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Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province, Vietnam

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Geoscience Frontiers 2021年第5期12卷 54-68页

作者： Hoang Phan Hai Yen Binh Thai Pham Tran Van Phong Duong Hai Ha Romulus Costache Hiep Van Le Huu Duy Nguyen Mahdis Amiri Nguyen Van Tao Indra Prakash Department of Geography School of Social Sciences EducationVinh University182 Le DuanVinhNghe AnVietnam University of Transport Technology Hanoi 100000Viet Nam Institute of Geological Sciences Vietnam Academy of Science and Technology(VAST)84 Chua LangDong DaHanoiViet Nam Institute for Water and Environment Hanoi 100000Viet Nam Research Institute of the University of Bucharest 90-92 Sos.Panduri5th DistrictBucharestRomania National Institute of Hydrology and Water Management București-Ploiești Road97E1st District013686 BucharestRomania Faculty of Geography VNU University of ScienceVietnam National University334 Nguyen TraiHanoi 100000Viet Nam Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan ***Iran DDG(R)Geological Survey of India Gandhinagar 382010India

The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like *** this article,we proposed new machine learning ensemble techniques namely AdaBoost ensemble(ABLWL),Bagging ensemble(BLWL),Multi Boost ensemble(MBLWL),Rotation Forest ensemble(RFLWL)with Locally Weighted Learning(LWL)algorithm as a base classifier to build the groundwater potential map of Gia Lai province in *** this study,eleven conditioning factors(aspect,altitude,curvature,slope,Stream Transport Index(STI),Topographic Wetness Index(TWI),soil,geology,river density,rainfall,land-use)and 134 wells yield data was used to create training(70%)and testing(30%)datasets for the development and validation of the *** statistical indices were used namely Positive Predictive Value(PPV),Negative Predictive Value(NPV),Sensitivity(SST),Specificity(SPF),Accuracy(ACC),Kappa,and Receiver Operating Characteristics(ROC)curve to validate and compare performance of *** show that performance of all the models is good to very good(AUC:0.75 to 0.829)but the ABLWL model with AUC=0.89 is the *** the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters.

关键词： Locally weighted learning hybrid models Groundwater potential GIS Vietnam

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Performance of evolutionary optimized machine learning for modeling total organic carbon in core samples of shale gas fields

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Petroleum 2024年第1期10卷 150-164页

作者： Leonardo Goliatt C.M.Saporetti L.C.Oliveira E.Pereira Department of Computational and Applied Mechanics Federal University of Juiz de ForaJuiz de ForaBrazil Department of Computational Modeling Polytechnic InstituteRio de Janeiro State UniversityNova FriburgoBrazil Petrobras Rio de JaneiroBrazil Department of Stratigraphy and Paleontology Rio de Janeiro State UniversityRio de JaneiroBrazil

Rock samples'TOC content is the best indicator of the organic matter in source *** origin rock samples’analysis is used to calculate it manually by *** method requires time and resources because it relies on samples from many well intervals in source ***,research has been done to aid this *** learning algorithms can estimate total organic carbon instead of well logs and stratigraphic *** light of these efforts,the current work present a study on automating the total organic carbon estimation using machine learning approaches improved by an evolutionary methodology to give the model flexibility and *** algorithms,differential evolution,particle swarm optimization,grey wolf optimization,artificial bee colony,and evolution strategies were used to improve machine learning models to predict *** six metaheuristics were integrated into four machine learning methods:extreme learning machine,elastic net linear model,linear support vector regression,and multivariate adaptive regression *** samples from the YuDong-Nan shale gas field,located in the Sichuan basin,were used to evaluate the hybrid *** findings show that combining machine learning models with an evolutionary algorithms in a hybrid fashion produce flexible models that accurately predict *** results show that,independent of the metaheuristic used to guide the model selection,optimized extreme learning machines attained the best performance scores according to six *** hybrid models can be used in exploratory geological research,particularly for unconventional oil and gas resources.

关键词： Total organic carbon hybrid models Automated machine learning Evolutionary algorithms Geology

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Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

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Computers, Materials & Continua 2024年第6期79卷 4603-4620页

作者： Mohammad Aldossary Hatem A.Alharbi Nasir Ayub Department of Computer Engineering and Information College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Al-Dawasir11991Saudi Arabia Department of Computer Engineering College of Computer Science and EngineeringTaibah UniversityMadinah42353Saudi Arabia Department of Creative Technologies Air University IslamabadIslamabad44000Pakistan

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure,thereby revolutionizing computer ***,the rising energy consumption in cloud centers poses a significant challenge,especially with the escalating energy *** paper tackles this issue by introducing efficient solutions for data placement and node management,with a clear emphasis on the crucial role of the Internet of Things(IoT)throughout the research *** IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data *** sensors continuously monitor vital parameters such as energy usage and temperature,thereby providing a comprehensive dataset for *** data generated by the IoT is seamlessly integrated into the hybrid TCN-GRU-NBeat(NGT)model,enabling a dynamic and accurate representation of the current state of the data center *** the incorporation of the Seagull Optimization Algorithm(SOA),the NGT model optimizes storage migration strategies based on the latest information provided by IoT *** model is trained using 80%of the available dataset and subsequently tested on the remaining 20%.The results demonstrate the effectiveness of the proposed approach,with a Mean Squared Error(MSE)of 5.33%and a Mean Absolute Error(MAE)of 2.83%,accurately estimating power prices and leading to an average reduction of 23.88%in power ***,the integration of IoT data significantly enhances the accuracy of the NGT model,outperforming benchmark algorithms such as DenseNet,Support Vector Machine(SVM),Decision Trees,and *** NGT model achieves an impressive accuracy rate of 97.9%,surpassing the rates of 87%,83%,80%,and 79%,respectively,for the benchmark *** findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive

关键词： Cloud computing energy efficiency data center optimization internet of things(IoT) hybrid models

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Advancing Type II Diabetes Predictions with a hybrid LSTM-XGBoost Approach

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Journal of Data Analysis and Information Processing 2024年第2期12卷 163-188页

作者： Ayoub Djama Waberi Ronald Waweru Mwangi Richard Maina Rimiru Department of Mathematics Pan African University Institute for Basic Sciences Technology and Innovation (PAUSTI) Nairobi Kenya Department of Computing Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya School of Computing and Information Technology (SCIT) Jomo Kenyatta University of Agriculture and Technology (JKUAT) Nairobi Kenya

In this paper, we explore the ability of a hybrid model integrating Long Short-Term Memory (LSTM) networks and eXtreme Gradient Boosting (XGBoost) to enhance the prediction accuracy of Type II Diabetes Mellitus, which is caused by a combination of genetic, behavioral, and environmental factors. Utilizing comprehensive datasets from the Women in Data Science (WiDS) Datathon for the years 2020 and 2021, which provide a wide range of patient information required for reliable prediction. The research employs a novel approach by combining LSTM’s ability to analyze sequential data with XGBoost’s strength in handling structured datasets. To prepare this data for analysis, the methodology includes preparing it and implementing the hybrid model. The LSTM model, which excels at processing sequential data, detects temporal patterns and trends in patient history, while XGBoost, known for its classification effectiveness, converts these patterns into predictive insights. Our results demonstrate that the LSTM-XGBoost model can operate effectively with a prediction accuracy achieving 0.99. This study not only shows the usefulness of the hybrid LSTM-XGBoost model in predicting diabetes but it also provides the path for future research. This progress in machine learning applications represents a significant step forward in healthcare, with the potential to alter the treatment of chronic diseases such as diabetes and lead to better patient outcomes.

关键词： LSTM XGBoost hybrid models Machine Learning. Deep Learning

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Spatial Heterogeneity Modeling Using Machine Learning Based on a hybrid of Random Forest and Convolutional Neural Network (CNN)

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Journal of Data Analysis and Information Processing 2024年第3期12卷 319-347页

作者： Amadou Kindy Barry Anthony Waititu Gichuhi Lawrence Nderu Department of Mathematics Pan African University Institute for Basic Sciences Technology and Innovation (PAUISTI) Nairobi Kenya Department of Statistics and Actuarial Science Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Department of Computing School and Information Technology (SCIT) Jomo Kenyatta University of Agriculture and Technology (JKUAT) Nairobi Kenya

Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.

关键词： Spatial Heterogeneity Spatial Data Feature Selection Standardization Machine Learning models hybrid models

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A review of stand basal area growth models

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Forestry Studies in China 2007年第1期9卷 85-94页

作者： Sun Hong-gang Zhang Jian-guo Duan Ai-guo He Cai-yun Key Laboratory of Silviculture of the State Forestry Administration Research Institute of Forestry Chinese Academy of Forestry Beijing 100091 P. R. China

Growth and yield modeling has a long history in forestry. The methods of measuring the growth of stand basal area have evolved from those developed in the U.S.A. and Germany during the last century. Stand basal area modeling has progressed rapidly since the first widely used model was published by the U.S. Forest Service. Over the years, a variety of models have been developed for predicting the growth and yield of uneven/even-aged stands using stand-level approaches. The modeling methodology has not only moved from an empirical approach to a more ecological process-based approach but also accommodated a variety of techniques such as： 1） simultaneous equation methods, 2） difference models, 3） artificial neural network techniques, 4） linear/nonlinear regression models, and 5） matrix models. Empirical models using statistical methods were developed to reproduce accurately and precisely field observations. In contrast, process models have a shorter history, developed originally as research and education tools with the aim of increasing the understanding of cause and effect relationships. Empirical and process models can be married into hybrid models in which the shortcomings of both component approaches can, to some extent, be overcome. Algebraic difference forms of stand basal area models which consist of stand age, stand density and site quality can fully describe stand growth dynamics. This paper reviews the current literature regarding stand basal area models, discusses the basic types of models and their merits and outlines recent progress in modeling growth and dynamics of stand basal area. Future trends involving algebraic difference forms, good fitting variables and model types into stand basal area modeling strategies are discussed.

关键词： stand basal area empirical models process-based models algebraic difference hybrid models

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A comprehensive review and analysis of solar forecasting techniques

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Frontiers in Energy 2022年第2期16卷 187-223页

作者： Pardeep SINGLA Manoj DUHAN Sumit SAROHA Department of ECE Deenbandhu Chhotu Ram University of Science and TechnologyMurthalSonepat 131039India Department of Printing Technology(Electrical Engineering) Guru Jambheshwar University of Science and TechnologyHisar 125001India

In the last two decades,renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic,industrial,and agriculture *** forecasting plays a vital role in smooth operation,scheduling,and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV *** models and techniques have been developed in short,mid and long-term solar *** paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature,by mainly focusing on investigating the influence of meteorological variables,time horizon,climatic zone,pre-processing techniques,air pollution,and sample size on the complexity and accuracy of the *** make the paper reader-friendly,it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication,time resolution,input parameters,forecasted parameters,error metrics,and *** literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving *** accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input ***,it also discusses the diverse key constituents that affect the accuracy of a *** has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.

关键词： forecasting techniques hybrid models neural network solar forecasting error metric support vector machine(SVM)

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A hybrid MODEL FOR SIMULATING VELOCITY FIELD OF A RIVER WITH COMPLEX GEOMETRY PLUNGED BY MULTIPLE JETS

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Journal of Hydrodynamics 2006年第6期18卷 752-759页

作者： LI Lian-xia LIAO Hua-sheng LI Tian-xiang State Key Laboratory of Hydraulics and Mountain River Engineering Sichuan University Chengdu 610065China

A hybrid model that combines both physical and numerical models was employed to simulate the velocity field in a river area in complex geometry with multiple plunging jets. The simulation was based on experiments concerning energy dissipation and scour prevention at the Xiluodu Hydropower Station on the Yangtze River. The calculated results indicate that the complex geometry of the river area has a significant influence on the velocity field, especially on the circulation flow pattern at upstream and downstream of the plunging area and on the asymmetric characteristics of the spiral flow near both banks. The scour characteristics of the downstream river bed caused by the multiple jets were also predicted and analyzed according to the characteristics of the calculated velocity field. The good agreement between the simulated and experimental results indicates that the hybrid model can be used to effectively solve complicated 3-D problems with complex geometric and inlet conditions. Such problems may not easily be solved by using either a physical or a numerical model alone, and therefore the method presented in this article is considered to be a practical and effective way of dealing with this kind of problems.

关键词： hybrid models turbulence model scour multiple jets

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Modeling landslide susceptibility based on convolutional neural network coupling with metaheuristic optimization algorithms

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International Journal of Digital Earth 2023年第1期16卷 3384-3416页

作者： Zhuo Chen Danqing Song College of Civil Engineering Sichuan Agricultural UniversityDujiangyanPeople’s Republic of China Sichuan Higher Education Engineering Research Center for Disaster Prevention and Mitigation of Village Construction Sichuan Agricultural UniversityDujiangyanPeople’s Republic of China School of Civil Engineering and Transportation South China University of TechnologyGuangzhouPeople’s Republic of China State Key Laboratory of Subtropical Building Science South China University of TechnologyGuangzhouPeople’s Republic of China

Landslides are one of the most common geological hazards worldwide,especially in Sichuan Province(Southwest China).The current study's main,purposes are to explore the potential applications of convolutional neural networks(CNN)hybrid ensemble metaheuristic optimization algorithms,namely beluga whale optimization(BWO)and coati optimization algorithm(COA),for landslide susceptibility mapping in Sichuan Province(China).For this aim,fourteen landslide conditioning factors were compiled in a spatial *** effectiveness of the conditioning factors in the development of the landslide predictive model was quantified using the linear support vector machine *** receiver operating characteristic(ROC)curve(AUC),the root mean square error,and six statistical indices were used to test and compare the three resultant *** the training dataset,the AUC values of the CNN-COA,CNN-BWO and CNN models were 0.946,0.937 and 0.855,*** terms of the validation dataset,the CNN-COA model exhibited a higher AUC value of 0.919,while the AUC values of the CNN-BWO and CNN models were 0.906 and 0.805,*** results indicate that the CNN-COA model,followed by the CNN-BWO model,and the CNN model,offers the best overall performance for landslide susceptibility analysis.

关键词： Landslide susceptibility convolutional neural network beluga whale optimization coati optimization algorithm hybrid models Sichuan Province

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hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

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Computer Systems Science & Engineering 2022年第5期41卷 511-524页

作者： P.Rajeswari K.Jayashree Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennai602105India

Recent advancements in cloud computing(CC)technologies signified that several distinct web services are presently developed and exist at the cloud data ***,web service composition gains maximum attention among researchers due to its significance in real-time *** of Service(QoS)aware service composition concerned regarding the election of candidate services with the maximization of the whole *** these models have failed to handle the uncertainties of *** resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by *** the other hand,trip planning is an essential technique in supporting digital map *** aims to determine a set of location based services(LBS)which cover all client intended activities quantified in the *** the available web service composition solutions do not consider the complicated spatio-temporal *** resolving this issue,this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model(F3L-WSCM)in a cloud environment for location *** presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking,hotels,car rentals,*** the next stage,the firefly algorithm is applied to generate composition plans to minimize the number of composition *** by,the fuzzy subtractive clustering(FSC)will select the best composition plan from the available composite ***,the presented F3L-WSCM model involves four input QoS parameters namely service cost,service availability,service response time,and user *** extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy,execution time,and efficiency.

关键词： Web service composition trip planning hybrid models firefly algorithm QoS aware services fuzzy logic

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