Article
Automation & Control Systems
Weijie Zhou, Yuke Cheng, Song Ding, Li Chen, Ruojin Li
Summary: The article introduces a grey seasonal least square support vector regression model that reflects seasonal variations by combining dummy variables and grey accumulation generation operation, with the introduction of a regulation method to enhance model stability and generalization. Experimental results demonstrate the model's superiority in seasonal time series analysis.
Article
Automation & Control Systems
Guohui Li, Jin Lu, Kang Chen, Hong Yang
Summary: With the emergence of new mutant corona virus disease 2019 (COVID-19) strains such as Delta and Omicron, the number of infected people in various countries has reached a new high. Accurate prediction of the number of infected people is of far-reaching significance to epidemiological prevention in all countries of the world. A new hybrid prediction model of COVID-19 is proposed to improve the prediction accuracy of COVID-19 daily new case data, which consists of four modules: decomposition, complexity judgment, prediction, and error correction.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Lin Bao-De, Zhang Xin-Yang, Zhang Mei, Li Hui, Lu Guang-Qian
Summary: This paper establishes a load forecasting model to increase the precision of meteorological impacts, temperature, and short-term power load forecasting. It proposes a short-term power load forecasting technique based on AI algorithm, IGA-LS-SVM, and improves the forecast accuracy and generalization capability through parameter optimization. The model uses temperature, load, weather state, working and holiday days as input, and load value as predicted output, showing promising results for short-term power load prediction.
Article
Computer Science, Theory & Methods
Umesh Gupta, Deepak Gupta
Summary: This paper presents two efficient variant models to handle noise and outliers, obtaining solutions by solving a system of linear equations and minimizing the impact of noise. The proposed models demonstrate exceptional generalization performance.
FUZZY SETS AND SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Zaher Mundher Yaseen, Mumtaz Ali, Ahmad Sharafati, Nadhir Al-Ansari, Shamsuddin Shahid
Summary: The study examined the ability of various machine learning models to predict droughts, with the ELM model identified as the most effective in forecasting droughts.
SCIENTIFIC REPORTS
(2021)
Article
Energy & Fuels
Xin-yue Fu, Zhong-kai Feng, Hui Cao, Bao-fei Feng, Zheng-yu Tan, Yin-shan Xu, Wen-jing Niu
Summary: This paper proposes an enhanced machine learning model, combining twin support vector regression, singular spectrum analysis, and grey wolf optimizer, for streamflow time series forecasting. The results show that the proposed model can yield superior results compared with traditional forecasting models.
Review
Ecology
Moncef Bouaziz, Emna Medhioub, Elmar Csaplovisc
Summary: This study used the Standardized Precipitation Index (SPI) to classify and track drought events from 1981 to 2019, aiming to establish an effective monitoring and forecasting system.
JOURNAL OF ARID ENVIRONMENTS
(2021)
Article
Multidisciplinary Sciences
Mohammad G. H. Alijani, Mohammad H. Neshati
Summary: In this paper, the crosstalk sensitivity of a microwave coupled-line structure due to fabrication imperfections is analyzed using the LS-SVM method. The LS-SVM method proves to be computationally efficient and accurate in predicting the worst-case crosstalk values and the probability of obtaining different outcomes of the coupled-line.
SCIENTIFIC REPORTS
(2023)
Article
Multidisciplinary Sciences
Changhong Liu, Cuiping Yang, Qi Yang, Jiao Wang
Summary: Drought in Sichuan Province shows differences in characteristics between different physiognomy types, with increasing intensity in the western region mainly concentrated in the Sichuan basin. Altitude is not the main factor causing spatial unevenness of precipitation in Sichuan Province, as altitude, temperature, longitude, and latitude jointly determine precipitation distribution.
SCIENTIFIC REPORTS
(2021)
Article
Environmental Sciences
Jingchun Lei, Quan Quan, Pingzhi Li, Denghua Yan
Summary: The study introduced a prediction model based on LSSVM optimized by GA, which accurately estimated precipitation in the Yellow River source region. EEMD method was used to select meteorological factors for precipitation prediction, without relying on historical data. The model showed that SST in the Nino 1 + 2 region had the biggest influence on prediction accuracy, followed by Ep and T.
Article
Meteorology & Atmospheric Sciences
Andrew Paul Barnes, Nick McCullen, Thomas Rodding Kjeldsen
Summary: This paper presents an approach that combines modern meteorological forecasts with convolutional neural networks to improve the forecasting of monthly regional rainfall in Great Britain. The approach outperforms traditional numerical simulations and empirical models, and shows better performance compared to ECMWF predictions across different lead times.
THEORETICAL AND APPLIED CLIMATOLOGY
(2023)
Article
Environmental Sciences
Guangxin Liu, Liguo Wang, Danfeng Liu
Summary: This paper proposes a least squares bias constraint additional empirical risk minimization nonparallel support vector machine (LS-BC-AERM-NSVM) for hyperspectral image classification. Experimental results show that LS-BC-AERM-NSVM achieves significant improvement in solution speed and classification accuracy compared to previous methods.
Article
Engineering, Electrical & Electronic
Zhang Zihao, Wang Xuefeng, Yu Junwei, Mu Yashuang
Summary: This paper proposes a disparity refinement method based on the least square support vector machine (LSSVM), which predicts disparity values using a regression model and removes outliers based on residual analysis. The experimental results demonstrate that the proposed method outperforms current disparity refinement methods.
SIGNAL IMAGE AND VIDEO PROCESSING
(2022)
Article
Engineering, Marine
Wenhe Shen, Jianxi Yao, Xinjue Hu, Jialun Liu, Shijie Li
Summary: With the rapid development of Maritime Autonomous Surface Ships (MASS), building an accurate ship dynamics model using system identification method has become a critical issue. This study proposes a non-parametric and robust two-phase system identification method, which filters the data using an improved complete ensemble empirical mode decomposition and models the ship dynamics using Semblance least square support vector machine (S-LS-SVM) with a state-of-the-art Semblance kernel function. Compared to traditional methods, this method significantly improves the Root Mean Square Error (RMSE) of overall prediction on the test dataset.
Article
Computer Science, Artificial Intelligence
Jujie Wang, Liu Feng, Yang Li, Junjie He, Chunchen Feng
Summary: Stock index forecasting is crucial for investment decision and risk management. The proposed deep nonlinear ensemble framework includes Singular Spectrum Analysis, Enhanced Weighted Support Vector Machine, Recurrent Neural Network, and Gaussian Process Regression, providing accurate point and interval forecasting for stock indices.
COGNITIVE COMPUTATION
(2021)
Article
Geosciences, Multidisciplinary
Muhammad Shafeeque, Yi Luo, Arfan Arshad, Sher Muhammad, Muhammad Ashraf, Quoc Bao Pham
Summary: The present study uses the SPHY model and CMIP6 climate data to quantitatively assess glacio-hydrological changes in the Upper Indus Basin. The study finds that future glacier area and freshwater availability will decrease, and the contributions of snowmelt, glacier melt, baseflow, and rain-runoff to total runoff will change, as will the contribution of the critical zone. In addition, low flows are projected to increase while high flows are likely to decrease. Warming temperature is identified as the dominant driver for changes in glacier area and total runoff.
Article
Engineering, Multidisciplinary
Pakorn Ditthakit, Sirimon Pinthong, Nureehan Salaeh, Jakkarin Weekaew, Thai Thanh Tran, Quoc Bao Pham
Summary: This study compares the applicability of machine learning methods and the GR2M model for simulating monthly runoff. The results show that machine learning algorithms, particularly SVR-rbf, outperform the GR2M model in stations with low correlation coefficients between input and output datasets.
AIN SHAMS ENGINEERING JOURNAL
(2023)
Article
Engineering, Environmental
Ehsan Shahiri Tabarestani, Sanaz Hadian, Quoc Bao Pham, Sk Ajim Ali, Dung Tri Phung
Summary: This research conducted a flood potential mapping study in Golestan Province, Iran using six novel ensemble techniques. The proposed models showed high accuracy and capability in flood susceptibility assessment.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Article
Meteorology & Atmospheric Sciences
Francis Polong, Khidir Deng, Quoc Bao Pham, Nguyen Thi Thuy Linh, S. I. Abba, Ali Najah Ahmed, Duong Tran Anh, Khaled Mohamed Khedher, Ahmed El-Shafie
Summary: This study evaluates the effects of land use/land cover (LULC) and climate changes on hydrological processes in a tropical catchment. The Soil and Water Assessment Tool (SWAT) model is used to simulate different combinations of LULC and climate scenarios. The results show that climate changes have a greater impact on the simulated parameters than changes in LULC. The study emphasizes the need to assess the isolated and combined effects of LULC and climatic changes when evaluating impacts on hydrological processes.
THEORETICAL AND APPLIED CLIMATOLOGY
(2023)
Article
Engineering, Civil
Duong Tran Anh, Dat Vi Thanh, Hoang Minh Le, Bang Tran Sy, Ahad Hasan Tanim, Quoc Bao Pham, Thanh Duc Dang, Son T. Mai, Nguyen Mai Dang
Summary: This study aims to find the best optimization algorithm to improve the forecasting accuracy of rainfall-runoff modeling. The deep learning LSTM models were developed at two hydro-meteorological stations in the Mekong Delta, Vietnam, and the results showed that the Adagrad optimizer has the best model performance.
WATER RESOURCES MANAGEMENT
(2023)
Article
Environmental Sciences
Rabeea Noor, Arfan Arshad, Muhammad Shafeeque, Jinping Liu, Azhar Baig, Shoaib Ali, Aarish Maqsood, Quoc Bao Pham, Adil Dilawar, Shahbaz Nasir Khan, Duong Tran Anh, Ahmed Elbeltagi
Summary: This study investigates the performance of a downscaled-calibration procedure to generate fine-scale gridded precipitation estimates. The results indicate that the MGWR model outperforms the RF model in predicting precipitation. The method of combining high-resolution satellite data and rain gauge observations improves the accuracy and precision of precipitation estimation.
Article
Environmental Sciences
Marzieh Naem Hasani, Kouros Nekoufar, Morteza Biklarian, Morteza Jamshidi, Quoc Bao Pham, Duong Tran Anh
Summary: This study aimed to investigate the influence of roughness on pressure fluctuations in sudden expanding stilling basins. The results showed that roughness reduces the intensity of pressure fluctuations in sudden expanding stilling basins. Additionally, in the sudden expanding sections, the energy loss increases and the intensity of pressure fluctuations decreases due to the formation of lateral vortices. The maximum values of extreme pressure fluctuations occur in the range 0.609 < X < 3.385, suggesting the importance of reinforcing the bed of stilling basins in this range.
Article
Green & Sustainable Science & Technology
Hadi Nazaripouya, Mehdi Sepehri, Abbas Atapourfard, Bagher Ghermezcheshme, Celso Augusto Guimaraes Santos, Mehdi Khoshbakht, Sarita Gajbhiye Meshram, Vikas Kumar Rana, Nguyen Thi Thuy Linh, Quoc Bao Pham, Duong Tran Anh
Summary: This study evaluated the impact of watershed management practices (WMP) on sediment yield in the Gonbad region of Hamadan province, Iran. The results showed that WMP had no significant effect on reducing sediment yield.
Article
Environmental Sciences
Loubna Hamdi, Nabil Defaflia, Abdelaziz Merghadi, Chamssedine Fehdi, Ali P. Yunus, Jie Dou, Quoc Bao Pham, Hazem Ghassan Abdo, Hussein Almohamad, Motrih Al-Mutiry
Summary: This study uses GPS data and PS-InSAR techniques to monitor land subsidence in the Cheria basin in Algeria. The results show significant changes in land surface, with a maximum subsidence of 500 mm over 6 years. These findings can be used to identify vulnerable areas and evaluate surface deformation for potential damage reduction in the future.
Article
Environmental Sciences
Mohammed Achite, Nehal Elshaboury, Muhammad Jehanzaib, Dinesh Kumar Vishwakarma, Quoc Bao Pham, Duong Tran Anh, Eslam Mohammed Abdelkader, Ahmed Elbeltagi
Summary: Drought negatively impacts water resources, land and soil degradation, desertification, agricultural productivity, and food security. The standardized precipitation index (SPI) is crucial for predicting meteorological droughts and managing water resources. Five machine learning models, including support vector machine (SVM), were used to model SPI at different timescales. The SVM model was found to be the most effective for predicting SPI, and satisfactory results were achieved when applying it to sub-basin 2. The suggested model outperformed other models in estimating drought and can be helpful for predicting drought on different timescales and managing water resources.
Article
Green & Sustainable Science & Technology
Phong Nguyen Thanh, Thinh Le Van, Tuan Tran Minh, Tuyen Huynh Ngoc, Worapong Lohpaisankrit, Quoc Bao Pham, Alexandre S. Gagnon, Proloy Deb, Nhat Truong Pham, Duong Tran Anh, Vuong Nguyen Dinh
Summary: In Southeast Vietnam, frequent droughts have caused significant damage and hindered socio-economic development. Water scarcity has particularly impacted the industrial and agricultural sectors. This study examined water balance and resilience in the La Nga-Luy River basin under two scenarios: business-as-usual and sustainable development approach. The results identified areas experiencing abnormal dryness and moderate droughts, as well as regions with severe and extreme droughts. The study also demonstrated the possibility of meeting irrigation water demand under different drought conditions and highlighted the importance of increased water use efficiency.
Article
Environmental Sciences
Siham Acharki, Pierre-Louis Frison, Bijeesh Kozhikkodan Veettil, Quoc Bao Pham, Sudhir Kumar Singh, Mina Amharref, Abdes Samed Bernoussi
Summary: Crop type identification is crucial for sustainable agriculture policy development and environmental evaluations. This study examined the effectiveness of different satellite sensors and classification algorithms in identifying land cover and crop types in a Mediterranean irrigated area. The findings revealed that the Support Vector Machine algorithm performed well in extracting crop type information from high-resolution imagery.
ENVIRONMENTAL MONITORING AND ASSESSMENT
(2023)
Article
Green & Sustainable Science & Technology
Subbarayan Saravanan, Nagireddy Masthan Reddy, Quoc Bao Pham, Abdullah Alodah, Hazem Ghassan Abdo, Hussein Almohamad, Ahmed Abdullah Al Dughairi
Summary: Accurate streamflow modeling is crucial for effective water resource management. This study used five machine learning models and various precipitation datasets to simulate and predict streamflow in the Pranhita subbasin in India. The results highlight the importance of machine learning models in streamflow modeling applications, providing valuable insights for water resource management and decision making.
Article
Environmental Sciences
Emmanuel Chibundo Chukwuma, Chris Chukwuma Okonkwo, Oluwasola Olakunle Daniel Afolabi, Quoc Bao Pham, Daniel Chinazom Anizoba, Chikwunonso Divine Okpala
Summary: This study used a modified DRASTIC model to evaluate the susceptibility to groundwater pollution. A novel hybrid multi-criteria decision-making model was employed to determine the interrelationships between hydrogeologic factors and their relative weights. The flexibility of GIS was utilized to improve the DRASTIC model by handling spatial data.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Environmental Sciences
Fadoua El Qorchi, Mohammed Yacoubi Khebiza, Onyango Augustine Omondi, Ahmed Karmaoui, Quoc Bao Pham, Siham Acharki
Summary: This study analyzes the main characteristics and historical drought trend in the Upper Draa Basin using the SPI, SPEI, Run Theory, and Mann-Kendall Trend Test. The results show significant variation in rainfall across different regions and years, with higher rainfall in areas of higher altitude. Drought frequency is relatively low but severe droughts have occurred in certain years.
