An Interpretable Implicit-Based Approach for Modeling Local Spatial Effects: A Case Study of Global Gross Primary Productivity Estimation by S. Du, H. Huang, K. Shen, Z. Liu, S. Tang

“…In geographic machine learning tasks, conventional statistical learning methods often struggle to capture spatial heterogeneity, leading to unsatisfactory prediction accuracy and unreliable interpretability. …”
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Spatial clustering analysis combined with ensemble modeling identified potential coastal conservation hotspots of White-eyed gulls in the Red Sea by Mohanad Abdelgadir, Monif AlRashidi, Randa Alharbi, Abdulaziz S. Alatawi

“…In this study, we used a spatial clustering analysis combined with an ensemble modeling approach to predict the coastal distribution and identify potential hotspots for the White-eyed gull. …”
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Application of fluid dynamics in modeling the spatial spread of infectious diseases with low mortality rate: A study using MUSCL scheme by Nnaji Daniel Ugochukwu, Kiogora Phineas Roy, Onah Ifeanyi Sunday, Mung’atu Joseph, Aguegboh Nnaemeka Stanley

“…This study presents a comprehensive mathematical framework that applies fluid dynamics to model the spatial spread of infectious diseases with low mortality rates. …”
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Identifying climate and environmental determinants of spatial disparities in wheat production using a geospatial machine learning model by Kai Ren, Yongze Song, Linchao Li, Francesco Mancini, Zhuoyao Xiao, Xueyuan Zhang, Rui Qu, Qiang Yu

“…Next, the geographically optimal zones-based heterogeneity (GOZH) model, an integration of spatial stratified heterogeneity and decision tree learning models, is used to identify determinants and their interactions on spatial disparities of wheat production. …”
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Monthly Arctic Sea‐Ice Prediction With a Linear Inverse Model by M. Kathleen Brennan, Gregory J. Hakim, Edward Blanchard‐Wrigglesworth

“…Abstract We evaluate Linear Inverse Models (LIMs) trained on last millennium model data to predict Arctic sea‐ice concentration, thickness, and other atmospheric and oceanic variables on monthly timescales. …”
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Hybrid approaches enhance hydrological model usability for local streamflow prediction by Yiheng Du, Ilias G. Pechlivanidis

“…Abstract Hydrological models are essential for predicting water flux dynamics, including extremes, and managing water resources, yet traditional process-based large-scale models often struggle with accuracy and process understanding due to their inability to represent complex, non-linear hydrometeorological processes, limiting their effectiveness in local conditions. …”
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RUL prediction method based on cross-view hybrid network model by Ai Yandi, Fang Dong, Tian Zhiping, Yan Kaiyang

“…To this end, this paper designs a RUL prediction framework based on a cross-view hybrid network model (CVHNet). …”
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Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction by Jingye Zhang, Ke Sun, Xiaoming Han, Ning Mao

“…Magnitude prediction is a key focus in earthquake science research, and using machine learning models to analyze seismic data, identify pre-seismic anomalies, and improve prediction accuracy is of great scientific and practical significance. …”
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Deciphering the Mechanism of Better Predictions of Regional LSTM Models in Ungauged Basins by Qiang Yu, Liguang Jiang, Raphael Schneider, Yi Zheng, Junguo Liu

“…The long short‐term memory (LSTM) model has gained popularity in rainfall‐runoff prediction in recent years and has proven applicable in PUB. …”
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An improved machine-learning model for lightning-ignited wildfire prediction in Texas by Qi Zhang, Cong Gao, Chunming Shi

“…Using this dataset, we developed an eXtreme gradient boosting-based machine learning model that integrates meteorological, soil, vegetative, lightning, topographic, and human activity variables to predict LIW probability. …”
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A Meteorology Based Particulate Matter Prediction Model for Megacity Dhaka by Sadia Afrin, Mohammad Maksimul Islam, Tanvir Ahmed

“…Models also exhibit strong predictive power in forecasting PM levels of two other CAMSs in Dhaka. …”
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A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models by J. C. Vogeler, W. B. Cohen

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External validation of risk prediction models for post-stroke mortality in Berlin by Jessica L Rohmann, Tobias Kurth, Heinrich J Audebert, Marco Piccininni, Lukas Reitzle

“…We aimed to assess the performance of two prediction models for post-stroke mortality in Berlin, Germany.Design We used data from the Berlin-SPecific Acute Treatment in Ischaemic or hAemorrhagic stroke with Long-term follow-up (B-SPATIAL) registry.Setting Multicentre stroke registry in Berlin, Germany.Participants Adult patients admitted within 6 hours after symptom onset and with a 10th revision of the International Classification of Diseases discharge diagnosis of ischaemic stroke, haemorrhagic stroke or transient ischaemic attack at one of 15 hospitals with stroke units between 1 January 2016 and 31 January 2021.Primary outcome measures We evaluated calibration (calibration-in-the-large, intercept, slope and plot) and discrimination performance (c-statistic) of Bray et al’s 30-day mortality and Smith et al’s in-hospital mortality prediction models. …”
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A lightweight hybrid model for accurate ammonia prediction in pig houses by Jacqueline Musabimana, Qiuju Xie, Hong Zhou, Ping Zheng, Honggui Liu, Tiemin Ma, Jiming Liu

“…The model improves accuracy compared to other state-of-the-art and ability for NH3 prediction.…”
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A novel cancer-associated membrane signature predicts prognosis and therapeutic response for lung adenocarcinoma by Biao Tu, Jun Wu, Wei Zhang, Haitao Tang, Tenghui Dai, Bingfeng Xie

“…A distinct LUAD-enriched epithelial cluster (Epi_c0) exhibiting hypoxic and EMT signatures was identified. 35 cancer-specific membrane proteins were defined, several of which, including TSPAN8, BACE2, and COX16, showed strong spatial localization within the tumor regions. LCaMPS, a 9-membrane gene-based prognostic model, stratified patient prognosis and predicted 5- and 10-year survival rates with high accuracy. …”
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Predictive and spatial analysis for estimating the impact of sociodemographic factors on contraceptive use among women living with HIV/AIDS (WLWHA) in Kenya: Implications for polic... by Menkeoma Laura Okoli, Samuel Alao, Somtochukwu Ojukwu, Nnadozie C Emechebe, Asuelimen Ikhuoria, Kevin E Kip

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Tuberculosis disease burden in China: a spatio-temporal clustering and prediction study by Jingzhe Guo, Ce Liu, Fang Liu, Erkai Zhou, Runxue Ma, Ling Zhang, Bin Luo

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BUILDING PREDICTIVE MODELS TO ASSESS DEGRADATION OF SOIL ORGANIC MATTER OVER TIME USING REMOTE SENSING DATA by Abdulsalam Aljumaily, Ammar Kashmolaa

“…The results of the study showed the possibility of applying predictive models to Satellite data for a particular area and for previous years to give results with high spatial accuracy (R2 = 0.9581). …”
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Improving Discharge Predictions in Ungauged Basins: Harnessing the Power of Disaggregated Data Modeling and Machine Learning by Aggrey Muhebwa, Colin J. Gleason, Dongmei Feng, Jay Taneja

“…Abstract Current machine learning methods for discharge prediction often employ aggregated basin‐wide hydrometeorological data (lumped modeling) for parametric and non‐parametric training. …”
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Modeling inter-city population flows: a Deep Radiation model with multisource geographic features by Jingjing Liu, Lei Xu, Le Ma, Chao Wang, Nengcheng Chen

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