The Sloping Mire Soil-Landscape of Southern Ecuador: Influence of Predictor Resolution and Model Tuning on Random Forest Predictions by Mareike Ließ, Martin Hitziger, Bernd Huwe

“…The recursive partitioning algorithm Random Forest was used to predict the spatial water stagnation pattern and the thickness of the organic layer from terrain attributes. …”
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Predicting Urban Vitality at Regional Scales: A Deep Learning Approach to Modelling Population Density and Pedestrian Flows by Feifeng Jiang, Jun Ma

“…However, existing studies often rely on discrete sampling points and single metrics, limiting their ability to capture the continuous spatial distribution of urban vibrancy. This study introduces the UVPN (urban vitality prediction network), a novel deep-learning architecture designed to generate high-resolution predictions of static and dynamic vitality at regional scales. …”
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A combined model for short-term traffic flow prediction based on variational modal decomposition and deep learning by Chuanxiang Ren, Fangfang Fu, Changchang Yin, Li Lu, Lin Cheng

“…Abstract The emergence of Deep Learning provides an opportunity for traffic flow prediction. However, uncertainty and volatility exhibited by nonlinearity and instability of traffic flow pose challenges to Deep Learning models. …”
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Comparing Satellite-Derived and Model-Based Surface Soil Moisture for Spring Barley Yield Prediction in Central Europe by Felix Reuß, Mariette Vreugdenhil, Emanuel Bueechi, Wolfgang Wagner

“…Surface soil moisture (SSM) has proven to be an important variable for the yield prediction of main crops like maize and wheat, but its value for spring barley, the third most cultivated crop in Europe, has not yet been evaluated. …”
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A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm by G. Ramkumar, Satyajeet Sahoo, T. M. Amirthalakshmi, S. Ramesh, R. Thandaiah Prabu, Kasipandian Kasirajan, Antony V. Samrot, A. Ranjith

“…This approach can replace existing knowledge with new information on a continuous basis. The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. …”
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Research on freeze-thaw displacement prediction model of sandy soil based on attention mechanism CNN-BiGRU by Zecheng Wang, Dongwei Li, Zhengbin Dong, Zhiwen Jia, Chaochao Zhang

“…This study develops an attention-based CNN-BiGRU model that synergizes convolutional neural networks for spatial feature extraction, bidirectional gated recurrent units for temporal dependency modeling, and attention mechanisms for critical time-step weighting. …”
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Groundwater level prediction using an improved SVR model integrated with hybrid particle swarm optimization and firefly algorithm by Sandeep Samantaray, Abinash Sahoo, Falguni Baliarsingh

“…The goal was to identify the variables that were most efficient in predicting GWL. The SVR-FFAPSO model performs best in GWL forecasting for Khuntuni station, according to the quantitative analysis with correlation coefficient (R) = 0.9978, Nash–Sutcliffe efficiency (NSE) = 0.9933, mean absolute error (MAE) = 0.00025 (m), root mean squared error (RMSE) = 0.00775 (m) during the training phase. …”
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AI-Based Damage Risk Prediction Model Development Using Urban Heat Transport Pipeline Attribute Information by Sungyeol Lee, Jaemo Kang, Jinyoung Kim, Myeongsik Kong

“…This study analyzed the probability of damage in heat transport pipelines buried in urban areas using pipeline attribute information and damage history data and developed an AI-based predictive model. A dataset was constructed by collecting spatial and attribute data of pipelines and defining basic units according to specific standards. …”
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Forest Fire Risk Prediction in South Korea Using Google Earth Engine: Comparison of Machine Learning Models by Jukyeong Choi, Youngjo Yun, Heemun Chae

“…DEM, NDVI, and population density consistently ranked as the most influential predictors. Spatial prediction maps from each model revealed consistent high-risk areas with some local prediction differences. …”
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Construction of a traffic flow prediction model based on neural ordinary differential equations and Spatiotemporal adaptive networks by Li Ma, Yunshun Wang, Xiaoshi Lv, Lijun Guo

“…Abstract To address the issue of spatiotemporal illusion in short-term traffic flow prediction and deeply explore the underlying short-term traffic flow network characteristics, a traffic flow prediction model that combines long-term spatiotemporal heterogeneity with short-term spatiotemporal features is proposed. …”
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Prediction of the change in suitable growth area of Sabina tibetica on the Qinghai-Tibetan plateau using MaxEnt model by Xiaoxiong Li, Dongsheng Yang, Jingjie Wang, Gang Pan

“…We employed the MaxEnt model with 10 bioclimatic and topographic variables to predict its distribution shifts under RCP4.5 and RCP8.5 scenarios for 2050 and 2070. …”
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Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling. by Yolanda R Hill, Nick Child, Ben Hanson, Mikael Wallman, Ruben Coronel, Gernot Plank, Christopher A Rinaldi, Jaswinder Gill, Nicolas P Smith, Peter Taggart, Martin J Bishop

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Ultra-short-term prediction of spatio-temporal wind speed based on a hybrid deep learning model by Zixuan Chen, Zixuan Chen, Zixuan Chen, Jinman Zhang, Jinman Zhang, Shuang Zhou, Zengbao Zhao, Zengbao Zhao, Yushan Liu

“…This study develops a spatio-temporal forecasting model for predicting wind speeds across the Beijing-Tianjin-Hebei region over a 4-h horizon. …”
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Prediction models show differences in highly pathogenic avian influenza outbreaks in Japan and South Korea compared to Europe by Lene Jung Kjær, Carsten Thure Kirkeby, Anette Ella Boklund, Charlotte Kristiane Hjulsager, Anthony D. Fox, Michael P. Ward

“…Using data on H5 HPAI virus (HPAIV) occurrence from the World Organization for Animal Health and the Food and Agriculture Organization, we employed a spatial time-series modelling framework to predict occurrences in Japan and South Korea, 2020–2024. …”
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A Convolutional Neural Network-Weighted Cellular Automaton Model for the Fast Prediction of Urban Pluvial Flooding Processes by Jiarui Yang, Kai Liu, Ming Wang, Gang Zhao, Wei Wu, Qingrui Yue

“…Abstract Deep learning models demonstrate impressive performance in rapidly predicting urban floods, but there are still limitations in enhancing physical connectivity and interpretability. …”
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Developing a Prediction Model for Real-Time Incident Detection Leveraging User-Oriented Participatory Sensing Data by Md Tufajjal Hossain, Joyoung Lee, Dejan Besenski, Branislav Dimitrijevic, Lazar Spasovic

“…Additionally, multiple machine learning-based predictive models were developed and evaluated to forecast in real time whether Waze alerts correspond to actual incidents. …”
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From Patterns to Predictions: Spatiotemporal Mobile Traffic Forecasting Using AutoML, TimeGPT and Traditional Models by Hassan Ayaz, Kashif Sultan, Muhammad Sheraz, Teong Chee Chuah

“…By merging machine learning techniques with advanced temporal modeling, this study provides a strong framework for scalable and intelligent mobile traffic prediction. …”
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A Study of Landslide Susceptibility Assessment and Trend Prediction Using a Rule-Based Discrete Grid Model by Yanjun Duan, Xiaotong Zhang, Wenbo Zhao, Xinpei Han, Lingfeng Lv, Yunjun Yao, Kun Jia, Qiao Wang

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Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City by Afsaneh Ghasemi, Jamil Amanollahi

“…Lightning is one of the most severe weather hazards that will cause significant economic, social and environmental damage each year. The prediction of a lightning is a very difficult task due to the spatial and temporal expansion of weather either physically or dynamically. …”
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Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data by DongHyeon Yoon, Ha-Eun Yu, Euiho Hwang, Ki-mook Kang, Gibeom Nam, Jin-Gyeom Kim

“…Regression analysis was conducted between the extracted water surface area and observed water levels to create a predictive model, yielding a highly accurate equation with an R2 core of 0.981 on the test set. …”
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