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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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 Spatial Transformation Based Next Frame Predictor by Saad Mokssit, Daniel Bonilla Licea, Bassma Guermah, Mounir Ghogho

“…In this work, we equip autonomous cars with an object-oriented next-frame predictor that leverages Transformer architecture to extract, for each moving object in the scene, a spatial transformation applied to the object to predict its configuration in the next frame. …”
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Multivariate Segment Expandable Encoder-Decoder Model for Time Series Forecasting by Yanhong Li, David C. Anastasiu

“…Additionally, MSEED incorporates a simple vanilla encoder-decoder model for strengthening rolling predictions. The framework has been tested on four challenging real-world datasets, focusing on two critical forecasting scenarios: long-term predictions (three days ahead) and rolling predictions (every four hours) to simulate real-time decision-making in water resource management. …”
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Increasing neural network robustness improves match to macaque V1 eigenspectrum, spatial frequency preference and predictivity. by Nathan C L Kong, Eshed Margalit, Justin L Gardner, Anthony M Norcia

“…They also suggest that it may be useful to penalize slow-decaying eigenspectra or to bias models to extract features of lower spatial frequencies during task-optimization in order to improve robustness and V1 neural response predictivity.…”
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Spatial-Temporal Distribution Prediction of Electric Vehicle Charging Load Considering Charging Behavior and Real-Time SOC by ZHANG Linjuan, LI Wenfeng, XU Changqing, GUO Jianyu, ZHANG Xiawei, YUAN Jia, WANG Yaoqiang

“…[Methods] The influence of traffic conditions and ambient temperature on EV energy consumption and charging behavior is analyzed，and road traffic network and comprehensive energy consumption models are established. Based on the user's travel chain，the user's travel characteristics are analyzed，the shortest time method is used to plan the driving path，and a spatial-temporal distribution prediction model of the EV charging load is built considering the charging queue time and real-time SOC. …”
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Coupling coordination between agricultural carbon emission efficiency and food security in China: The spatial-temporal evolution and prediction. by Xixian Zheng, Wenmei Liao

“…Additionally, a Combination Forecasting Model predicts CCD trends through 2030. The findings indicate positive trends in both ACEE and FS, albeit with significant regional disparities and a notable lag of FS behind ACEE improvement. …”
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Prediction of the daily spatial variation of stem water potential in cherry orchards using weather and Sentinel-2 data by Francisco Zambrano, Abel Herrera, Mauricio Olguín, Miro Miranda, Jesica Garrido, Andrea Miyasaka Almeida

“…The primary goal of this work is to predict the daily spatial variation of Ψs using machine learning models. …”
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Spatial-temporal Differentiation and Prediction of Extreme Weather Events and Vegetation Cover in Guizhou Province Under Climate Change by LI Xi'nan, HE Linhong, XUE Lianqing

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Spatial predictions of soil moisture across a longitudinal gradient in semiarid ecosystems using UAV and RGB sensors by Alexander Hernandez, Efrain Duarte, Peter Porter, Holden Brecht

“…Texture metrics (‘mean’ and ‘entropy’), and the Excess Green (ExG) index had high predictive power while RGB bands performed poorly. Unlike Idaho and Montana, the spatial predictions for Utah and California showed high reliability (α < 0.01). …”
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A Machine Learning Approach for Predicting Particle Spatial, Velocity, and Temperature Distributions in Cold Spray Additive Manufacturing by Lurui Wang, Mehdi Jadidi, Ali Dolatabadi

“…Stage 1 applies sampling and a K-nearest-neighbor kernel-density-estimation algorithm that predicts the spatial distribution of impacting particles and re-allocates weights in regions of under-estimation. …”
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Predicting Rural Industrial Transformation via Coupling Coordination Between Polder-Based Spatial Features and Industrial Development by Wenzhu Zhou, Dawei Wang, Yiwen Zhang, Hanjing Xu

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DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial–temporal fusion attention by Zheng Chen, Quan Qian

“…This combination enables DGL-STFA to effectively model both dynamic spatial relationships and long-term temporal dependencies, enhancing SOH prediction accuracy. …”
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