Investigating the effects of hyperparameter sensitivity on machine learning algorithms for PV forecasting by Ehtsham Muhammad, Rotilio Marianna, Cucchiella Federica, Di Giovanni Gianni, Schettini Domenico

“…Moreover, the results of this study also highlight the significance of meteorological estimates from NASA’s Power project, as models successfully discerned the complex energy forecast patterns. …”
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Investigating the Effect of Seasonal Variation on Road Traffic Accident Occurance Along Selected Routes in Western Uganda. by Kwesiga, Ivan

“…Secondary data on RTAs and mean monthly rainfall were collected for the period covered by the study from the Uganda Traffic Police Force and the Uganda National Meteorological Authority, Kabale Meteorological Station. …”
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Spatio-Temporal Characteristics of Climate Extremes in Sub-Saharan Africa and Potential Impact of Oceanic Teleconnections by Lormido Ernesto Zita, Flávio Justino, Carlos Gurjão, James Adamu, Manuel Talacuece

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The Impact of Spatial Dynamic Error on the Assimilation of Soil Moisture Retrieval Products by Xuesong Bai, Zhengkun Qin, Juan Li, Shupeng Zhang, Lili Wang

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Influences of a Quasi-stationary Front on Particulate Matter in the Low-latitude Plateau Region in China by Wenxuan Fan, Jian Wu, Xiaopeng Li, Fangyuan Yang, Bing Chen, Jun Guo

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On the Regional Variability of dB/dt and Its Significance to GIC by A. P. Dimmock, L. Rosenqvist, D. T. Welling, A. Viljanen, I. Honkonen, R. J. Boynton, E. Yordanova

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Evaluation of Five Different Models of Valiantzas Method in Estimating Reference Crop Evapotranspiration (Case Study: Lorestan Province) by Khashayar Peyghan, Shakoor Tafi, Zahra Miri, Amir Soltani, Milad Pour Mansouri

“…In this study, in order to evaluate the accuracy of five different models of Valiantzas method, monthly reference evapotranspiration during the years 1999 to 2018, in nine meteorological stations of Lorestan province (Azna, Aleshtar, Aligudarz, Boroujerd, Poldakhtar, Khorramabad, Dorod, Kuhdasht, Noorabad) were calculated and the results were compared with the FAO-56 reference method. …”
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Evaluating the Performance of SWAN model in forecasting storm surges over the Persian Gulf and Oman Sea in a case study (June 7-10, 2023) by Zahra Ghassabi, Sara Karami, Behzad Layeghi

“…In this study, the ability of the SWAN model to predict storm surges in the Persian Gulf and the Oman Sea on June 7-9, 2023, when the National Meteorological Organization had issued several orange and red warnings, was investigated. …”
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Adaptive Month Matching: A Phenological Alignment Method for Transfer Learning in Cropland Segmentation by Reza Maleki, Falin Wu, Guoxin Qu, Amel Oubara, Loghman Fathollahi, Gongliu Yang

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Advances in the Research on Brown Carbon Aerosols: Its Concentrations, Radiative Forcing, and Effects on Climate by Shuai Li, Hua Zhang, Zhili Wang, Yonghang Chen

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Effect of Future Climate Change on Stratosphere-to-Troposphere-Exchange Driven Ozone in the Northern Hemisphere by Shovan Kumar Sahu, Lei Chen, Song Liu, Jia Xing, Rohit Mathur

“…Comparison of STE and non-STE meteorological process contributions to O3 due to climate change revealed that contributions of non-STE processes were highest in summer while STE contributions were highest in winter. …”
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Application of the ESTARFM algorithm for fusing Sentinel-2 and MODIS NDSI series in the eastern Qilian Mountains by Hui Guo, Xiaoyan Wang, Zhiqi Ouyang, Siyong Chen, Tao Che, Zhaojun Zheng

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How to Achieve a 50% Reduction in Nutrient Losses From Agricultural Catchments Under Different Climate Trajectories? by Maarten Wynants, Johan Strömqvist, Lukas Hallberg, John Livsey, Göran Lindström, Magdalena Bieroza

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Advancing Slope Stability and Hydrological Solutions Through Biocementation: A Bibliometric Review by Armstrong Ighodalo Omoregie, Tariq Ouahbi, Fock-Kui Kan, Qurratu Aini Sirat, Hafsat Omolara Raheem, Adharsh Rajasekar

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Airport delay prediction model based on regional residual and LSTM network by Jingyi QU, Meng YE, Xing QU

“…Nowadays,the civil aviation industry has a high precision requirement of airport delay prediction,so an airport delay prediction model based on the RR-LSTM network was proposed.Firstly,the airport information,meteorological information and related flight information were integrated.Then,the RR-LSTM network was used to extract the features of the fused airport data set.Finally,the Softmax classifier was adopted to classify and predict the airport delay.The proposed RR-LSTM network model can not only extract the time correlation of airport delay data effectively,but also avoid the gradient disappearance problem of deep LSTM network.The experimental results indicate that the RR-LSTM network model has a prediction accuracy of 95.52%,which achieves better prediction results than the traditional network model.The prediction accuracy can be improved about 11% by fusing the weather information and the flight information of the airport.…”
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Evaluating the Association Between Ambient Pollutants and Climate Conditions in Chiangmai, Thailand by S. Piyavadee, R. Chumaporn and V. Patipat

“…Therefore, the vital meteorological variables associated with air pollution are very useful for forecasting the harmful and severity level of each air pollutant.…”
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Performance Evaluation and Estimation of Energy Measures of Grid-Connected PV Module by R. Srimathi, J. Meenakshi, R. Vijayabhasker, Semagn Shifere Belay

“…The final yield, photovoltaic (PV) effectiveness, array yield, performance ratio, and capacity utilisation factor seem to be the variables used to evaluate performance. Using recorded meteorological data at the selected location, PVsyst software predicts both PV systems’ year-round performances. …”
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A critical approach to clustering precipitation series in the Dobrogea region, Romania by Saliba Youssef, Barbulescu Alina, Dumitriu Cristian Ștefan

“…This study provides a detailed framework for applying clustering algorithms to analyze precipitation data from the Dobrogea region in Romania, covering 46 meteorological stations from 1965 to 2005. Three clustering methods—K-means, K-medoids, and DBSCAN—were employed to partition the stations based on their monthly precipitation patterns. …”
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Predicting Last Spring Freezing Day in West and Northwest of Iran by Hasan Zolfaghari, Gholamreza Zahedi, Tayebe Sajjadifar

“…In this paper we try to predict last freezing day of spring for 17 meteorological stations in west and northwest of Iran by Artificial Neural Networks. …”
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Analysis of Temporal and Spatial Characteristics of Precipitation Series in Weihe River Basin Based on Sample Entropy by MU Jiaxin, SUN Dongyong, RU Ya'nan, WANG Miaomiao, LI Xianwei, LI Ya'nan

“…In a changing environment,the temporal and spatial complexity of precipitation in the Weihe River Basin changes.In this paper,the measured daily precipitation data of 21 meteorological stations in the Weihe River Basin in 59 years from 1960 to 2018 are selected.The static and dynamic evolution characteristics of the complexity of precipitation series in the Weihe River Basin are studied by the moving sample entropy (M-SampEn).The mutation test is carried out by the combination of the moving cut sample entropy (MC-SampEn) and Bayesian analysis.The results show that the complexity of precipitation series in the Weihe River Basin has spatial differences,and the precipitation complexity and intergenerational variation trend in each sub-region are relatively consistent.The precipitation of the upper and middle reaches of Weihe River and Jinghe River Basin mutated in 1995,and that of the lower reaches of Weihe River mutated in 1991 while that of the Beiluo River Basin mutated in 1970 and 2000.…”
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