Using Deep Learning to Improve Short-term Climate Prediction of Summer Precipitation in Southwestern China by Haoyuan ZHANG, Panjie QIAO, Wenqi LIU, Yongwen ZHANG

“…In recent years， Southwestern China， including Yunnan， Guizhou， Sichuan， and Chongqing， has been frequently hit by flood disasters caused by climate change， resulting in severe casualties and enormous property losses.The occurrence of these disasters is closely related to abnormal precipitation.Although traditional statistical methods and atmospheric models have achieved certain effectiveness in precipitation forecasting， effective approaches for dealing with the complex spatiotemporal characteristics of precipitation data are still lacking.With the development of machine learning technology， the convolutional long short-term memory network （ConvLSTM）， which integrates convolutional neural networks （CNN） and long short-term memory networks （LSTM）， has shown outstanding performance in addressing spatiotemporal sequence problems， particularly in the field of precipitation forecasting.In order to more accurately predict the summer precipitation in the southwestern region of China for the next year （short-term climate prediction of precipitation）， this study constructed a dataset by integrating global sea surface temperature and precipitation data in Southwestern China.The ConvLSTM was used for training and named SST-ConvLSTM.This model not only captures the spatiotemporal characteristics in real precipitation data but also learns some information from global sea surface temperature data， thereby enhancing the accuracy of short-term climate prediction of precipitation.The results show that compared to ConvLSTM that does not consider sea surface temperature and a traditional atmospheric model， SST-ConvLSTM model has significant advantages in short-term climate prediction of summer precipitation in Southwestern China.（1） Numerically， the predictions of the SST-ConvLSTM model are closest to the real precipitation data， with similar trend changes.In contrast， both ConvLSTM and the traditional atmospheric model show certain deviations in their predictions.（2） Spatially， the SST-ConvLSTM model also performs well.Its predictions are consistent with the spatial distribution of real precipitation data and accurately reflect the spatial distribution of precipitation.（3） In model evaluation， three evaluation metrics were used to assess the performance of the SST-ConvLSTM model.The results show that the SST-ConvLSTM model performs well in all evaluation metrics and achieves the best scores.These findings provide important references and insights for future research on precipitation prediction in Southwestern China.…”
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T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks. by Anning Ji, Xintao Ma

“…Inspired by the propagation idea of graph convolutional networks, we propose ripple-propagation-based attentive graph neural networks for traffic flow prediction (T-RippleGNN). Firstly, we adopt Ripple propagation to capture the topology structure of the traffic spatial model. …”
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DISTANCE- DEPENDENT COMPETITION INDICES FOR PREDICTING GROWTH OF Pinus brutia TEN. TREES IN ZAWEETA REGION

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HiCDiffusion - diffusion-enhanced, transformer-based prediction of chromatin interactions from DNA sequences by Mateusz Chiliński, Dariusz Plewczynski

“…Several solutions have been proposed, most of which are based on encoder-decoder architecture, where 1D sequence is convoluted, encoded into the latent representation, and then decoded using 2D convolutions into the Hi-C pairwise chromatin spatial proximity matrix. Those methods, while obtaining high correlation scores and improved metrics, produce Hi-C matrices that are artificial - they are blurred due to the deep learning model architecture. …”
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LSTM-based prediction method for shape error of steel truss during incremental launching construction. by Zhe Hu, Hao Chen, Chunguang Dong, Qinhe Li, Ronghui Wang

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. …”
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Shoreline Change Sensitivity in The Kocasu Stream Delta and The Future (2033 and 2043) Shore Change Predictions by Sultan Murat Uzun

“…According to the temporal prediction modelling, it is predicted that erosion will continue at the mouth of the Kocasu River.…”
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Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction. by Kang Xu, Bin Pan, MingXin Zhang, Xuan Zhang, XiaoYu Hou, JingXian Yu, ZhiZhu Lu, Xiao Zeng, QingQing Jia

“…Existing methods, however, have the following limitations: (1) insufficient exploration of interactions across different temporal scales, which restricts effective future flow prediction; (2) reliance on predefined graph structures in graph neural networks, making it challenging to accurately model the spatial relationships in complex road networks; and (3) end-to-end training, which often results in unclear optimization directions for model parameters, thereby limiting improvements in predictive performance. …”
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Runoff Prediction and Uncertainty Analysis for Xijiang River Basin Based on CMIP6 Climate Scenarios by WU Huiming, YAN Meng, ZHOU Shuai

“…Furthermore, by utilizing data from 15 climate models under CMIP6, the bias correction and spatial disaggregation (BCSD) downscaling method is applied to downscale the data to the Xijiang River Basin. …”
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Prediction of Dissolved Gas Concentration in Transformer Oil Based on Hybrid Mode Decomposition and LSTM-CNN by Tie CHEN, Zhifan ZHANG, Xianshan LI, Yifu CHEN, Hongxin LI

“…Finally, in order to enhance the fitting of the model to the temporal and spatial features of the sequence, the TA-LSTM-CNN is used to predict the decomposition components and reconstruct the gas concentration data. …”
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Monitoring and Prediction of Land Surface Phenology Using Satellite Earth Observations—A Brief Review by Mateo Gašparović, Ivan Pilaš, Dorijan Radočaj, Dino Dobrinić

“…This review provides a brief overview of key EO satellite missions, including the advanced very-high resolution radiometer (AVHRR), moderate resolution imaging spectroradiometer (MODIS), and the Landsat program, which have played an important role in capturing LSP dynamics at various spatial and temporal scales. Recent advancements in machine learning techniques have further enhanced LSP prediction capabilities, offering promising approaches for short-term prediction of vegetation phenology and cropland suitability assessment. …”
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GLC Prediction of Dioxin-Furan and Metals Emissions from a Hazardous Waste Incineration Plant by Afsaneh Afzali, M. Rashid

“…Spatial prediction and evaluation of pollutants emissions from incineration plant can be assessed by using air dispersion models. …”
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MFE-DDI: A multi-view feature encoding framework for drug-drug interaction prediction by Lingfeng Wang, Yinghong Li, Yaozheng Zhou, Liping Guo, Congzhou Chen

“…In this study, we propose the Multi-view Feature Embedding for drug-drug interaction prediction (MFE-DDI), which integrates SMILES information, molecular graph data and atom spatial semantic information to model drugs from multiple perspectives and encapsulate the intricate drug information crucial for predicting DDIs. …”
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Hybrid Neural Network Approach with Physical Constraints for Predicting the Potential Occupancy Set of Surrounding Vehicles by Bin Sun, Shichun Yang, Jiayi Lu, Yu Wang, Xinjie Feng, Yaoguang Cao

“…Furthermore, a mixture density network (MDN) is employed to estimate predictive uncertainty, transforming deterministic trajectory predictions into spatial probability distributions. …”
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Wheat yield prediction in Andalucía using MERIS Terrestrial Chlorophyll Index (MTCI) time series by V. Egea-Cobrero, V. Rodriguez-Galiano, E. Sánchez-Rodríguez, M.A. García-Pérez

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Predicting female football outcomes by machine learning: behavioural analysis of goals as high stress events by Aratz Olaizola, Ibai Errekagorri, Elsa Fernández, Julen Castellano, John Suckling, Karmele Lopez-de-Ipina

“…This study aims to enhance the performance and in-game success in women’s football by developing machine learning (ML) models that predict match outcomes based on player and team behaviour following goals. …”
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Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources by Jared D. Willard, Charuleka Varadharajan, Xiaowei Jia, Vipin Kumar

“…Modern machine learning methods increasingly outperform their process-based and empirical model counterparts for hydrologic time series prediction with their ability to extract information from large, diverse data sets. …”
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Liver Tumor Prediction using Attention-Guided Convolutional Neural Networks and Genomic Feature Analysis by S. Edwin Raja, J. Sutha, P. Elamparithi, K. Jaya Deepthi, S.D. Lalitha

“…The task of predicting liver tumors is critical as part of medical image analysis and genomics area since diagnosis and prognosis are important in making correct medical decisions. …”
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Enhancing Crop Health: Advanced Machine Learning Techniques for Prediction Disease in Palm Oil Tree by Nandy Manish, Kumar Yalakala Dinesh

“…Environmental variables like temperatures, humidity and soil conditions; as well as features of the leaves, including their texture and shape were given as input features to the trained models. To increase the spatial resolution and coverage of our predictions, we also included remote sensing data and imagery from various drones, satellites, which generate data from all over the nation.As it turns out, these machine learning models do a far better job predicting the onset of diseases in palm oil trees than typical statistical methods. …”
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A Lightweight Transformer-Based Spatiotemporal Analysis Prediction Algorithm for High-Dimensional Meteorological Data by Yinghao Tan, Junfeng Wu, Yihang Liu, Shiyu Shen, Xia Xu, Bin Pan

“…Apart from slow conventional numerical weather prediction methods, recently developed deep learning methods often fail to fully integrate spatial information of the high-dimensional data and require a significant amount of computational resources. …”
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Predicting current and future potential distribution of Changnienia amoena in China under global climate change by Xingjian Liu, Qimeng Sun, Tingting Li, Shu’an Wang, Jiahao Shen, Yueqi Sun, Mimi Li

“…The dominant environmental variables influencing its distribution were also identified. The MaxEnt model yielded an AUC of 0.990 and CBI of 0.959, indicating extremely high predictive accuracy. …”
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