Predicting the Distribution of Mesophotic Coral Ecosystems in the Chagos Archipelago by Clara Diaz, Kerry L. Howell, Kyran P. Graves, Adam Bolton, Phil Hosegood, Edward Robinson, Nicola L. Foster

“…The goals of this study are to (1) predict the spatial distribution and extent of distinct benthic communities and MCEs in the Chagos Archipelago, central Indian Ocean, (2) test the effectiveness of a range of environmental and topography derived variables to predict the location of MCEs around Egmont Atoll and the Archipelago, and (3) independently validate the models produced. …”
Get full text

Modeling land cover changes using an enhanced Markov-future land use simulation model with spatial distribution considerations: a case study in the Yellow River Basin by Jianchen Zhang, Heying Li, Hanwen Zhang, Jiayao Wang, Guangxia Wang, JianWei Xu, Haohua Zheng, HuiLing Ma

“…The traditional Markov-future land use simulation (FLUS) model for land use prediction primarily emphasizes the quantity changes and spatial distribution of land use types, but it neglects the influence of their inherent spatial characteristics on the prediction precision. …”
Get full text

Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models by Yuting Wang, Sangar Khan, Zongwei Lin, Xinxin Qi, Kamel M. Eltohamy, Collins Oduro, Chao Gao, Paul J. Milham, Naicheng Wu

Get full text

Landslide Susceptibility Prediction Based on a CNN–LSTM–SAM–Attention Hybrid Model by Honggang Wu, Jiabi Niu, Yongqiang Li, Yinsheng Wang, Daohong Qiu

“…In this study, we propose a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Spatial Attention Mechanism (SAM) hybrid deep learning model designed for spatial landslide susceptibility prediction. …”
Get full text

Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model by Yiqiang Liu, Luming Shen, Xinghui Zhu, Yangfan Xie, Shaofang He

“…This study presents an LSTM-CNN-Attention model that integrates temporal and spatial feature extraction with attention mechanisms to improve predictive accuracy. …”
Get full text

A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction by Bao Li, Quan Yang, Jianjiang Chen, Dongjin Yu, Dongjing Wang, Feng Wan

“…In this paper, we propose a deep learning model for lane-level traffic prediction. Specifically, we take advantage of the graph convolutional network (GCN) with a data-driven adjacent matrix for spatial feature modeling and treat different lanes of the same road segment as different nodes. …”
Get full text

Integrative Framework for Decoding Spatial and Temporal Drivers of Land Use Change in Malaysia: Strategic Insights for Sustainable Land Management by Guanqiong Ye, Kehao Chen, Yiqun Yang, Shanshan Liang, Wenjia Hu, Liuyue He

“…Integrating the land use transfer matrix, GeoDetector model, and Structural Equation Modeling (SEM), we reveal a significant expansion of farmland and urban areas alongside a decline in forest cover, with notable regional variations in Malaysia. …”
Get full text

Alzheimer’s Disease Prediction Using Fisher Mantis Optimization and Hybrid Deep Learning Models by Sameer Abbas, Mustafa Yeniad, Javad Rahebi

Get full text

Spatial Distribution Pattern of Aromia bungii Within China and Its Potential Distribution Under Climate Change and Human Activity by Liang Zhang, Ping Wang, Guanglin Xie, Wenkai Wang

“…Hot spot distribution areas were identified using Getis‐Ord Gi*. An optimized MaxEnt model was used to predict the potential distribution areas of A. bungii within China under four shared economic pathways by combining multivariate environmental data: (1) prediction of natural environmental variables predicted under current climate models; (2) prediction of natural environmental variables + human activities under current climate models; and (3) prediction of natural environmental variables under the future climate models (2050s and 2070s). …”
Get full text

A new water temperature modeling approach to predict thermal habitat suitability for nonnative cichlids in Florida rivers by Alexandra M. Scott, Andrew K. Carlson

“…To understand how water temperature changes may affect the spatial distribution of these nonnative species, more effective water temperature prediction models are necessary. …”
Get full text

Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation by Yue Hu, Yitong Ding, Wenjing Jiang

“…This methodological framework provides valuable insights for addressing spatial heterogeneity in environmental modeling applications.…”
Get full text

Study on characteristics of heavy precipitation and model prediction correction under typical topography in Guizhou by Zhiyu LI, Xiaoling DU, Yulei ZHU, Qingxia XIE, Tao WEI, Wenyu ZHOU, Diannan LI

Get full text

Predicting vector distribution in Europe: at what sample size are species distribution models reliable? by Lianne Mitchel, Lianne Mitchel, Guy Hendrickx, Ewan T. MacLeod, Cedric Marsboom, Cedric Marsboom

“…IntroductionSpecies distribution models can predict the spatial distribution of vector-borne diseases by forming associations between known vector distribution and environmental variables. …”
Get full text

Dynamic Prediction Method of 3D Spatial Information of Coal Mining Subsidence Water Area Integrated with Landsat Remote Sensing and Knothe Time Function by Hui Liu, Yu Li

“…Taking the 1031 working face of Wugou Coal Mine in Huaibei, Anhui, China, as the research subject, (1) a three-dimensional (3D) spatial information dynamic prediction method was proposed for high-water-level coal mining subsidence areas by combining the Knothe time function based on the probability integration method (PIM) and the principle of water balance. (2) The dynamic evolution law of the water accumulation area in the high-water-level coal mining subsidence area was studied. (3) The applicability of the dynamic prediction model of the water accumulation range in the high-water-level coal mining subsidence area was verified. …”
Get full text

Spatial distribution of archeological sites in the Kingdom of Saudi Arabia and the application of the theory of planned behavior to predict pro-environmental behavioral intention a... by Nada Sulaiman Mohammed Alali, Tarek Abdel Azim Ahmed, Eman Sarhan Shaker

Get full text

Based on the Prediction of Future Drought Evolution in Heilongjiang Province under the CMIP6 Model by LIU Tao, SI Zhenjiang, LIU Yan

Get full text

Enhancing Traffic Speed Prediction Accuracy: The Multialgorithmic Ensemble Model With Spatiotemporal Feature Engineering by Ali Ardestani, Hao Yang, Saiedeh Razavi

“…Traditional traffic prediction models often fall short due to their inability to capture the complex and dynamic nature of traffic flow. …”
Get full text

WoFSCast: A Machine Learning Model for Predicting Thunderstorms at Watch‐to‐Warning Scales by Montgomery L. Flora, Corey Potvin

“…Abstract Developing AI models that match or exceed the forecast skill of numerical weather prediction (NWP) systems but run much more quickly is a burgeoning area of research. …”
Get full text

Hybrid neural network models for time series disease prediction confronted by spatiotemporal dependencies by Hamed Bin Furkan, Nabila Ayman, Md. Jamal Uddin

“…This study addresses this gap by evaluating four established hybrid neural network models for predicting influenza outbreaks. These models were analyzed by employing time series data from eight different countries to challenge the models with imposed spatial difficulties, in a month-on-month structure. …”
Get full text

Early Modeling of the Upcoming Landsat Next Constellation for Soybean Yield Prediction Under Varying Levels of Water Availability by Luís Guilherme Teixeira Crusiol, Marcos Rafael Nanni, Rubson Natal Ribeiro Sibaldelli, Liang Sun, Renato Herrig Furlanetto, Sergio Luiz Gonçalves, Norman Neumaier, José Renato Bouças Farias

“…Early modeling of the upcoming Landsat Next products for soybean yield prediction is essential for long-term satellite monitoring strategies. …”
Get full text