Downhole Pressure Pulse Signal Recognition Based on SSA-CNN-LSTM by JIANG Panqin, LIU Xingbin, JIANG Zhicheng, LI Shanwen, HE Zhuang

“…It is found that the SSA-CNN-LSTM algorithm model outperforms traditional LSTM, CNN-LSTM, and PSO (particle swarm optimization) -CNN-LSTM models in terms of both fitting ability and prediction accuracy. …”
Get full text

Large-scale assessment of rainfall-induced landslide hazard based on hydrometeorological information: application to Partenio Massif (Italy) by D. C. Roman Quintero, P. Marino, A. Abdullah, G. F. Santonastaso, R. Greco

“…Although the uncertainty reduces the predictive skill of all the thresholds, the hydrometeorological ones show more robustness, with small numbers of both missed and false alarms. …”
Get full text

Mapping the groundwater potential zones in mountainous areas of Southern China using GIS, AHP, and fuzzy AHP by Meng Chen, Shuangxi Zhang, Shengbo Liu, Mengkui Li, Tao Zhang, Tengfei Wu, Xiangyu Bu

“…The model performance was validated using borewell data, receiver operating characteristic (ROC) curves, and yield prediction models. …”
Get full text

A Multi-Scale Approach for Finite Element Method Structural Analysis of Injection-Molded Parts of Short Fiber-Reinforced Polymer Composite Materials by Young Seok Cho, Byungwook Jeon, Juwon Min, Kiweon Kang, Haksung Lee

“…Homogenization techniques were applied to predict effective mechanical properties, which were subsequently used to evaluate the performance of actual components at the macroscale. …”
Get full text

High vulnerability of the endemic Southern Ocean snail Neobuccinum eatoni (Buccinidae) to critical projected oceanographic changes by Rosvita González, Luis R. Pertierra, Pablo C. Guerrero, Angie Díaz

“…Employing Species Distribution Models (SDMs) on spatially cross-validated occurrences, we achieved high predictive accuracy, identifying "sea water salinity range" at mean bottom depth as the most significant predictor of habitat preferences. …”
Get full text

中心蜗杆范成法数控加工廓面误差综合研究 by 胡自化, 陈生墨, 陈小告, 张华

“…In order to explore the influence law of original errors included machine tool kinematic errors,tool wear errors on the profile machining accuracy of sun-worm,based on the multi-body system error-modeling method and the conjugate meshing theory of spatial mechanism,the sunworm profile equation included these errors is deduced.The normal error between worm theoretical profile and error profile is calculated by using Newton iteration method and the result is analyzed to describe the effect of each error and comprehensive error on sun-worm profile accuracy.The main research conclusion can effectively disclosure and predict the law of influence that the various errors for sun-worm profile normal error.Consequently,a scientific proof is supplied to compensate machine geometric errors and improve the precision and quality of the sun-worm.…”
Get full text

A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data by Kai Du, Jingni Song, Dan Chen, Ming Li, Yadi Zhu

“…First, individual spatiotemporal travel patterns are mapped and discretized in both the spatial and temporal dimensions. Travel characteristic data are then extracted for spatial grid units. …”
Get full text

Enhancing Urban Space Optimization and Governance through Artificial Intelligence: Insights from Megacities by Tengyun Hu, Tuo Chen, Guojiang Yu, Meng Zhang, Yan Ding, Han Liu, Yu Wang

“…With rapid urbanization, megacities worldwide are transitioning to stock optimization and reduction planning to address spatial constraints. …”
Get full text

A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation by Jonathan Schmidt, Luca Schmidt, Felix M. Strnad, Nicole Ludwig, Philipp Hennig

“…As this predictive task is inherently uncertain, we leverage the probabilistic nature of diffusion models and sample multiple trajectories. …”
Get full text

Machine learning approaches for imputing missing meteorological data in Senegal by Mory Toure, Nana Ama Browne Klutse, Mamadou Adama Sarr, Md Abul Ehsan Bhuiyan, Annine Duclaire Kenne, Wassila Mamadou Thiaw, Daouda Badiane, Amadou Thierno Gaye, Ousmane Ndiaye, Cheikh Mbow

“…XGB consistently outperformed all methods across variables and scenarios, achieving the highest average predictive accuracy with R2 values up to [95 % CI: 0.82–0.88], along with lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). …”
Get full text

Quantifying Uncertainties in the Quiet‐Time Ionosphere‐Thermosphere Using WAM‐IPE by Weijia Zhan, Alireza Doostan, Eric Sutton, Tzu‐Wei Fang

Get full text

Rain‐Induced Surface Sensible Heat Flux Reduces Monsoonal Rainfall Over India by Xin Zhou, Pallav Ray, Haochen Tan, Jimy Dudhia, R. S. Ajayamohan, Helber Gomes, Yipeng Pan

“…This reduction was primarily due to a reduction in vertical advection of moisture. …”
Get full text

Using random forests to forecast daily extreme sea level occurrences at the Baltic Coast by K. Bellinghausen, B. Hünicke, E. Zorita

“…<p>We have designed a machine learning method to predict the occurrence of daily extreme sea level at the Baltic Sea coast with lead times of a few days. …”
Get full text

Computational exploration of structural hypotheses for an additional sequence in a mammalian mitochondrial protein. by Aymen S Yassin, Rajendra K Agrawal, Nilesh K Banavali

“…<h4>Conclusions</h4>By hypothesizing that the insert sequence occupies the IF1 binding site, continuous IF2(mt) models that occupy both the IF2 and IF1 binding sites can be predicted computationally. …”
Get full text

Less Is More: Brain Functional Connectivity Empowered Generalizable Intention Classification With Task-Relevant Channel Selection by Haowei Lou, Zesheng Ye, Lina Yao, Yu Zhang

“…Inevitably, the sensory electrodes on the entire scalp would collect signals irrelevant to the particular BCI task, increasing the risks of overfitting in machine learning-based predictions. While this issue is being addressed by scaling up the EEG datasets and handcrafting the complex predictive models, this also leads to increased computation costs. …”
Get full text

Forecasting Stock Market Volatility Using Housing Market Indicators: A Reinforcement Learning-Based Feature Selection Approach by Pourya Zareeihemat, Samira Mohamadi, Jamal Valipour, Seyed Vahid Moravvej

“…We propose a sophisticated Early Warning System (EWS) designed to forecast stock market instability by leveraging the predictive power of housing market bubbles. Current EWS methods often face significant hurdles, including model generalization, feature selection, and hyperparameter optimization challenges. …”
Get full text

Ocean Currents Velocity Hindcast and Forecast Bias Correction Using a Deep-Learning Approach by Ali Muhamed Ali, Hanqi Zhuang, Yu Huang, Ali K. Ibrahim, Ali Salem Altaher, Laurent M. Chérubin

“…However, numerical models are often unable to accurately model and predict real ocean dynamics, leading to a lack of fulfillment of a range of services that require reliable predictions at various temporal and spatial scales. …”
Get full text

Statistical Analysis of High-Resolution Coherent Monopulse Radar Sea Clutter by Hong Zhu, Qingping Wang, Ning Tai, Jingjian Huang, Naichang Yuan

“…Finally, we perform a spectral analysis, highlighting the temporal and spatial variabilities of Doppler spectra. It is found that the individual Doppler spectra in all three channels can be represented by Gaussian-shaped power spectral densities, and their centroid and width can be modeled as two separate stage linear functions of spectrum intensity.…”
Get full text

Flood Risk Forecasting: An Innovative Approach with Machine Learning and Markov Chains Using LIDAR Data by Luigi Bibbò, Giuliana Bilotta, Giuseppe M. Meduri, Emanuela Genovese, Vincenzo Barrile

“…These extreme events are often unpredictable and pose considerable challenges for spatial planning and risk management. This study explores an innovative approach that employs machine learning and Markov chains to enhance spatial planning and predict flood risk areas. …”
Get full text

BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data by Abdullah Alourani, Farzeen Ashfaq, N. Z. Jhanjhi, Navid Ali Khan

“…Attention modules are added to the GNN and BLSTM to find high-impact attention weight values for the chosen road section. Our model offers the best prediction accuracy with a mean absolute percentage error of 5.21% and a root mean squared error of 4. …”
Get full text