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Efrain Noa-Yarasca, Javier M. Osorio Leyton and Jay P. Angerer
Timely forecasting of aboveground vegetation biomass is crucial for effective management and ensuring food security. However, research on predicting aboveground biomass remains scarce. Artificial intelligence (AI) methods could bridge this research gap a...
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José Francisco Lima, Fernanda Catarina Pereira, Arminda Manuela Gonçalves and Marco Costa
Linear models, seasonal autoregressive integrated moving average (SARIMA) models, and state-space models have been widely adopted to model and forecast economic data. While modeling using linear models and SARIMA models is well established in the literat...
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Fernando Ferreira Lima dos Santos and Farzaneh Khorsandi
All-Terrain Vehicles (ATVs) are popular off-road vehicles in the United States, with a staggering 10.5 million households reported to own at least one ATV. Despite their popularity, ATVs pose a significant risk of severe injuries, leading to substantial ...
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Irina Kochetkova, Anna Kushchazli, Sofia Burtseva and Andrey Gorshenin
Fifth-generation (5G) networks require efficient radio resource management (RRM) which should dynamically adapt to the current network load and user needs. Monitoring and forecasting network performance requirements and metrics helps with this task. One ...
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Gabriela Emiliana de Melo e Costa, Frederico Carlos M. de Menezes Filho, Fausto A. Canales, Maria Clara Fava, Abderraman R. Amorim Brandão and Rafael Pedrollo de Paes
Stochastic modeling to forecast hydrological variables under changing climatic conditions is essential for water resource management and adaptation planning. This study explores the applicability of stochastic models, specifically SARIMA and SARIMAX, to ...
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Pouya Hosseinzadeh, Ayman Nassar, Soukaina Filali Boubrahimi and Shah Muhammad Hamdi
Streamflow prediction plays a vital role in water resources planning in order to understand the dramatic change of climatic and hydrologic variables over different time scales. In this study, we used machine learning (ML)-based prediction models, includi...
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Vadim Kramar and Vasiliy Alchakov
The models for forecasting time series with seasonal variability can be used to build automatic real-time control systems. For example, predicting the water flowing in a wastewater treatment plant can be used to calculate the optimal electricity consumpt...
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Michael Wood, Emanuele Ogliari, Alfredo Nespoli, Travis Simpkins and Sonia Leva
Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and...
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Lihui Chen, Zhonghua He, Xiaolin Gu, Mingjin Xu, Shan Pan, Hongmei Tan and Shuping Yang
Droughts are becoming more frequent in the karst region of southwest China due to climate change, and accurate monitoring of karst agricultural droughts is crucial. To this end, in this study, based on random forest (RF) and support vector regression (SV...
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Ayushi Chahal, Preeti Gulia, Nasib Singh Gill and Ishaani Priyadarshini
IoT devices collect time-series traffic data, which is stochastic and complex in nature. Traffic flow prediction is a thorny task using this kind of data. A smart traffic congestion prediction system is a need of sustainable and economical smart cities. ...
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