Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farman Ali, Biswajeet Pradhan, Soo-Mi Choi

“…Past studies have relied mainly on traditional machine learning models, but these models do not perform well for complex spatial patterns. …”
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Soil Erosion Prediction Using Morgan-Morgan-Finney Model in a GIS Environment in Northern Ethiopia Catchment by Gebreyesus Brhane Tesfahunegn, Lulseged Tamene, Paul L. G. Vlek

“…The average soil loss estimated by TC using MMF model at catchment level was 26 t ha−1 y−1. In most parts of the catchment (>80%), the model predicted soil loss rates higher than the maximum tolerable rate (18 t ha−1 y−1) estimated for Ethiopia. …”
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Estimation and prediction of water conservation in the upper reaches of the Hanjiang River Basin based on InVEST-PLUS model by Pengtao Niu, Zhan Wang, Jing Wang, Yi Cao, Peihao Peng

“…With the gradual prominence of global water shortage and other problems, evaluating and predicting the impact of land use change on regional water conservation function is of great reference significance for carrying out national spatial planning and environmental protection, and realizing land intelligent management. …”
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A Novel Short‐Term Prediction Model for Regional Equatorial Plasma Bubble Irregularities in East and Southeast Asia by Xiukuan Zhao, Guozhu Li, Haiyong Xie, Lianhuan Hu, Wenjie Sun, Yi Li, Guofeng Dai, Jianfei Liu, Yu Li, Baiqi Ning, Michi Nishioka, Septi Perwitasari, Prasert Kenpankho

“…For 60‐min prediction, the STEP model can still achieve reasonable accuracy with an RMSE of 0.110 TECU/min and an R2 of 0.482, showing significant improvement over traditional models. …”
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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

“…Applied to New York City, UVPN leverages diverse urban morphological features such as streetscape attributes and land use patterns to predict continuous vitality distributions. The model outperforms existing architectures, achieving reductions of 34.03% and 38.66% in mean squared error for population density and pedestrian flow predictions, respectively. …”
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Predicting the needs of people living with a disability using the two-level logit-skewed exponential power model by Abayomi Ajayi, Olaniyi Olayiwola, Fadeke Apantaku, Idowu Osinuga, Oluwaseun Wale-Orojo

“…Cartograms were used to determine the spatial distribution for the proportion of doctor’s visit and cost using the predicted values. …”
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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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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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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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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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