Short-term rainfall prediction based on radar echo using an efficient spatio-temporal recurrent unit by Dali Wu, Shunli Zhang, Guohong Zhao, Yongchao Feng, Yuan Ma, Yue Zhang

“…The combined effect of the Self-Attention (SA) mechanism and convolution allows the model to focus on both global and local dependencies in spatial information, improving the clarity of the generated images. …”
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High-resolution global modeling of wheat’s water footprint using a machine learning ensemble approach by Murat Emeç, Abdullah Muratoğlu, Muhammed Sungur Demir

“…The study revealed distinct outcomes for different clustering methods, demonstrating the model's robustness across varying spatial scales. …”
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Performance Evaluation of Real-Time Image-Based Heat Release Rate Prediction Model Using Deep Learning and Image Processing Methods by Joohyung Roh, Sehong Min, Minsuk Kong

“…For comparative analysis, the YOLO segmentation model was used. Furthermore, the fire diameter and flame height were determined from the spatial information of the segmented flame, and the HRR was predicted based on the correlation between flame size and HRR. …”
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Predicting determinants of unimproved water supply in Ethiopia using machine learning analysis of EDHS-2019 data by Jember Azanaw, Mihret Melese, Eshetu Abera Worede

“…Geographic differences in access to better water sources were found through spatial analysis, with rural areas being the most impacted. …”
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Quantifying 3D coral reef structural complexity from 2D drone imagery using artificial intelligence by Aviv Suan, Simone Franceschini, Joushua Madin, Elizabeth Madin

“…The validation of our model resulted in R2 values of 0.71, 0.65, and 0.56 for each metric, respectively, indicating a robust predictive capability. …”
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Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem. by Daniele Da Re, Marius Gilbert, Celia Chaiban, Pierre Bourguignon, Weerapong Thanapongtharm, Timothy P Robinson, Sophie O Vanwambeke

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Research on model predictive control strategy of three-level dual-active bridge DC-DC converter by Junrui Wang, Lu Tan, Jin Li, Changjiang Ji

“…Through the switching conduction mode of the three-level DAB converter, the working conditions of the inductor current in each mode are analyzed, and the range of zero-voltage turn-on of all switches of the three-level DAB under single phase shift (SPS) modulation is derived. The spatial equation of state is established according to each mode, the purpose of fast response is achieved through the cost function, and finally the predictive control strategy of the three-level DAB model with zero voltage turn-on is obtained discretically. …”
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Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging by Vijaya Bhaskar Sadu, Sathvik Bagam, Mohd Naved, Siva Krishna Reddy Andluru, Kamalakar Ramineni, Meshal Ghalib Alharbi, Sudhakar Sengan, Rahmaan Khadhar Moideen

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Evaluation of Statistical Models of NDVI and Agronomic Variables in a Protected Agriculture System by Edgar Vladimir Gutiérrez-Castorena, Joseph Alejandro Silva-Núñez, Francia Deyanira Gaytán-Martínez, Vicente Vidal Encinia-Uribe, Gustavo Andrés Ramírez-Gómez, Emilio Olivares-Sáenz

“…This has created a database to generate predictive models of development and yield as a function of nutrient status. …”
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Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectory by Yizhen Sun, Junyou Yang, Donghui Zhao, Moses Chukwuka Okonkwo, Jianmin Zhang, Shuoyu Wang, Yang Liu

“…The kinematic model for a single front steering‐wheel forklift vehicle is constructed with all known state quantities, including the steering angle, resulting in a more accurate model description and trajectory prediction. …”
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Evaluating the Uncertainty and Predictive Performance of Probabilistic Models Devised for Grade Estimation in a Porphyry Copper Deposit by Raymond Leung, Alexander Lowe, Arman Melkumyan

“…Probabilistic models are used to describe random processes and quantify prediction uncertainties in a principled way. …”
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Deep Learning-Based Spatial Prediction of Landslide Risk in Coastal Areas Using GIS and Multicriteria Decision Making: A DeepLabV3+ Approach by Huyong Yan, Asad Khan, Ahsan Jamil, Belkendil Abdeldjalil, Taoufik Saidani, Nazih Y. Rebouh

“…The complex, nonlinear interconnections of environmental and human elements cause terrain instability and challenge conventional prediction methods. In this work, we offer a DeepLabV3+-based deep learning framework coupled with geographic information systems and multicriteria decision making methods for spatial prediction of landslide risk, over the Dubai coastal and urban region (covering approximately 4000 km²). …”
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Current and future climate suitability for the hazel dormouse in the UK and the impact on reintroduced populations by Emma L. Cartledge, Joe Bellis, Ian White, Jane L. Hurst, Paula Stockley, Sarah Dalrymple

“…Climate change threatens species in a variety of ways, such as by impacting life history traits or causing spatial and temporal distribution mismatches of interdependent species. …”
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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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Exploring the Structure of Spatial Representations. by Tamas Madl, Stan Franklin, Ke Chen, Robert Trappl, Daniela Montaldi

“…We show that these learned metrics, together with a probabilistic model of clustering based on the Bayesian cognition paradigm, allow prediction of participants' cognitive map structures in advance. …”
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FEN-MRMGCN: A Frontend-Enhanced Network Based on Multi-Relational Modeling GCN for Bus Arrival Time Prediction by Ting Qiu, Chan-Tong Lam, Bowie Liu, Benjamin K. Ng, Xiaochen Yuan, Sio Kei Im

“…The proposed module captures spatial relationships in dense, multi-route areas by using graph convolution layers based on multi-relational modeling to aggregate spatial information. …”
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Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals. by Reihaneh Hassanzadeh, Rogers F Silva, Anees Abrol, Mustafa Salman, Anna Bonkhoff, Yuhui Du, Zening Fu, Thomas DeRamus, Eswar Damaraju, Bradley Baker, Vince D Calhoun

“…Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. However, the predictive signal present in the spatial heterogeneity of brain connectivity networks is yet to be extensively studied. …”
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Enhancing soil total nitrogen prediction in rice fields using advanced Geo-AI integration of remote sensing data and environmental covariates by Novandi Rizky Prasetya, Aditya Nugraha Putra, Mochtar Lutfi Rayes, Sri Rahayu Utami

“…Recently, advanced Geospatial-Artificial Intelligence (Geo-AI) techniques such as the random forest (RF) algorithm have been developed to increase the accuracy and spatial representativeness of STN prediction. However, critical challenges remain in using datasets from multiple locations. …”
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Performance Evaluation of CMIP6-GCMs Using Three Spatial Interpolation Methods Over Catchment Area of Koyna Reservoir, India by Sucheta Dumbre, Pradnya Dixit, Shreenivas Londhe

“…The present study evaluates the performance of general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and spatial interpolation methods over the catchment area of the Koyna reservoir in Maharashtra, India. …”
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Temporal and Spatial Evolution and Driving Force Analysis of Water Conservation Function in Yunnan Province Based on Climate and Land Use Change by LIU Fengru, ZHAO Junsan, LIN Yilin, CHEN Guoping, ZHANG Longjiang, PENG Sufen

“…[Methods] Taking Yunnan Province as an example, this paper combined the system dynamics model, patch-level land use change simulation model (PLUS model), and InVEST model to build a water conservation evaluation framework under the impact of future climate and land use change. …”
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