Comparative analysis of machine learning models and explainable AI for agriculture drought prediction: A case study of the Ta-pieh mountains by Lichang Xu, Shaowei Ning, Xiaoyan Xu, Shenghan Wang, Le Chen, Rujian Long, Shengyi Zhang, Yuliang Zhou, Min Zhang, Bhesh Raj Thapa

“…The models were trained on data from 2000 to 2021, with 2022 serving as an independent case study to evaluate their prediction accuracy. …”
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Spatiotemporal Dynamics and Potential Distribution Prediction of <i>Spartina alterniflora</i> Invasion in Bohai Bay Based on Sentinel Time-Series Data and MaxEnt Modeling by Qi Wang, Guoli Cui, Haojie Liu, Xiao Huang, Xiangming Xiao, Ming Wang, Mingming Jia, Dehua Mao, Xiaoyan Li, Yihua Xiao, Huiying Li

“…Despite this local reduction, MaxEnt modeling suggests that climate trends and habitat suitability continue to support potential northward expansion, particularly in high-risk areas such as the Binhai New District, the Shandong Yellow River Delta, and the Laizhou Bay tributary estuary. …”
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A hybrid deep learning framework for global irradiance prediction using fuzzy C-Means, CNN-WNN, and Informer models by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Wissem Dimassi, Salah Hannachi

“…The proposed CNN-WNN-Informer model achieved average reductions across all cities of 67.7% in t-statistic, 73.9% in Mean Absolute Percentage Error (MAPE), 82.5% in Mean Absolute Bias Error (MABE), and 59.0% in Root Mean Square Error (RMSE), underscoring its significant improvements. …”
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Spatial extension of soil water regime variables derived from soil moisture values using geomorphological variables in Hungary by Tamás Deák, András Dobai, Zoltán Károly Kovács, Ferenc Molnár, Endre Dobos

“…The monitoring and spatial extension results indicate that GVs enhance the spatial prediction of soil moisture, capturing periodic fluctuations in the upper soil layer more effectively by using in-situ, time series soil moisture sensor readings rather than traditional, on field, one time reading approaches. …”
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DeepMiRBP: a hybrid model for predicting microRNA-protein interactions based on transfer learning and cosine similarity by Sasan Azizian, Juan Cui

“…Methods We present DeepMiRBP, a novel hybrid deep learning model specifically designed to predict microRNA-binding proteins by modeling molecular interactions. …”
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Zenith Tropospheric Delay Forecasting in the European Region Using the Informer–Long Short-Term Memory Networks Hybrid Prediction Model by Zhengdao Yuan, Xu Lin, Yashi Xu, Jie Zhao, Nage Du, Xiaolong Cai, Mengkui Li

“…We then employed this interpolated data from 2016 to 2020, along with an Informer–LSTM Hybrid Prediction Model, to develop a long-term prediction model for ZTD with a prediction duration of one year. …”
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Analysis and prediction of spatiotemporal carbon storage changes in the Taihu Lake Basin in Jiangsu Province based on PLUS and InVEST model by Yu Zhu, Bing Ma, Haibo Hu, Dongxia Ding, Hongwei Zhou, Jiaxuan Liu, Jiacai Liu, Zhirong Lin

“…It aims to assess and predict spatiotemporal carbon storage changes in the Taihu Lake Basin of Jiangsu Province.thereby providing a scientific basis for regional low-carbon land-use planning under China’s ''dual carbon'' goals. …”
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Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals by Hakan Uyanik, Mehmet Kokum, Erman Senturk, Mohamed Freeshah, Salih T. A. Ozcelik, Muhammed Halil Akpinar, Serenay Celik, Abdulkadir Sengur

“…Performance metrics, including classification accuracy, sensitivity, specificity, and F1-score, are used for evaluation. Our model achieved a classification accuracy of 97.49%, demonstrating its potential for earthquake prediction systems. …”
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Climate-Driven Invasion Risks of Japanese Beetle (<i>Popillia japonica</i> Newman) in Europe Predicted Through Species Distribution Modelling by Giuseppe Pulighe, Flavio Lupia, Valentina Manente

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Modelling height to crown base using non-parametric methods for mixed forests in China by Zeyu Zhou, Huiru Zhang, Ram P. Sharma, Xiaohong Zhang, Linyan Feng, Manyi Du, Lianjin Zhang, Huanying Feng, Xuefan Hu, Yang Yu

“…BRT and Cubist were the best approaches for modelling the four species-group specific HCBs. Although spatial structure variables had minor relative importance, further in-depth investigations are warranted.…”
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Development of a prognostic immune cell-based model for ovarian cancer using multiplex immunofluorescence by Sai Li, Boyang Jiang, Hongying Zhou, Sifu Yang, Liu Yang, Yupeng Hong

“…Conclusion This study highlights the prognostic value of immune cell spatial distribution in ovarian cancer. The ICRPI model, integrating immune cell markers and spatial analysis, provides a novel framework for predicting patient outcomes. …”
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Statistical Analysis of Atmospheric Delay Gradient and Rainfall Prediction in a Tropical Region by Anik Naha Biswas, Yee Hui Lee, Wei Tao Yeo, Wai Chong Low, Ding Yu Heh, Shilpa Manandhar

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Improved aboveground biomass estimation and regional assessment with aerial lidar in California’s subalpine forests by Sara Winsemius, Chad Babcock, Van R. Kane, Kat J. Bormann, Hugh D. Safford, Yufang Jin

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DEBEcoMod: A dynamic energy budget R tool to predict life-history traits of marine organisms across time and space by A. Giacoletti, M. Bosch-Belmar, G. Di Bona, M.C. Mangano, B. Stechele, G. Sarà

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Predictive study of machine learning combined with serum Neuregulin 4 levels for hyperthyroidism in type II diabetes mellitus by Huilan Gu, Ye Lu

“…Machine learning techniques have garnered widespread attention due to their advantages in modeling high-dimensional, heterogeneous data.ObjectiveThis study was to evaluate the predictive capability of a support vector machine (SVM) model based on serum NRG4 combined with a convolutional neural network (CNN) and long short-term memory network (LSTM)-based ultrasound feature classification (SVM-CNN+LSTM) model for predicting the occurrence of FT in patients with T2DM.MethodsStudied 500 T2DM patients (60 with FT, 440 without), and 200 healthy controls. …”
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Geospatial Robust Wheat Yield Prediction Using Machine Learning and Integrated Crop Growth Model and Time-Series Satellite Data by Rana Ahmad Faraz Ishaq, Guanhua Zhou, Guifei Jing, Syed Roshaan Ali Shah, Aamir Ali, Muhammad Imran, Hongzhi Jiang, Obaid-ur-Rehman

“…This underscores the necessity of multi-trait-based CYM approaches. Crop growth models enable trait dynamics with reflectance data and spectral indices as proxies for crop health and traits, respectively, to have real-time, spatially explicit monitoring. …”
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Spatial Evolution and Scenario Simulation of Carbon Metabolism in Coal-Resource-Based Cities Towards Carbon Neutrality: A Case Study of Jincheng, China by Li Zhu, Mengying Cao, Wenyuan Wang, Tianyue Zhang

“…This study focused on Jincheng City, utilizing the LOW EMISSIONS ANALYSIS PLATFORM (LEAP) model to predict carbon emissions from energy consumption under various scenarios from 2020 to 2060. …”
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Predicting the geospatial distribution of Chinese rice nutrient element in regional scale for the geographical origin—A case study on the traceability of Japonica rice by Meiling Sheng, Chunlin Li, Weixing Zhang, Jing Nie, Hao Hu, Weidong Lou, Xunfei Deng, Shengzhi Shao, Xiaonan Lyu, Zhouqiao Ren, Karyne M. Rogers, Syed Abdul Wadood, Yongzhi Zhang, Yuwei Yuan

“…In this study, environmental similarity was used to establish a spatial database of rice nutrient element, and then the validity of the database was verified using the back propagation artificial neural networks modeling (BPNN). …”
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Spatial and temporal characteristics of water conservation services and rapid response framework for water yield in key ecological zones of the Yiluo River basin by Junqiang Xu, Fan Wang, Chao Ren, Jianmin Bian, Tao Li, Zikai Ping

“…Study focus: In this study, we analyzed the spatial and temporal patterns of water yield and the main influencing factors of the Yiluo River Basin based on the water yield response framework constructed by the SWAT model and the intelligent optimization algorithm. …”
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Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction by Sung Jae Kim, Yongbok Cho

“…Using 3D universal kriging, the study interpolates missing HAB concentration values, transforming geospatial point data into spatially continuous grid images that serve as the foundation for predictive modeling. …”
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