Article
Agronomy
Rana Muhammad Adnan Ikram, Reham R. Mostafa, Zhihuan Chen, Abu Reza Md. Towfiqul Islam, Ozgur Kisi, Alban Kuriqi, Mohammad Zounemat-Kermani
Summary: Hybrid metaheuristic algorithm is a powerful tool in AI that provides accurate ETo prediction, which is crucial for water resource availability and hydrological studies. However, its use in predicting ETo is limited. In this study, the prediction abilities of two SVR models combined with three MAs were compared. The results showed that the SVR-PSOGWO model outperformed other models and could significantly reduce RMSE in ETo prediction. This hybrid machine learning model is recommended for monthly ETo prediction in humid regions and similar climates worldwide.
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
Computer Science, Artificial Intelligence
Zhuo Wang, Pengjian Shang, Xuegeng Mao
Summary: In this paper, the cumulative residual Tsallis singular entropy (CRTSE) is introduced to measure the complex characteristics of nonlinear signals. The effectiveness and robustness of CRTSE are verified through simulation experiments. The proposed CRTSE-GWOSVM model based on grey wolf optimized support vector machine (GWOSVM) can effectively and accurately identify complex systems.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Chemistry, Analytical
Shousong Jin, Mengyi Cao, Qiancheng Qian, Guo Zhang, Yaliang Wang
Summary: This paper proposes an improved grey wolf-optimized support vector regression (IGWO-SVR) method for predicting rotation error, which addresses the limitations of existing methods in terms of time consumption and calculation accuracy. The IGWO-SVR method outperforms other prediction methods, such as BP neural networks and SVR models optimized by particle swarm algorithm and grey wolf algorithm, in terms of parameter optimization and prediction performance. The results demonstrate that the IGWO-SVR model meets the requirements of production beat and product quality with a mean squared error of 0.026, running time of 7.843 seconds, and maximum relative error of 13.5%. Thus, the IGWO-SVR method can effectively improve product quality and reduce rework rate and cost in the rotate vector (RV) reducer parts-matching model.
Article
Green & Sustainable Science & Technology
Usman Bashir Tayab, Junwei Lu, Fuwen Yang, Tahani Saad AlGarni, Muhammad Kashif
Summary: The concept of microgrid integrates conventional generators, different renewable energy resources, and energy storage systems to meet specific load demands. However, the intermittent nature of renewable energy causes a variance in output, leading to an imbalance between power generation and demand. An energy management system is needed to manage the flow of electricity among different energy resources, while ensuring cost-effective operation.
Article
Computer Science, Artificial Intelligence
Meng Liu, Kaiping Luo, Junhuan Zhang, Shengli Chen
Summary: The study shows that the hybrid algorithm combining grey wolf optimizer and support vector machine can help achieve stable excess returns, improve the predictive performance of the support vector regression machine, and achieve better profitability and reliability in the Chinese A-share market.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Agriculture, Multidisciplinary
Jianhua Dong, Xiaogang Liu, Guomin Huang, Junliang Fan, Lifeng Wu, Jie Wu
Summary: Accurate estimation of reference crop evapotranspiration (ET0) is crucial for crop water use and agricultural water resources management. This study evaluated the performance of four bio-inspired algorithm optimized kernel-based nonlinear extension of Arps decline (KNEA) models in estimating monthly ET0 across China, with the GWO-KNEA model showing the best overall performance and the input combination 2 being the most effective. Among different climate zones, station specific models in the semi-arid steppe of Inner Mongolia performed the best in general.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Agronomy
Dilip Kumar Roy, Alvin Lal, Khokan Kumer Sarker, Kowshik Kumar Saha, Bithin Datta
Summary: This study evaluates and compares the performances of different hybridized ANFIS models for predicting daily ET0, with the FA-ANFIS identified as the best performing model. The findings are significant for effective irrigation scheduling in areas with similar climatic conditions.
AGRICULTURAL WATER MANAGEMENT
(2021)
Article
Environmental Sciences
Pangam Heramb, K. V. Ramana Rao, A. Subeesh, Ankur Srivastava
Summary: Mismanagement of fresh water negatively impacts agricultural productivity. Estimating crop water requirements using reference evapotranspiration (ET0) values is vital. This study optimized different machine learning algorithms using the grey wolf optimizer and found that hybrid ML models outperformed conventional and empirical models in accurately predicting water requirements at different locations.
Article
Chemistry, Applied
Kiran Raj Bukkarapu, Anand Krishnasamy
Summary: This study developed SVR models based on FTIR spectra of biodiesel and biodiesel-diesel blends to predict important engine fuel properties. The models showed good performance in predicting the blend proportion, viscosity, cetane number, and calorific value. Compared with other regression models, SVR was found to be the most suitable approach.
FUEL PROCESSING TECHNOLOGY
(2022)
Article
Thermodynamics
Dongxiao Niu, Zhengsen Ji, Wanying Li, Xiaomin Xu, Da Liu
Summary: This paper proposes a secondary decomposition model to reduce the complexity of power demand sequences, combines different models for prediction, and further improves prediction accuracy through the Markov chain model. The case study in Zhejiang Province demonstrates that the proposed model can effectively extract the characteristics of changes in electricity demand and improve forecast accuracy.
Article
Energy & Fuels
Shuang Li, Kun Xu, Guangzhe Xue, Jiao Liu, Zhengquan Xu
Summary: An improved grey wolf optimized support vector regression model for predicting coal spontaneous combustion temperature is proposed in this study, considering the characteristics of prediction data samples and the timeliness of applicable models. The effectiveness of the improved grey wolf optimizer algorithm is verified by numerical experiments, showing stronger global search ability, faster convergence speed, and better stability. The proposed prediction model has significant advantages in accuracy and stability, providing better decision reference for predicting and warning coal spontaneous combustion fires in coal mines.
Article
Green & Sustainable Science & Technology
Chenhui Wang, Wei Guo
Summary: Accurate prediction of landslide displacement is achieved by establishing a prediction model based on VMD and GWO-SVR. The original data are decomposed using VMD, and influential factors are selected for constructing the input training data set. The model achieves good prediction accuracy by summing up the three displacement components.
Article
Meteorology & Atmospheric Sciences
Yijia Ren, Guowei Shi, Wei Sun
Summary: This study proposes a support vector machine ensemble model based on grey wolf optimization to predict annual high-temperature days in Guangzhou, Shanghai, and Beijing. The results show that the model performs well in terms of prediction accuracy and average root mean square error.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2023)
Article
Computer Science, Information Systems
Abdullah Mohammed Rashid, Habshah Midi, Waleed Dhhan Slwabi, Jayanthi Arasan
Summary: This paper introduces a new robust iteratively reweighted SIMPLS based on nu-Support Vector Regression, referred to as SVR-RWSIMPLS. It proves to be more efficient, robust, and faster in computational running times compared to the traditional RWSIMPLS, especially when multiple leverage points and vertical outliers are present. The proposed diagnostic plot is also successful in accurately classifying observations into different groups.
Article
Meteorology & Atmospheric Sciences
Saman Maroufpoor, Hadi Sanikhani, Ozgur Kisi, Ravinesh C. Deo, Zaher Mundher Yaseen
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2019)
Article
Agronomy
Saman Maroufpoor, Jalal Shiri, Eisa Maroufpoor
AGRICULTURAL WATER MANAGEMENT
(2019)
Article
Engineering, Civil
Saman Maroufpoor, Eisa Maroufpoor, Omid Bozorg-Haddad, Jalal Shiri, Zaher Mundher Yaseen
JOURNAL OF HYDROLOGY
(2019)
Article
Agronomy
Amin Seyedzadeh, Saman Maroufpoor, Eisa Maroufpoor, Jalal Shiri, Omid Bozorg-Haddad, Farnoosh Gavazi
AGRICULTURAL WATER MANAGEMENT
(2020)
Article
Environmental Sciences
Saman Maroufpoor, Mohammadnabi Jalali, Saman Nikmehr, Naser Shiri, Jalal Shiri, Eisa Maroufpoor
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2020)
Article
Agronomy
Maryam Babaee, Saman Maroufpoor, Mohammadnabi Jalali, Manizhe Zarei, Ahmed Elbeltagi
Summary: The study estimated rice crop yield in Iran using artificial neural networks (ANNs) and ANN-genetic programming (GP). Results showed that ANN-GP was more accurate than stand-alone ANN, with irrigation, drainage, and soil type parameters having the most significant impact on rice yield at 36%, 28%, and 31% respectively. This proposed method can be an efficient tool for estimating rice yield and aiding decision makers in managing and developing agricultural systems.
IRRIGATION AND DRAINAGE
(2021)
Article
Agriculture, Multidisciplinary
Mehdi Jamei, Ahmed Elbeltagi, Saman Maroufpoor, Masoud Karbasi, Mozhdeh Jamei, Mohammadnabi Jalali, Negin Najafzadeh
Summary: This study utilized a novel prediction index to assess drought disasters, integrating different data and algorithms, and using an artificial neural network for simulation and prediction. The results showed that the artificial neural network model with the whale optimization algorithm performed well in terms of predictive ability and reducing root mean square error.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Agriculture, Multidisciplinary
Ali Mokhtar, Ahmed Elbeltagi, Saman Maroufpoor, Nasrin Azad, Hongming He, Karam Alsafadi, Yeboah Gyasi-Agyei, Wenming He
Summary: This study utilized machine learning models to model the blue and green water footprint of rice in Yunnan Province, showing a potential upward trend in water footprint in the future, calling for measures to mitigate the impact and ensure food security.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Multidisciplinary Sciences
Saman Maroufpoor, Omid Bozorg-Haddad, Eisa Maroufpoor, P. Winnie Gerbens-Leenes, Hugo A. Loaiciga, Dragan Savic, Vijay P. Singh
Summary: The study reveals that by establishing trade networks and utilizing multi-objective optimization models, water waste can be reduced and cropping patterns can be improved to ensure food security, providing effective decision-making solutions for water-scarce countries.
SCIENTIFIC REPORTS
(2021)
Article
Agronomy
Neda Mahmoudi, Arash Majidi, Mehdi Jamei, Mohammadnabi Jalali, Saman Maroufpoor, Jalal Shiri, Zaher Mundher Yaseen
Summary: This study developed several hybridized adaptive neuro fuzzy inference system (NF) models using meta-heuristic algorithms to improve predictability performance. The NF-FA model outperformed others in both humid and arid climates, achieving the best accuracy in estimating SMC with readily available inputs.
AGRICULTURAL WATER MANAGEMENT
(2022)
Article
Agronomy
Mehdi Jamei, Saman Maroufpoor, Younes Aminpour, Masoud Karbasi, Anurag Malik, Bakhtiar Karimi
Summary: The study aims to develop an accurate model for estimating the distribution of nitrate in a drip-fertigation system. The proposed model combines Boruta Random Forest, Whale Optimization Algorithm, and Artificial Neural Network, and is validated using various algorithms and functions. The results show that the proposed model outperforms traditional methods in terms of accuracy and robustness.
AGRICULTURAL WATER MANAGEMENT
(2022)
Article
Environmental Sciences
Saman Maroufpoor, Saad Sh. Sammen, Nadhir Alansari, S. I. Abba, Anurag Malik, Shamsuddin Shahid, Ali Mokhtar, Eisa Maroufpoor
Summary: This study used intelligent hybrid models to predict dissolved oxygen (DO) concentration, and evaluated the performance of the models using statistical metrics. The results showed that the NF-GWO model performed the best among all input combinations, accurately predicting DO concentration at the two stations.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2022)
Article
Agriculture, Multidisciplinary
Jincheng Zhou, Dan Wang, Sayna Nezhad Kheirollah, Saman Maroufpoor, Shahab S. Band
Summary: By identifying and analyzing the variables affecting wheat yield at different growth stages, the study determined the key factors influencing wheat production. The results showed that the mid-season stage suitable time window, crop coefficient, maximum temperature, relative humidity, yield bias, precipitation, and growing degree days were the most influential variables in wheat yield estimation.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Agronomy
Ahmed Elbeltagi, Jinsong Deng, Ke Wang, Anurag Malik, Saman Maroufpoor
AGRICULTURAL WATER MANAGEMENT
(2020)
Article
Engineering, Civil
Arfan Arshad, Ali Mirchi, Javier Vilcaez, Muhammad Umar Akbar, Kaveh Madani
Summary: High-resolution, continuous groundwater data is crucial for adaptive aquifer management. This study presents a predictive modeling framework that incorporates covariates and existing observations to estimate groundwater level changes. The framework outperforms other methods and provides reliable estimates for unmonitored sites. The study also examines groundwater level changes in different regions and highlights the importance of effective aquifer management.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Lihua Chen, Jie Deng, Wenzhe Yang, Hang Chen
Summary: A new grid-based distributed karst hydrological model (GDKHM) is developed to simulate streamflow in the flood-prone karst area of Southwest China. The results show that the GDKHM performs well in predicting floods and capturing the spatial variability of karst system.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Faruk Gurbuz, Avinash Mudireddy, Ricardo Mantilla, Shaoping Xiao
Summary: Machine learning algorithms have shown better performance in streamflow prediction compared to traditional hydrological models. In this study, researchers proposed a methodology to test and benchmark ML algorithms using artificial data generated by physically-based hydrological models. They found that deep learning algorithms can correctly identify the relationship between streamflow and rainfall in certain conditions, but fail to outperform traditional prediction methods in other scenarios.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yadong Ji, Jianyu Fu, Bingjun Liu, Zeqin Huang, Xuejin Tan
Summary: This study distinguishes the uncertainty in drought projection into scenario uncertainty, model uncertainty, and internal variability uncertainty. The results show that the estimation of total uncertainty reaches a minimum in the mid-21st century and that model uncertainty is dominant in tropical regions.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Z. R. van Leeuwen, M. J. Klaar, M. W. Smith, L. E. Brown
Summary: This study quantifies the effectiveness of leaky dams in reducing flood peak magnitude using a transfer function noise modelling approach. The results show that leaky dams have a significant but highly variable impact on flood peak magnitude, and managing expectations should consider event size and type.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Zeda Yin, Yasaman Saadati, M. Hadi Amini, Linlong Bian, Beichao Hu
Summary: Combined sewer overflows pose significant threats to public health and the environment, and various strategies have been proposed to mitigate their adverse effects. Smart control strategies have gained traction due to their cost-effectiveness but face challenges in balancing precision and computational efficiency. To address this, we propose exploring machine learning models and the inversion of neural networks for more efficient CSO prediction and optimization.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Qimou Zhang, Jiacong Huang, Jing Zhang, Rui Qian, Zhen Cui, Junfeng Gao
Summary: This study developed a N-cycling model for lowland rural rivers covered by macrophytes and investigated the N imports, exports, and response to sediment dredging. The findings showed a considerable N retention ability in the study river, with significant N imports from connected rivers and surrounding polders. Sediment dredging increased particulate nitrogen resuspension and settling rates, while decreasing ammonia nitrogen release, denitrification, and macrophyte uptake rates.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Xue Li, Yingyin Zhou, Jian Sha, Man Zhang, Zhong-Liang Wang
Summary: High-resolution climate data is crucial for predicting regional climate and water environment changes. In this study, a two-step downscaling method was developed to enhance the spatial resolution of GCM data and improve the accuracy for small basins. The method combined medium-resolution climate data with high-resolution topographic data to capture spatial and temporal details. The downscaled climate data were then used to simulate the impacts of climate change on hydrology and water quality in a small basin. The results demonstrated the effectiveness of the downscaling method for spatially differentiated simulations.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Tongqing Shen, Peng Jiang, Jiahui Zhao, Xuegao Chen, Hui Lin, Bin Yang, Changhai Tan, Ying Zhang, Xinting Fu, Zhongbo Yu
Summary: This study evaluates the long-term interannual dynamics of permafrost distribution and active layer thickness on the Tibetan Plateau, and predicts future degradation trends. The results show that permafrost area has been decreasing and active layer thickness has been increasing, with an accelerated degradation observed in recent decades. This has significant implications for local water cycle processes, water ecology, and water security.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Chi Zhang, Xu Zhang, Qiuhong Tang, Deliang Chen, Jinchuan Huang, Shaohong Wu, Yubo Liu
Summary: Precipitation over the Tibetan Plateau is influenced by systems such as the Asian monsoons, the westerlies, and local circulations. The Indian monsoon, the westerlies, and local circulations are the main systems affecting precipitation over the entire Tibetan Plateau. The East Asian summer monsoon primarily affects the eastern Tibetan Plateau. The Indian monsoon has the greatest influence on precipitation in the southern and central grid cells, while the westerlies have the greatest influence on precipitation in the northern and western grid cells. Local circulations have the strongest influence on the central and eastern grid cells.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Manuel Almeida, Antonio Rodrigues, Pedro Coelho
Summary: This study aimed to improve the accuracy of Total Phosphorus export coefficient models, which are essential for water management. Four different models were applied to 27 agroforestry watersheds in the Mediterranean region. The modeling approach showed significant improvements in predicting the Total Phosphorus diffuse loads.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yutao Wang, Haojie Yin, Ziyi Wang, Yi Li, Pingping Wang, Longfei Wang
Summary: This study investigated the distribution and transformation of dissolved organic nitrogen (DON) in riverbed sediments impacted by effluent discharge. The authors found that the spectral characteristics of dissolved organic matter (DOM) in surface water and sediment porewater could be used to predict DON variations in riverbed sediments. Random forest and extreme gradient boosting machine learning methods were employed to provide accurate predictions of DON content and properties at different depths. These findings have important implications for wastewater discharge management and river health.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Saba Mirza Alipour, Kolbjorn Engeland, Joao Leal
Summary: This study assesses the uncertainty associated with 100-year flood maps under different scenarios using Monte Carlo simulations. The findings highlight the importance of employing probabilistic approaches for accurate and secure flood maps, with the selection of probability distribution being the primary source of uncertainty in precipitation.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Janine A. de Wit, Marjolein H. J. van Huijgevoort, Jos C. van Dam, Ge A. P. H. van den Eertwegh, Dion van Deijl, Coen J. Ritsema, Ruud P. Bartholomeus
Summary: The study focuses on the hydrological consequences of controlled drainage with subirrigation (CD-SI) on groundwater level, soil moisture content, and soil water potential. The simulations show that CD-SI can improve hydrological conditions for crop growth, but the success depends on subtle differences in geohydrologic characteristics.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Constantin Seidl, Sarah Ann Wheeler, Declan Page
Summary: Water availability and quality issues will become increasingly important in the future due to climate change impacts. Managed Aquifer Recharge (MAR) is an effective water management tool, but often overlooked. This study analyzes global MAR applications and identifies the key factors for success, providing valuable insights for future design and application.
JOURNAL OF HYDROLOGY
(2024)
