Prediction of litchi flower induction in South China region based on the CMIP6 climate model by HOU Wei, ZHANG Liuhong, ZHANG Lei, LUAN Lan, ZHANG Mingjie, WANG Xiuzhen, ZHANG Hui

“…Additionally, we selected the average ensemble of four climate models (CanESM5, FGOALS-g3, GFDL-CM4, and IPSL-CM6A-LR) from CMIP6 to assess the spatial and temporal evolution characteristics of the commercial cultivation limits and flower formation induction of litchi under two climate scenarios, comparing the base period with future projections in the South China region. …”
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Lightning Prediction in the Tehran Region Using the WRF Model With Multiple Physical Parameterizations and an Ensemble Approach by Sakineh Khansalari, Maryam Gharaylou

“…The initial and boundary conditions for the WRF model were derived from the Global Forecast System data set, with a spatial resolution of 0.5°. …”
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A Hybrid Spatiotemporal Deep Learning Model for Short-Term Metro Passenger Flow Prediction by Hao Zhang, Jie He, Jie Bao, Qiong Hong, Xiaomeng Shi

“…A hybrid spatiotemporal deep learning model is developed to predict both inbound and outbound passenger flows for every 10 minutes. …”
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A comparative approach of machine learning models to predict attrition in a diabetes management program. by Samantha Kanny, Grisha Post, Patricia Carbajales-Dale, William Cummings, Janet Evatt, Windsor Westbrook Sherrill

“…These findings underscore the difficulty for models to accurately predict health behavior outcomes, highlighting the need for future research to improve predictive modeling to better support patient engagement and retention.…”
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Time series prediction based on the variable weight combination of the T-GCN-Luong attention and GRU models by Yushu Guo, Jiacheng Huang, Xuchu Jiang

“…The results revealed that (1) the inclusion of spatial information significantly improved the effectiveness of the temperature predictions. (2) The Luong attention mechanism weights different time steps and improves the prediction accuracy of the T-GCN model. (3) The TGLAG combination model constructed via the variable weight method exhibited good predictive performance at 15 sites. …”
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Predicting suitable habitats and conservation areas for Suaeda salsa using MaxEnt and Marxan models by Yongji Wang, Zhusong Liu, Kefan Wu, Jiamin Peng, Yanyue Mao, Guanghua Zhao, Fenguo Zhang

“…Using 130 occurrence records and 14 selected environmental variables, this study applied the MaxEnt model to predict suitable habitats of S. salsa across China under current and future climate scenarios. …”
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Development of an enhanced base unit generation framework for predicting demand in free‐floating micro‐mobility by Dohyun Lee, Kyoungok Kim

“…Although these methods are feasible and provide a uniform area division, they are highly susceptible to the Modifiable Areal Unit Problem (MAUP), which is a critical issue in spatial data analysis. Although MAUP can adversely affect predictive model learning, studies addressing this issue are scarce. …”
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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. …”
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ResHAN-GAM: A novel model for the inversion and prediction of soil organic matter content by Liying Cao, Dongjie Yin, Miao Sun, Yuzhu Yang, Musharaf Hassan, Yunpeng Duan

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Vehicle Lane Change Multistep Trajectory Prediction Based on Data and CNN_BiLSTM Model by Shijie Gao, Zhimin Zhao, Xinjian Liu, Yanli Jiao, Chunyang Song, Jiandong Zhao

“…In order to accurately predict the lane-changing trajectory of the vehicle and improve the driving safety of the vehicle, a lane-changing trajectory prediction model based on the combination of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) neural network is proposed by comprehensively considering the historical driving behavior, the spatial characteristics of surrounding vehicles and the bidirectional time sequence information of the vehicle trajectory. …”
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Prediction of runoff in the upper reaches of the Hei River based on the LSTM model guided by physical mechanisms by Huazhu Xue, Chaoqiang Guo, Guotao Dong, Chenchen Zhang, Yaokang Lian, Qian Yuan

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Construction of crown profile prediction model of Pinus yunnanensis based on CNN-LSTM-attention method by Longfeng Deng, Jianming Wang, Jiting Yin, Yuling Chen, Baoguo Wu

“…Incorporating CPCI improved prediction accuracy across all models, especially benefiting the Vanilla LSTM model. …”
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Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves by Carlos Augusto Alves Cardoso Silva, Rodnei Rizzo, Marcelo Andrade da Silva, Matheus Luís Caron, Peterson Ricardo Fiorio

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Predicting the impact of climate change on the distribution of rhododendron on the qinghai-xizang plateau using maxent model by Sen-Xin Chai, Li-Ping Ma, Zhong-Wu Ma, Yu-Tian Lei, Ya-Qiong Ye, Bo Wang, Yuan-Ming Xiao, Ying Yang, Guo-Ying Zhou

“…To investigate the possible spatial distribution of Rhododendron on the Qinghai-Xizang Plateau in light of future global warming scenarios, we employed the Maximum entropy model (MaxEnt model) to map its suitable habitat using geographic distribution data and environmental factors projected for 2050s and 2070s, considering three representative concentration pathway (RCP) scenarios, while identifying the key factors influencing their distribution. …”
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Predicting bone metastasis risk of colorectal tumors using radiomics and deep learning ViT model by Guanfeng Chen, Wenxi Liu, Yingmin Lin, Jie Zhang, Risheng Huang, Deqiu Ye, Jing Huang, Jieyun Chen

“…Results: The ViT model demonstrated superior predictive performance, achieving an AUC of 0.918 on the test set, significantly outperforming all traditional radiomics-based models. …”
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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

“…In this paper, we propose an Efficient Spatio-Temporal Recurrent Unit (ESTRU) for short-term precipitation prediction based on radar echoes. The ability of the model to process spatio-temporal information is enhanced by fusing two ConvGRU units while controlling the complexity. …”
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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

“…This study aimed to provide more accurate predictions and data-driven insights that can inform policy-making, resource allocation, and interventions to address Ethiopia’s water crisis. …”
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MSLKSTNet: Multi-Scale Large Kernel Spatiotemporal Prediction Neural Network for Air Temperature Prediction by Feng Gao, Jiaen Fei, Yuankang Ye, Chang Liu

“…However, statistical analysis reveals that temperature evolution varies across temporal and spatial scales due to factors like terrain, leading to a lack of existing temperature prediction models that can simultaneously learn both large-scale global features and small to medium-scale local features over time. …”
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Analysis of Drought Evolution Characteristics in Haihe River Basin Based on Sub-Period Prediction Model by HAN Dongmei, JIANG Shanshan

“…In order to reduce the prediction uncertainty of future extreme climate events,a sub-period prediction model was constructed based on the daily observed precipitation data of 0.5°×0.5° provided by the China Meteorological Data Service Center and the simulated data of five global climate models (GCMs) from CMIP5.Meanwhile,the spatio-temporal evolution of drought in the Haihe River Basin (HRB) during 2020—2050 was predicted.Results show that both single GCM and multi-model ensemble average can better reproduce changes in annual average precipitation in HRB,but a large error in extreme precipitation simulation exists.The sub-period prediction model was constructed by the regression relationship at the monthly scale between the five GCMs and actually observed precipitation,and the test results show that the model has significantly improved the simulation ability of extreme precipitation in HRB.In the future,HRB tends to be humid,with moderate drought mainly appearing.Spatially,the frequency and degree of drought increase from west to east.This study aims to provide a reference for improving the ability of GCMs in simulating extreme climate events and offer ideas for decision-making for future droughts in HRB.…”
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Predicting potential biomass production by geospatial modelling: The case study of citrus in a Mediterranean area by G.A. Catalano, P.R. D'Urso, C. Arcidiacono

“…The methodology combines Geographic Information System (GIS) tools, for data interpolation and map overlays, with Software for Assisted Habitat Modelling (SAHM) for local level simulations.The results of the different models showed accurate and spatially coherent predictions, with AUC values ranging from 0.85 to 0.90, and highest potentialities in the northern and eastern regions of the study area. …”
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