Article
Multidisciplinary Sciences
Yi Liu, Wenju Cai, Xiaopei Lin, Ziguang Li, Ying Zhang
Summary: The El Nino-Southern Oscillation (ENSO) is a consequential climate phenomenon affecting global extreme weather events often with largescale socioeconomic impacts. Research has found that the economic damage from El Nino is far greater than the benefits from La Nina, and under greenhouse warming, increased ENSO variability leads to increased economic loss.
NATURE COMMUNICATIONS
(2023)
Article
Environmental Sciences
Minghong Liu, Hong-Li Ren, Run Wang, Jieru Ma, Xin Mao
Summary: This study investigates the distinct impacts of Eastern Pacific (EP) and Central Pacific (CP) El Nino-Southern Oscillation (ENSO) on Tibetan Plateau (TP) summer precipitation. The results show that EP El Nino and CP La Nina have opposite effects on summer precipitation in the southwestern TP, with significant decreases and increases respectively, while CP El Nino causes significant decreases in central-eastern TP. This study may deepen our understanding of ENSO impacts on TP summer precipitation and have implications for regional climate predictions.
Article
Environmental Sciences
Jing Li, Eric Garshick, Shaodan Huang, Petros Koutrakis
Summary: This study investigates the influence of El Nino-Southern Oscillation (ENSO) on surface dust levels in different regions. Results show that dust concentrations are positively related with SOI, with stronger associations in North Africa and the Middle East. La Nina episodes are associated with increased dust concentrations, while El Nino events are associated with decreased dust concentrations in regions with high dust pollution.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Environmental Sciences
Anbao Zhu, Haiming Xu, Jiechun Deng, Jing Ma, Shuhui Li
Summary: The El Nino-Southern Oscillation has a significant impact on spring aerosols over mainland South East Asia, southern China, and the ocean south of Japan. The ENSO affects aerosols in East Asia mainly through modulation of upstream aerosol generation and transport processes. The physical mechanism involves changes in air moisture and precipitation leading to variations in biomass burning activities and carbonaceous aerosol emissions.
ATMOSPHERIC CHEMISTRY AND PHYSICS
(2021)
Article
Meteorology & Atmospheric Sciences
Nan Chen, Xianghui Fang
Summary: This paper develops a simple multiscale intermediate coupled stochastic model to capture the diversity and complexity of El Nino-Southern Oscillation (ENSO), and successfully reproduces the spatiotemporal dynamical evolution of different types of ENSO events.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2023)
Article
Geosciences, Multidisciplinary
Yu Zhang, Shi-Yun Yu, Dillon J. Amaya, Yu Kosaka, Malte F. Stuecker, Jun-Chao Yang, Xiaopei Lin, Lei Fan
Summary: The study reveals the connection between tropical Pacific-forced Aleutian low variability and the Pacific Meridional Mode, while tropical Pacific-forced North Pacific Oscillation does not significantly influence PMM variability. This finding provides important insights for future research on subtropical-tropical interactions.
GEOPHYSICAL RESEARCH LETTERS
(2022)
Article
Environmental Sciences
Omid Alizadeh
Summary: Research shows that the amplitude and duration of El Nino and La Nina events have not significantly changed over the past six decades. However, the amplitude variability of El Nino events is higher than that of La Nina events, while the duration variability is lower.
Article
Environmental Sciences
Fan Jia, Wenju Cai, Bolan Gan, Lixin Wu, Emanuele Di Lorenzo
Summary: Research suggests that under high-emissions warming scenarios, the North Pacific Meridional Mode (NPMM) strengthens its impact on El Nino/Southern Oscillation (ENSO), leading to increased frequency of extreme ENSO events and improved predictability.
NATURE CLIMATE CHANGE
(2021)
Article
Engineering, Marine
Ming Ze Lee, Fatemeh Mekanik, Amin Talei
Summary: El Nino Southern Oscillation (ENSO) is an important phenomenon driving global climate variability and relating to extreme events. This study develops an ENSO forecasting model using the dynamic evolving neural fuzzy inference system (DENFIS) and multiple climate variables, achieving high accuracy in short-term ENSO phase prediction.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Geosciences, Multidisciplinary
Madeline McKenna, Christina Karamperidou
Summary: This study examines the relationship between Northern Hemisphere blocking events and the Central Pacific (CP) and Eastern Pacific (EP) flavors of El Nino. The results show that these two El Nino flavors have different impacts on atmospheric circulation, affecting the strength and placement of the upper-level jet stream, and thus the frequency and duration of blocking events. Therefore, future investigations of blocking and ENSO-related variability should consider the different El Nino flavors.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Geosciences, Multidisciplinary
Cong Guan, Feng Tian, Michael J. McPhaden, Shijian Hu, Fan Wang
Summary: Salinity anomalies in the central Pacific induce the strongest surface warming during both types of El Nino, tapering off to the east and west. The distinct sea surface salinity zonal structures between the two El Ninos amplify their difference in sea surface temperature magnitude by about 10%. Salinity effects on vertical mixing and entrainment account for the different eastern Pacific and central Pacific El Nino responses.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Meteorology & Atmospheric Sciences
J. Saha, C. Price, T. Plotnik, A. Guha
Summary: This study reveals a strong connection between upper tropospheric water vapor (UTWV) and the El Nino-Southern Oscillation (ENSO), and uncovers the physical mechanism behind this connection through analyzing 41 years of data.
ATMOSPHERIC RESEARCH
(2022)
Article
Green & Sustainable Science & Technology
Babalwa Gqomfa, Thabang Maphanga, Takalani Terry Phungela, Benett Siyabonga Madonsela, Karabo Malakane, Stanley Lekata
Summary: This study investigates how ENSO affects water quality by examining COD, SS, and Escherichia coli levels along the Crocodile River. Water samples were collected from three strategic locations on the river between 2016 and 2021 and analyzed in an accredited laboratory using Python (version 3.8), Spyder, and Microsoft Excel 2019. The highest COD concentration (800 mg/L) was observed at the White River site during El Nino, followed by 600 mg/L during the normal period, and 240 mg/L during La Nina. In 2019, E. coli levels were consistently at 60 cfu/100 mL during La Nina and the normal period, while no E. coli levels were detected in 2021 during La Nina, El Nino, and the normal periods. Suspended solids were more prevalent in the White River (upstream) during the El Nino period. These findings demonstrate the ability to evaluate local impacts associated with large-scale climate variability.
Article
Geosciences, Multidisciplinary
Fei-Fei Jin
Summary: The El Nino Southern Oscillation (ENSO) phenomenon, characterized by large-scale sea surface temperature anomalies, is a major driver of global climate patterns and weather activities. ENSO exhibits remarkable spatiotemporal pattern diversity and has significant impacts on the environment, ecology, economy, and society. While the basic dynamics of ENSO are well understood, the mechanisms explaining the key features of ENSO associated with Central Pacific and Eastern Pacific events remain to be better understood.
FRONTIERS IN EARTH SCIENCE
(2022)
Article
Physics, Multidisciplinary
Saureesh Das, Rashmi Bhardwaj, Varsha Duhoon
Summary: This paper investigates the chaotic dynamics in a recharge-discharge oscillator model of El Nino Southern Oscillation (ENSO). The model equations are transformed to a van der Pol-Duffing oscillator model with an 11-year solar forcing. The numerical simulation shows that the oscillator model transitions to chaos as the forcing is increased. Multiple transitions between regular and chaotic states are observed in the bifurcation plot with varying amplitude of periodic forcing (F), and the regular and chaotic states are verified using Lyapunov exponent and recurrence quantification analysis.
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS
(2023)
Article
Engineering, Civil
Xiaole Han, Jintao Liu, Puneet Srivastava, Subhasis Mitra, Ruimin He
JOURNAL OF HYDROLOGY
(2020)
Article
Ecology
L. Sangha, J. Lamba, H. Kumar, P. Srivastava, M. Dougherty, R. Prasad
JOURNAL OF SOIL AND WATER CONSERVATION
(2020)
Article
Engineering, Civil
Subhasis Mitra, Puneet Srivastava
Summary: The study developed bivariate and multivariate drought indices that can accurately quantify and indicate drought conditions in coastal, bay, and estuarine areas. These indices were able to capture major drought events in the southeast region of the United States and were highly correlated with standardized univariate drought indices.
JOURNAL OF HYDROLOGIC ENGINEERING
(2021)
Article
Engineering, Environmental
Di Tian, Xiaogang He, Puneet Srivastava, Latif Kalin
Summary: This study developed a hybrid framework for reservoir inflow forecast, which integrated new datasets using machine learning models. The performance was evaluated in two headwater reservoirs, showing the best models and input variables for accurate inflow forecasts. The approach has potential for improving reservoir inflow forecasts globally.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Water Resources
Ritesh Karki, Puneet Srivastava, Latif Kalin, Subhasis Mitra, Sarmistha Singh
Summary: The study focused on excessive groundwater withdrawal for irrigation in the Lower Apalachicola-Chattahoochee-Flint River Basin, which has led to declining water levels and reduced baseflows. Evaluating projected impacts, the research found that certain geohydrologic zones were particularly sensitive to recharge. Future irrigation scenarios were simulated, predicting significant reductions in groundwater levels, particularly in areas where the aquifer is thinner. Additionally, analysis of stream-aquifer flux showed substantial reductions, particularly in the Lower Flint and Kinchafoonee watersheds.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2021)
Article
Environmental Sciences
Ryan P. McGehee, Dennis C. Flanagan, Puneet Srivastava, Bernard A. Engel, Chi-Hua Huang, Mark A. Nearing
Summary: Maps of erosivity play a critical role in soil conservation efforts, but current maps in the United States and globally face issues. In this study, the isoerodent map of the United States was updated using extensive precipitation measurements and it was found that topographic effects have a significant impact on erosivity. Benchmarking comparisons also revealed that existing maps underestimate erosivity. Reevaluation of correction methods is needed.
INTERNATIONAL SOIL AND WATER CONSERVATION RESEARCH
(2022)
Article
Water Resources
Sarmistha Singh, Ash Abebe, Puneet Srivastava, Indrajeet Chaubey
Summary: This study assessed the effects of large-scale oceanic-atmospheric oscillations on streamflow levels in the contiguous United States. The results identified new significant teleconnections between ENSO, PDO, AMO, NAO and streamflows. The study provides useful information for forecasting water resources in the region.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2021)
Article
Environmental Sciences
Suman Budhathoki, Jasmeet Lamba, Puneet Srivastava, Kritika Malhotra, Thomas R. Way, Sheela Katuwal
Summary: The study showed that soil macropore characteristics varied with topography and time. Besides macropore diameter, all other macropore characteristics exhibited an increasing trend. Significant differences in macropore characteristics were mainly observed in the surface layer (0-100 mm) and between different seasons.
JOURNAL OF SOILS AND SEDIMENTS
(2022)
Article
Environmental Sciences
Manashi Paul, Adnan Rajib, Masoud Negahban-Azar, Adel Shirmohammadi, Puneet Srivastava
Summary: This study aimed to improve hydrological modeling by integrating remotely sensed data, which significantly enhanced the accuracy of estimating water and crop productivity under different irrigation schemes in semi-arid regions.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Soil Science
Suman Budhathoki, Jasmeet Lamba, Puneet Srivastava, Kritika Malhotra, Thomas R. Way, Sheela Katuwal
Summary: This study characterized soil pore structure (>0.75 mm diameter) at different slope positions within a 0.40 ha pasture field using X-ray computed tomography (CT) and image analysis. The results revealed variations in macropore number and macroporosity values among different slope positions, indicating the relevance of topographical variation in influencing soil macropore characteristics.
SOIL & TILLAGE RESEARCH
(2022)
Article
Environmental Sciences
Xiaole Han, Jintao Liu, Puneet Srivastava, Hu Liu, Xiaopeng Li, Xuhui Shen, Hongbing Tan
Summary: The redistribution of hillslope soil water during and after rainstorms is influenced by soil properties and topography. 6-min data can capture short-lived flow processes, showing VWC-TWI correlations fluctuate frequently, and the correlation significantly increases during wet-dry transitions.
WATER RESOURCES RESEARCH
(2021)
Article
Geosciences, Multidisciplinary
Suman Budhathoki, Jasmeet Lamba, Puneet Srivastava, Colleen Williams, Francisco Arriaga, K. G. Karthikeyan
Summary: This study used X-ray computed tomography and image analysis to quantify the characteristics of soil macropores under different land uses and soil tillage practices. The results showed distinct differences in macropore characteristics for different treatments, particularly near the surface depth. The findings of this study have significant implications for non-equilibrium flow prediction and contaminant transport modeling in soils.
Article
Environmental Sciences
Henrique Haas, Latif Kalin, Puneet Srivastava
Summary: This study investigates the impact of forest characterization on watershed hydrological responses using the Soil and Water Assessment Tool (SWAT) model. By incorporating remote-sensing data, field observations, and published literature, an improved forest parameterization was developed and tested in two forested watersheds in the southeastern United States. The results show significant improvements in predicting leaf area index (LAI), biomass, and evapotranspiration (ET), highlighting the importance of accurately representing forest dynamics in hydrological models.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Environmental Sciences
Henrique Haas, Nathan G. F. Reaver, Ritesh Karki, Latif Kalin, Puneet Srivastava, David A. Kaplan, Carlos Gonzalez-Benecke
Summary: Forests play a critical role in the hydrologic cycle and it is important to accurately represent forest dynamics in watershed models. This study focuses on improving the representation of forest dynamics in the widely used Soil and Water Assessment Tool (SWAT) through species-specific re-parameterizations. The results show that the re-parameterized model outperforms the default model in simulating forest dynamics and has significant implications for water yield in the studied sites.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Agricultural Engineering
Hemendra Kumar, Puneet Srivastava, Brenda Ortiz, Guilherme Morata, Bijoychandra S. Takhellambam, Jasmeet Lamba, Luca Bondesan
Summary: This study investigated the spatiotemporal variability and temporal stability of soil water at various depths in two croplands sown with corn and cotton in the Tennessee Valley Region of northern Alabama during the 2018 growing season. Significant factors affecting soil water variability were identified, including topography in corn fields and soil properties in cotton fields. The study also found correlations between crop evapotranspiration, temperature, solar radiation, growing degree days, and soil water in the corn and cotton fields. The research provides insights into soil water variability, information on temporal stability, and significant factors for precision uniform irrigation scheduling.
TRANSACTIONS OF THE ASABE
(2021)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
