designed a two-stage model: an instance segmentation model (Model 1) that detects individual IRD from multi-grain images, and a classification model (Model 2) that categorizes each grain into one of four lithological types (i.e., quartz, detrital carbonate, clastic, and crystalline). The dataset comprises 110 images containing 9642 grains from the Chukchi Sea sediment core ARA14C-ST12. Model 1 achieved the detection performance with precision = 0.95, recall = 0.97, <em>m</em>AP50 = 0.98, <em>m</em>AP50-95 = 0.85, and F1 score = 0.96, demonstrating high model performance to detect the complex morphology of grain. The evaluation metric of Model 2, used to identify lithological classes, showed average Top-1 accuracy of 0.87 and 0.75 on the validation and test sets, respectively. The classification model showed reliable recognition for quartz and detrital carbonate, with moderate confusion between clastic and crystalline grains. These results demonstrate that the proposed YOLOv11-based approach enables rapid, reproducible, and objective lithological classification of IRD grains, providing an efficient alternative to conventional manual counting.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100191"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of processing speed of NRS-ANN hybrid and ANN models for oil production rate estimation of reservoir under waterflooding","authors":"Paul Theophily Nsulangi ,&nbsp;Werneld Egno Ngongi ,&nbsp;John Mbogo Kafuku ,&nbsp;Guan Zhen Liang","doi":"10.1016/j.aiig.2025.100139","DOIUrl":"10.1016/j.aiig.2025.100139","url":null,"abstract":"<div><div>This study compared the predictive performance and processing speed of an artificial neural network (ANN) and a hybrid of a numerical reservoir simulation (NRS) and artificial neural network (NRS-ANN) models in estimating the oil production rate of the ZH86 reservoir block under waterflood recovery. The historical input variables: reservoir pressure, reservoir pore volume containing hydrocarbons, reservoir pore volume containing water and reservoir water injection rate used as inputs for ANN models. To create the NRS-ANN hybrid models, 314 data sets extracted from the NRS model, which included reservoir pressure, reservoir pore volume containing hydrocarbons, reservoir pore volume containing water and reservoir water injection rate were used. The output of the models was the historical oil production rate (HOPR in m³ per day) recorded from the ZH86 reservoir block. Models were developed using MATLAB R2021a and trained with 25 models in three replicate conditions (2, 4 and 6), each at 1000 epochs. A comparative analysis indicated that, for all 25 models, the ANN outperformed the NRS-ANN in terms of processing speed and prediction performance. ANN models achieved an average of R² and MAE of 0.8433 and 8.0964 m³/day values, respectively, while NRS-ANN hybrid models achieved an average of R² and MAE of 0.7828 and 8.2484 m³/day values, respectively. In addition, ANN models achieved a processing speed of 49 epochs/sec, 32 epochs/sec, and 24 epochs/sec after 2, 4, and 6 replicates, respectively. Whereas the NRS-ANN hybrid models achieved lower average processing speeds of 45 epochs/sec, 23 epochs/sec and 20 epochs/sec. In addition, the ANN optimal model outperforms the NRS-ANN model in terms of both processing speed and accuracy. The ANN optimal model achieved a speed of 336.44 epochs/sec, compared to the NRS-ANN hybrid optimal model, which achieved a speed of 52.16 epochs/sec. The ANN optimal model achieved lower RMSE and MAE values of 7.9291 m³/day and 5.3855 m³/day in the validation dataset compared with the hybrid ANS optimal model, which achieved 13.6821 m³/day and 9.2047 m³/day, respectively. The study also showed that the ANN optimal model consistently achieved higher R² values: 0.9472, 0.9284 and 0.9316 in the training, test and validation data sets. Whereas the NRS-ANN hybrid optimal yielded lower R² values of 0.8030, 0.8622 and 0.7776 for the training, testing and validation datasets. The study showed that ANN models are a more effective and reliable tool, as they balance both processing speed and accuracy in estimating the oil production rate of the ZH86 reservoir block under the waterflooding recovery method.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of groundwater level in Indonesian tropical peatland forest plantations using machine learning","authors":"Kazuo Yonekura ,&nbsp;Sota Miyazaki ,&nbsp;Masaatsu Aichi ,&nbsp;Takafumi Nishizu ,&nbsp;Masao Hasegawa ,&nbsp;Katsuyuki Suzuki","doi":"10.1016/j.aiig.2025.100148","DOIUrl":"10.1016/j.aiig.2025.100148","url":null,"abstract":"<div><div>Maintaining high groundwater level (GWL) is important for preventing fires in peatlands. This study proposes GWL prediction using machine learning methods for forest plantations in Indonesian tropical peatlands. Deep neural networks (DNN) have been used for prediction; however, they have not been applied to groundwater prediction in Indonesian peatlands. Tropical peatland is characterized by high permeability and forest plantations are surrounded by several canals. By predicting daily differences in GWL, the GWL can be predicted with high accuracy. DNNs, random forests, support vector regression, and XGBoost were compared, all of which indicated similar errors. The SHAP value revealed that the precipitation falling on the hill rapidly seeps into the soil and flows into the canals, which agrees with the fact that the soil has high permeability. These findings can potentially be used to alleviate and manage future fires in peatlands.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100148"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}