Improved deep learning method and high-resolution reanalysis model-based intelligent marine navigation by Zeguo Zhang, Zeguo Zhang, Zeguo Zhang, Liang Cao, Liang Cao, Liang Cao, Jianchuan Yin, Jianchuan Yin, Jianchuan Yin

Subjects: “…extreme wind forecast…”
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METHODS OF MATHEMATICAL-STATISTICAL ANALYSIS IN THE PROCESS OF EVALUATION OF THE INFLUENCE OF THE PRICES OF OIL TO THE COURSE OF CURRENCY OF EXPORTER COUNTRIES by Tatiana Kulagovskaya, Polina Lipatova

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AI-Driven Smart Agriculture: An Integrated Approach for Soil Analysis, Irrigation, and Crop-Fertilizer Recommendations by Ayesha Khaliq, Atif Khan, Salman Jan, Muhammad Umair, Asad Gulshair, Ahmad Ali, Usman Ali Shah

“…IoT-based sensors provide real-time soil analysis, measuring critical parameters such as nutrient levels, moisture, and temperature. …”
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Low-Level Polarimetric Radar Signatures in EnKF Analyses and Forecasts of the May 8, 2003 Oklahoma City Tornadic Supercell: Impact of Multimoment Microphysics and Comparisons with... by Daniel T. Dawson II, Louis J. Wicker, Edward R. Mansell, Youngsun Jung, Ming Xue

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From online hate speech to offline hate crime: the role of inflammatory language in forecasting violence against migrant and LGBT communities by Carlos Arcila Calderón, Patricia Sánchez Holgado, Jesús Gómez, Marcos Barbosa, Haodong Qi, Alberto Matilla, Pilar Amado, Alejandro Guzmán, Daniel López-Matías, Tomás Fernández-Villazala

“…After conducting preliminary data analysis to check the moderate temporal correlation, we used time series analysis to develop computational models (VAR, GLMNet, and XGBTree) to predict four time periods of these rare events on a daily and weekly basis. …”
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Conflict Management Towards Creating an Open Community: Integrated Models for Measuring and Scenario Forecasting the Level of Social Stress in Local Communities by Roman Stadniichuk

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Machine learning and response surface methodology forecasting comparison for improved spray dry scrubber performance with brine sludge-derived sorbent by B.J. Chepkonga, L. Koech, R.S. Makomere, H.L. Rutto

“…Characterization of the final desulfurization products, particularly using XRD, showed the emergence of new phases such as hannebachite (CaSO3.0·5H2O), while FTIR analysis identified unreacted portlandite and calcite. …”
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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

“…To address the challenges of incomplete information extraction and gradient explosion in a single neural network when forecasting ZTD long-term, this study introduces an Informer–LSTM Hybrid Prediction Model. …”
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Emerging Technologies of Natural Language-Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics by Min-Hua Chao, Amy J. C. Trappey, Chun-Ting Wu

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FORECASTING THE CONSUMER PRICE INDEX WITH GENERALIZED SPACE-TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR): COMPROMISE REGION AND TIME by Prizka Rismawati Arum, Anita Retno Indriani, M Al Haris

“…Forecasting results show that the CPI value for the next 6 months will increase every month for each city…”
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Synergizing Machine Learning and Physical Models for Enhanced Gas Production Forecasting: A Comparative Study of Short- and Long-Term Feasibility by Bafren K. Raoof, Ali Rabia, Usama Alameedy, Pshtiwan Shakor, Moses Karakouzian

“…Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. …”
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SIMULATION OF THE SARIMA MODEL WITH THREE-WAY ANOVA AND ITS APPLICATION IN FORECASTING LARGE CHILLIES PRICES IN FIVE PROVINCES ON JAVA ISLAND by Ratna Nur Mustika Sanusi, Budi Susetyo, Utami Dyah Syafitri

“…The effect of several factors on the parameter estimation bias can be determined by experimental design by simulating the data obtained from the generation results with several scenarios. The results of the analysis show that all factors have a significant effect on the magnitude of the parameter bias, so that all factors can affect forecasting results. …”
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Groundwater Level Forecasting using a Deep Learning Convolutional Neural Network (CNN) Model: Seidan-Farooq Study Area by Mohammad Javad Aghaei, Hamed Ketabchi

“…Given the increasing challenges in water resource management, accurate forecasting of groundwater levels is crucial for the sustainable utilization of these resources. …”
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Assessment of Machine Learning Algorithms in Short-term Forecasting of PM10 and PM2.5 Concentrations in Selected Polish Agglomerations by Bartosz Czernecki, Michał Marosz, Joanna Jędruszkiewicz

“…Thus, in addition to implementing municipal mitigation strategies, alerting residents to pollution episodes through accurate PMx forecasting is necessary. This research aimed to assess the feasibility of short-term PMx forecasting via machine learning (ML) and the subsequent identification of the primary meteorological covariates. …”
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Ways to improve the reliability of interpreting the results of geophysical surveys performed for the local forecast and prospecting for solid minerals: a geodynamic approach by N. A. Kasyanova

“…The geodynamic approach is effectively used in regional forecasting and prospecting works. However, its application for local forecasting and prospecting for solid minerals is limited and sometimes impossible. …”
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Innovative Perspectives on Ecological Assessment in the Agro-Pastoral Ecotone of Inner Mongolia: An Integrated Evaluation and Forecast of Landscape and Ecological Risks and Drivers by Jiaru Wu, Peng Han, Jiwu Zhai, Qing Zhang

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Impact of climatic and water quality parameters on Tilapia (Oreochromis niloticus) broodfish growth: Integrating ARIMA and ARIMAX for precise modeling and forecasting. by Mohammad Abu Baker Siddique, Balaram Mahalder, Mohammad Mahfujul Haque, A K Shakur Ahammad

“…Pearson correlation analysis indicated a significant association between growth increments and water quality parameters. …”
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Attention Mechanism with Spatial-Temporal Joint Deep Learning Model for the Forecasting of Short-Term Passenger Flow Distribution at the Railway Station by Zhicheng Dai, Dewei Li, Shiqing Feng

“…We conduct a comparative analysis of the prediction performance and time complexity of the proposed architecture against existing baseline models, demonstrating superior performance and robustness exhibited by the ST-Bi-LSTM model (achieving a reduction in RMSE of over 10%). …”
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Ensemble Machine Learning, Deep Learning, and Time Series Forecasting: Improving Prediction Accuracy for Hourly Concentrations of Ambient Air Pollutants by Valentino Petrić, Hussain Hussain, Kristina Časni, Milana Vuckovic, Andreas Schopper, Željka Ujević Andrijić, Simonas Kecorius, Leizel Madueno, Roman Kern, Mario Lovrić

“…The role of the hybrid model was to combine the forecasting strengths of the Prophet model with the predictive power of the Random Forest model to better capture complex temporal patterns in the data. …”
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Hybrid deep learning CNN-LSTM model for forecasting direct normal irradiance: a study on solar potential in Ghardaia, Algeria by Boumediene Ladjal, Mohamed Nadour, Mohcene Bechouat, Nadji Hadroug, Moussa Sedraoui, Abdelaziz Rabehi, Mawloud Guermoui, Takele Ferede Agajie

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