Temperature and Precipitation Assessment and Extreme Climate Events Prediction based on the Coupled Model Intercomparison Project Phase 6 over the Qinghai-Xizang Plateau by Bo FENG, Xianhong MENG, Xianyu YANG, Mingshan DENG, Lin ZHAO, Zhaoguo LI, Lunyu SHANG

“…The Coupled Model Intercomparison Project （CMIP） provides reliable scientific data for predicting ecology， hydrology and climate under the backdrop of global change.However， there are large biases in current climate models， especially on the Qinghai-Xizang Plateau （QXP）.In this study， we employed Detrended Quantile Mapping （DQM） and Quantile Delta Mapping （QDM） methods to correct and evaluate the precipitation and temperature data of eight CMIP6 models with better simulation performance， utilizing the China Meteorological Forcing Dataset （CMFD）.The results showed that Both methods had corrected the simulation biases of the models， and the correction effects for temperature and precipitation data over the QXP were relatively consistent between the two methods.Then， based on the corrected multi-model ensemble mean （MME） results from QDM method， we analyzed the spatial and temporal variation characteristics of extreme high temperature events， low temperature events， atmospheric dryness and precipitation over the QXP in the early 21st century （2015 -2057） and later 21st century （2058-2100）.Under different emission scenarios in the future， extreme high temperature events strengthen， especially in the southeast of the QXP.Extreme high temperature events enhance with the increase of radiation.Extreme low temperature events decrease， with no occurrence in the later 21st century under high emission scenarios （SSP370 and SSP585）.Under different emission scenarios， precipitation and saturated vapor pressure difference both exhibit a significant increasing trend on the QXP.With global warming， the increase of precipitation does not mitigate atmospheric drought.The atmospheric dryness increases significantly under the future scenarios， especially in summer， at 1.3 to 2 times compared to annual average.…”
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Exploring the Habitat Distribution of <i>Decapterus macarellus</i> in the South China Sea Under Varying Spatial Resolutions: A Combined Approach Using Multiple Machine Learning and... by Qikun Shen, Peng Zhang, Xue Feng, Zuozhi Chen, Jiangtao Fan

“…This study is the first to systematically evaluate the impact of spatial resolution on environmental variable selection in machine learning models, integrating SHAP-based interpretability with MaxEnt modeling to achieve reliable habitat suitability prediction, offering valuable insights for fishery forecasting in the South China Sea.…”
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Early Fault Diagnosis and Prediction of Marine Large-Capacity Batteries Based on Real Data by Yifan Liu, Huabiao Jin, Xiangguo Yang, Telu Tang, Qijia Song, Yuelin Chen, Lin Liu, Shoude Jiang

“…Furthermore, the fault prediction method based on the iTransformer model is introduced to forecast variations in battery cluster voltages. …”
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ZWDX: a global zenith wet delay forecasting model using XGBoost by Laura Crocetti, Matthias Schartner, Marcus Franz Wareyka-Glaner, Konrad Schindler, Benedikt Soja

“…In this study, we present a global zenith wet delay (ZWD) model, called ZWDX, that offers accurate spatial and temporal ZWD predictions at any desired location on Earth. …”
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A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3) by Jiayu Ma, Haiyang Fu, J. D. Huba, Yaqiu Jin

“…The model incorporates the governing equations of the ionospheric physical model SAMI3 into the neural network to reconstruct the temporal‐spatial distribution of ionospheric plasma parameters. …”
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Real-time prediction of port water levels based on EMD-PSO-RBFNN by Lijun Wang, Shenghao Liao, Sisi Wang, Jianchuan Yin, Ronghui Li, Jingyu Guan

“…Subsequently, PSO was applied to fine-tune the center and spread parameters of the RBFNN, thereby enhancing the model’s predictive performance. The optimized PSO-RBFNN model was employed to make predictions on the decomposed sub-series. …”
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Machine Learning-Enhanced 3D GIS Urban Noise Mapping with Multi-Modal Factors by Jianping Pan, Yuzhe He, Wei Ma, Shengwang An, Lu Li, Dan Huang, Dunxin Jia

“…Most existing noise prediction models fail to fully account for three-dimensional (3D) spatial information and a wide range of environmental factors. …”
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The microenvironment cell index is a novel indicator for the prognosis and therapeutic regimen selection of cancers by Xian-Yan Yang, Nian Chen, Qian Wen, Yu Zhou, Tao Zhang, Ji Zhou, Cheng-Hui Liang, Li-Ping Han, Xiao-Ya Wang, Qing-Mei Kang, Xiao-Xia Zheng, Xue-Jia Zhai, Hong-Ying Jiang, Tian-Hua Shen, Jin-Wei Xiao, Yu-Xin Zou, Yun Deng, Shuang Lin, Jiang-Jie Duan, Jun Wang, Shi-Cang Yu

“…Furthermore, combined with the spatial distribution characteristics of the six types of MCs, an MCI-enhanced (MCI-e) model was constructed, which could predict the prognosis of the TNBC patients more accurately. …”
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Spatial immunogenomic patterns associated with lymph node metastasis in lung adenocarcinoma by Fanjie Meng, Hao Li, Ruoyi Jin, Airong Yang, Hao Luo, Xiao Li, Peiyu Wang, Yaxing Zhao, Olga Chervova, Kaicheng Tang, Sida Cheng, Bin Hu, Yun Li, Jianpeng Sheng, Fan Yang, David Carbone, Kezhong Chen, Jun Wang

“…By integrating data from NGS and mIHC, we successfully identified spatial immunogenomic patterns and developed a predictive model for LN metastasis, which was subsequently validated independently. …”
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A new geographically weighted stacked regression method for forest aboveground carbon storage estimation: A case study of bamboo forest by Jingyi Wang, Xuejian Li, Huaqiang Du, Guomo Zhou, Fangjie Mao, Mingshi Li, Enbin Liu, Weiliang Fan, Ning Han, Yanxin Xu, Zihao Huang

“…Moreover, neighboring pixels in remote sensing imagery are often highly correlated, yet few studies have explored how this spatial correlation affects AGC estimating. In this study, a geographically weighted stacked regression strategy was proposed which added the geographical information to model integration and provided a highly-accurate predictions with R2 of 0.83, and RMSE at 1.84 Mg ha−1. …”
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The Spatiotemporal Evolution and Driving Forces of the Urban Heat Island in Shijiazhuang by Xia Zhang, Yue Liu, Ruohan Chen, Menglin Si, Ce Zhang, Yiran Tian, Guofei Shang

“…The mono-window algorithm (MW) was used to retrieve land surface temperatures (LSTs), and the seasonal autoregressive integrated moving average (SARIMA) model was used to predict LST trends. Key factors such as the normalized difference vegetation index (NDVI), digital elevation model (DEM), population (POP), precipitation (PPT), impervious surface (IPS), potential evapotranspiration (PET), particulate matter 2.5 (PM2.5), and night light (NL) were analyzed using spatial autocorrelation to explore their dynamic relationship with the UHI. …”
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Cities and cellular automata by Roger White

“…Cellular automata provide a high-resolution representation of urban spatial dynamics.Consequently they give the most realistic predictions of urban structural evolution, and in particular they are able to replicate the various fractal dimensionalities of actual cities. …”
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Mapping the global risk of chikungunya virus endemicity and autochthonous transmission following importation by Yu-Feng Yang, Yun-Bo Qiu, Qiang Xu, Rui-Cheng Gao, Tian Tang, Yao Tian, Yan-He Wang, Sheng-Hong Lin, Yun-Dong Shi, Long-Tao Chen, Yan Zhang, Jun Ma, Chen-Long Lv, Guo-Lin Wang, Hai-Feng Pan, Wei Liu, Li-Qun Fang

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<i>Solanum macrocarpon</i> L. Ethanolic Leaf Extract Exhibits Neuroprotective and Anxiolytic Effects in Scopolamine-Induced Amnesic Zebrafish Model by Ion Brinza, Corina Guliev, Ibukun Oluwabukola Oresanya, Hasya Nazli Gok, Ilkay Erdogan Orhan, Lucian Hritcu

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Optimizing fully-efficient two-stage models for genomic selection using open-source software by Javier Fernández-González, Julio Isidro y Sánchez

“…Two-stage models, preferred for their simplicity and efficiency, first calculate adjusted genotypic means accounting for spatial variation within each environment, then use these means to predict GEBVs. …”
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Spatiotemporal Pattern Analysis and Prediction of Carbon Storage Based on Land Use and Cover Change: A Case Study of Jiangsu Coastal Cities in China by Ge Shi, Yutong Wang, Jingran Zhang, Jinghai Xu, Yu Chen, Wei Chen, Jiahang Liu

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Spatial-Spectral Linear Extrapolation for Cross-Scene Hyperspectral Image Classification by Lianlei Lin, Hanqing Zhao, Sheng Gao, Junkai Wang, Zongwei Zhang

“…Popular DG strategies constrain the model’s predictive behavior in synthetic space through deep, nonlinear source expansion, and an HSI generation model is usually adopted to enrich the diversity of training samples. …”
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Spatial Analysis of Anthropogenic Landscape Disturbance and Buruli Ulcer Disease in Benin. by Lindsay P Campbell, Andrew O Finley, M Eric Benbow, Jenni Gronseth, Pamela Small, Roch Christian Johnson, Ghislain E Sopoh, Richard M Merritt, Heather Williamson, Jiaguo Qi

“…Study results identified several significant variables, including the presence of natural wetland areas, warranting future investigations into these factors at additional spatial and temporal scales. A major contribution of this study included the incorporation of a spatial modeling component that predicted BU rates to new locations without strong knowledge of environmental factors contributing to disease distribution.…”
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Mapping predicted ecological states at landscape scales using remote‐sensing data and machine learning by N. J. Kleist, C. T. Domschke, A. C. Knight, T. W. Nauman, M. C. Duniway, S. K. Carter

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SPATIAL INTERPOLATION OF RAINFALL INTENSITY IN JAVA ISLAND USING ORDINARY KRIGING by Shabira A. Auliyazhafira, Fariza A. Putri, Theresia S. Nauli, Aulia R. Al Madani, I Gede Nyoman Mindra Jaya, Annisa N. Falah, Budi Nurani Ruchjana

“…To achieve this, semivariogram modeling was performed to determine the best theoretical model for spatial interpolation. …”
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