Mapping predicted ecological states at landscape scales using remote‐sensing data and machine learning by N. J. Kleist, C. T. Domschke, A. C. Knight, T. W. Nauman, M. C. Duniway, S. K. Carter

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Exploring and Modeling the Spatial Variability of Soil Erosion in Tana Basin, Northwestern Ethiopia by Derege Tsegaye Meshesha, Anteneh Wubet Belay, Gizachew Ayalew Tiruneh, Enyew Adgo, Tiringo Yilak Alemayehu, M. Chandrakala, José Miguel Reichert

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Climate-Driven Invasion Risks of Japanese Beetle (<i>Popillia japonica</i> Newman) in Europe Predicted Through Species Distribution Modelling by Giuseppe Pulighe, Flavio Lupia, Valentina Manente

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Predicting the Potential Geographic Distribution of <i>Phytophthora cinnamomi</i> in China Using a MaxEnt-Based Ecological Niche Model by Xiaorui Zhang, Haiwen Wang, Tingting Dai

“…Utilizing species occurrence records and 35 environmental variables (|R| < 0.8), we employed the MaxEnt model and ArcGIS spatial analysis to systematically predict the potential geographical distribution of <i>P. cinnamomi</i> under current (1970–2000) and future (2030S, 2050S, 2070S, 2090S) climate scenarios across three Shared Socioeconomic Pathways (SSPs). …”
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Predicting Multi-Scenario Land Use Changes and Soil Erosion in the Huaihe River Basin Based on Coupled PLUS-CSLE Model by GUO Weiling, XU Liuyang, JIA Jiang, GAO Chang, XIA Xiaolin, WANG Bangwen, ZHANG Jingyu, CHEN Lei, CHEN Yingjian

“…[Methods] Based on the PLUS model and the Chinese Soil Loss Equation (CSLE), the land use patterns in the Huaihe River Basin under three scenarios—natural development, ecological protection, and rapid development—for the year 2030 were simulated, and the future soil erosion patterns in the basin under these three scenarios were predicted. …”
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Assessing past, present, and simulated future prediction of land use land cover changes using CA-Markov chain models with Satellite data by Sajjad Hussain, Saeed Ahmad Qaisrani, Aqil Tariq, Muhammad Mubeen, Sajid Ullah

“…Our findings indicated significant LULCC changes over the study period, including urban expansion and agricultural encroachment. CA–Markov model is calibrated and validated using observed data, ensuring accuracy in predicting spatial shifts and magnitudes of land cover alterations. …”
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Prediction of fish (Coilia nasus) catch using spatiotemporal environmental variables and random forest model in a highly turbid macrotidal estuary by Vishal Singh Rawat, Gubash Azhikodan, Katsuhide Yokoyama

“…The results revealed that model M19, which incorporated salinity, SSC, and discharge, achieved the highest predictive accuracy (R2 = 0.89) and closely matched actual field conditions. …”
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Late-Life Alcohol Exposure Does Not Exacerbate Age-Dependent Reductions in Mouse Spatial Memory and Brain TFEB Activity by Hao Chen, Kaitlyn Hinz, Chen Zhang, Yssa Rodriguez, Sha Neisha Williams, Mengwei Niu, Xiaowen Ma, Xiaojuan Chao, Alexandria L. Frazier, Kenneth E. McCarson, Xiaowan Wang, Zheyun Peng, Wanqing Liu, Hong-Min Ni, Jianhua Zhang, Russell H. Swerdlow, Wen-Xing Ding

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A Model for Predicting Short-Term Operating Speeds of Compact Passenger Vehicles on Interchange Ramps Within Urban Expressway Networks by Tingyu Liu, Lanfang Zhang, Genze Li, Yating Wu, Zhenyu Zhao

“…Three models are established: a short-term operating speed model based on a generalized linear model (GLM), a GLM incorporating for spatial correlation (GLMS), and a deep neural network model considering spatial correlation (DNNS). …”
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Predicting sport event outcomes using deep learning by Jianxiong Gao, Yi Cheng, Jianwei Gao

“…In this study, we present a deep learning framework that combines a one-dimensional convolutional neural network (1D CNN) with a Transformer architecture to improve prediction accuracy. The 1D CNN effectively captures local spatial patterns in structured match data, while the Transformer leverages self-attention mechanisms to model long-range dependencies. …”
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Comparative Analysis of Different Interpolation Methods in Modeling Spatial Distribution of Monthly Precipitation by Yılmaz İçağa, Emin Taş

“…It is the main objective of the study that Geographic Information Systems (GIS) techniques are used to compare widely preferred interpolation methods and to model the spatial distribution of monthly precipitation values for prediction in ungauged areas in Akarcay Sinanpasa and Suhut sub-basins, Turkey. …”
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Aggregated Housing Price Predictions with No Information About Structural Attributes—Hedonic Models: Linear Regression and a Machine Learning Approach by Joanna Jaroszewicz, Hubert Horynek

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A statistical framework for modelling migration corridors by Tristan A. Nuñez, Mark A. Hurley, Tabitha A. Graves, Anna C. Ortega, Hall Sawyer, Julien Fattebert, Jerod A. Merkle, Matthew J. Kauffman

“…We developed a novel statistical corridor modelling approach that predicts movement corridors from cost‐distance models fit directly to migration tracking data. …”
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Development of a Weighted Average Ensemble Model for Predicting Officially Assessed Land Prices Using Grid Map Data and SHAP by Surin Im, Kangmin Kim, Geunhee Lee, Hoi-Jeong Lim

“…This study proposes a weighted average ensemble model to predict the Officially Assessed Land Price in Sejong City, South Korea, using 500m <inline-formula> <tex-math notation="LaTeX">$\times 500$ </tex-math></inline-formula>m grid-based spatial data. …”
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Multimodal Deep Learning Models in Precision Agriculture: Cotton Yield Prediction Based on Unmanned Aerial Vehicle Imagery and Meteorological Data by Chunbo Jiang, Xiaoshuai Guo, Yongfu Li, Ning Lai, Lei Peng, Qinglong Geng

“…Furthermore, although the models exhibited comparable prediction accuracy (RMSE: 0.27–0.33 t/ha; R²: 0.61–0.69 across test datasets), their yield prediction spatial distributions varied significantly (e.g., Model 9 predicted a mean yield of 3.88 t/ha with a range of 2.51–4.89 t/ha, versus Model 18 at 3.74 t/ha and 2.33–4.76 t/ha), suggesting the need for further evaluation of spatial stability. …”
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Leveraging Satellite Data for Predicting PM10 Concentration with Machine Learning Models: A Study in the Plains of North Bengal, India by Ayan Das, Manoranjan Sahu

“…To assess model transferability, all five models were utilized to predict PM10 concentrations in the Jalpaiguri region, referencing National Air Quality Monitoring Programme (NAMP) data. …”
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Development of a machine learning-based predictive risk model combining fatty acid metabolism and ferroptosis for immunotherapy response and prognosis in prostate cancer by Zhenwei Wang, Zhihong Dai, Yuren Gao, Zhongxiang Zhao, Zhen Li, Liang Wang, Xiang Gao, Qiuqiu Qiu, Xiaofu Qiu, Zhiyu Liu

“…Abstract Prostate cancer (PCa) remains a leading cause of cancer-related mortality, necessitating robust prognostic models and personalized therapeutic strategies. This study integrated bulk RNA sequencing, single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics to construct a prognostic model based on genes shared between ferroptosis and fatty acid metabolism (FAM). …”
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Measurement-guided therapeutic-dose prediction using multi-level gated modality-fusion model for volumetric-modulated arc radiotherapy by Changfei Gong, Changfei Gong, Changfei Gong, Yuling Huang, Yuling Huang, Yuling Huang, Junming Jian, Junming Jian, Junming Jian, Wenheng Zheng, Wenheng Zheng, Wenheng Zheng, Xiaoping Wang, Xiaoping Wang, Xiaoping Wang, Shenggou Ding, Shenggou Ding, Shenggou Ding, Yun Zhang, Yun Zhang, Yun Zhang

“…Furthermore, the existing models simply take advantage of low-dimensional dosimetry information, meaning that the spatial features about the complex dose distribution may be lost and limiting the predictive power of the models. …”
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AI-based model for Prediction of Power consumption in smart grid-smart way towards smart city using blockchain technology by Emran Aljarrah

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Parallel VMamba and Attention-Based Pneumonia Severity Prediction from CXRs: A Robust Model with Segmented Lung Replacement Augmentation by Bouthaina Slika, Fadi Dornaika, Karim Hammoudi

“…Early diagnosis plays a crucial role in preventing complications, necessitating the development of fast and efficient AI-based models for automated severity assessment. <b>Methods:</b> In this study, we introduce a novel approach that leverages VMamba, a state-of-the-art vision model based on the VisualStateSpace (VSS) framework and 2D-Selective-Scan (SS2D) spatial scanning, to enhance lung severity prediction. …”
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