Research on Electric Vehicle Charging Load Forecasting Method Based on Improved LSTM Neural Network by Chengmin Wang, Yangzi Wang, Fulong Song

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On the stochastic modeling and forecasting of the $ \mathscr{SVIR} $ epidemic dynamic model under environmental white noise by Shah Hussain, Naveed Iqbal, Elissa Nadia Madi, Mohsen Bakouri, Ilyas Khan, Wei Sin Koh

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Water availability forecasting for Naryn River using ground-based and satellite snow cover data by O. Y. Kalashnikova, A. A. Gafurov

“…This information was used to compile the forecast methods of water availability of snow‑ice and ice‑snow fed rivers for the vegetation period. …”
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Forecast Model of Russia’s Gross Domestic Product Depending on Financial Instruments of Trade in Energy and Commodities by Samkov T.L.

“…Methodology of forecasting the gross domestic product (GDP) growth for complex socio-economic systems is projected on economic conditions of the Russian Federation. …”
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High-Frequency Cryptocurrency Price Forecasting Using Machine Learning Models: A Comparative Study by Fátima Rodrigues, Miguel Machado

“…Existing forecasting methods often struggle with the inherent non-stationarity and complexity of cryptocurrency price dynamics. …”
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Study on River System Flood Forecasting Scheme for Longtan Hydropower Station Interval in Hongshui River by DU Yong, FU Yupeng, WEI Yongjiang, LI Zongtai

“…Longtan Hydropower Station is a flood control project in the Pearl River Basin,but there is currently a lack of a comprehensive river system flood forecasting scheme.In order to give full play to the flood control and storage function of Longtan Hydropower Station,tributary control stations such as Leigongtan,Moyang,Pinglihe,Pinghu,and Xianrenqiao are selected,and the key areas of concern are determined through the analysis of important sub-intervals.The river system flood forecasting scheme for the Longtan Hydropower Station interval is constructed by using the Xin'an River three-source runoff generation model,three-source lag routing convergence model,and Maskingen convergence algorithm.The research results show that the proportion of the maximum inflow flood volume of Longtan Hydropower Station is usually in uncontrolled intervals,followed by the tributaries in Guizhou Province (Mengjiang River,Bawang River,Caodu River,and Liudong River),which are the focus of flood forecasting.The total average relative peak flow error and the average relative flood volume error in the river system flood forecasting scheme are both 10%,and the average deterministic coefficients are above 0.75.The overall results are relatively accurate.Therefore,the river system flood forecasting scheme for the Longtan Hydropower Station interval can be applied to real-time flood operation forecasting,and it lays a solid foundation for further improving the “forecast,early warning,rehearsal,and contingency plan” capability.…”
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COMPARING GAUSSIAN AND EPANECHNIKOV KERNEL OF NONPARAMETRIC REGRESSION IN FORECASTING ISSI (INDONESIA SHARIA STOCK INDEX) by Yuniar Farida, Ida Purwanti, Nurissaidah Ulinnuha

“…ISSI reflects the movement of sharia stock prices as a whole. It is necessary to forecast the share price to help investors determine whether the shares should be sold, bought, or retained. …”
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Early forecasting of vegetation health index in the Srepok basin based on meteorological and hydrological drought indices by Huynh Cong Luc, Luong Van Viet

“…The study aimed to forecast early the vegetation health index (VHI) in the Srepok river basin and evaluate the contribution of drought indices to the forecast accuracy. …”
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A novel ST-iTransformer model for spatio-temporal ambient air pollution forecasting by Rui Zhang, Norhashidah Awang

“…Then, the multi-station, multi-step air pollutants forecast results of all the monitoring stations are obtained based on a linear layer. …”
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Forecasting prevalence of dengue hemorrhagic fever using ARIMA model in Sulawesi Tenggara Province, Indonesia by Mistawati Mistawati, Yasnani Yasnani, Hariati Lestari

“…ARIMA model was used for data analysis. Results: ARIMA (0.1.1)(0.1.1)4 was selected as the best-suited model. …”
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Monthly Load Forecasting in a Region Experiencing Demand Growth: A Case Study of Texas by Jeong-Hee Hong, Geun-Cheol Lee

“…In this study, we consider monthly load forecasting, which is an essential decision for energy infrastructure planning and investment. …”
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Choice of the interval traffic control system in terms of increasing demands for the coverage of forecast traffic volumes by N. V. Kornienko, M. I. Mekhedov

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Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization by Sambandh Bhusan Dhal, Debashish Kar

“…The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. …”
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Monitoring, modeling, and forecasting long-term changes in coastal seawater quality due to climate change by Xianghong Guan, Hua Huang, Xiong Ke, Xiaoqian Cheng, Heng Zhang, Acong Chen, Guanglei Qiu, Haizhen Wu, Chaohai Wei

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Inter-annual variability and forecast of the spring ice phenomena on Lake Baikal and reservoirs of the Angara cascade by E. N. Sutyrina

“…The study of interannual variability of ice phenomena on large inland waters, as well as the ice forecasts for them are of great practical importance for navigation because any ice on water interferes with navigation. …”
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Forecasting Parameters of Satellite Navigation Signal through Artificial Neural Networks for the Purpose of Civil Aviation by Karolina Krzykowska, Michał Krzykowski

“…Due to authors’ opinion, the researches should focus especially on the analysis of real-time satellite signal parameter performance or creating applications for UAVs automatically deciding about used techniques of navigation.…”
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FORECASTING RAINFALL IN PANGKALPINANG CITY USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE WITH EXOGENOUS (SARIMAX) by Ririn Amelia, Elyas Kustiawan, Ineu Sulistiana, Desy Yuliana Dalimunthe

“…When compared with the actual data and previous studies using ARIMAX, the SARIMAX model is still better in the forecasting process when compared to the ARIMAX model. …”
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A Stacking Ensemble Framework Leveraging Synthetic Data for Accurate and Stable Crop Yield Forecasting by Muhammad Waqar, Yong-Woon Kim, Yung-Cheol Byun

“…Stability and convergence analysis, supported by Wilcoxon and Friedman tests, further confirm the robustness and reliability of the proposed model for scalable crop yield forecasting.…”
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Artificial neural network forecast application for fine particulate matter concentration using meteorological data by M. Memarianfard, A.M. Hatami, M. Memarianfard

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Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting by Hyun-Jung Bae, Jong-Seong Park, Ji-hyeok Choi, Hyuk-Yoon Kwon

“…Abstract Electric load forecasting is crucial in the planning and operating electric power companies. …”
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