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Sharafi M, Samadianfard S, Behmanesh J, Prasad R. Integration of fruit fly and firefly optimization algorithm with support vector regression in estimating daily pan evaporation. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024; 68:237-251. [PMID: 38060013 DOI: 10.1007/s00484-023-02586-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 10/29/2023] [Accepted: 11/19/2023] [Indexed: 12/08/2023]
Abstract
The purpose of the present study was to predict the pan evaporation values at four stations including Urmia, Makou, Mahabad, and Khoy, located in West Azerbaijan, Iran, using support vector regression (SVR), SVR coupled by fruit fly algorithm (SVR-FOA), and SVR coupled with firefly algorithm (SVR-FFA). Therefore, for the first time, this research has used the combined SVR-FOA to predict pan evaporation. For this purpose, meteorological parameters in the period of 1990-2020 were gathered and then using the Pearson's correlation coefficient, significant inputs for pan evaporation estimation were determined. The correlation evaluation of the parameters showed that the two parameters of wind speed and sunshine hours had the highest correlation with the pan evaporation values, and in addition, these parameters, as input to the models, improved the results and increased the accuracy of the models. The obtained results indicated that at Urmia station, SVR-FFA with the lowest error was the best model. The SVR-FOA also had better performance than the SVR model. Additionally, the result showed that SVR-FOA with the lowest errors had the best capability in pan evaporation estimation at other studied stations. Therefore, it was concluded that FOA with advantages such as simplicity, fewer parameters, easy adjustment, and less calculation can significantly increase the capability of independent SVR models. Hence, based on the overall results, SVR-FOA may be recommended as the most accurate method for pan evaporation estimation.
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Affiliation(s)
- Milad Sharafi
- Department of Water Engineering, Urmia University, Urmia, Iran
| | - Saeed Samadianfard
- Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
| | - Javad Behmanesh
- Department of Water Engineering, Urmia University, Urmia, Iran.
| | - Ramendra Prasad
- Department of Science, School of Science and Technology, The University of Fiji, Lautoka, Fiji
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Alavi Nezhad Khalil Abad S, Hazbeh O, Rajabi M, Tabasi S, Lajmorak S, Ghorbani H, Radwan AE, Mudabbir M. Determination of the Rate of Penetration by Robust Machine Learning Algorithms Based on Drilling Parameters. ACS OMEGA 2023; 8:46390-46398. [PMID: 38107947 PMCID: PMC10720015 DOI: 10.1021/acsomega.3c02364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 06/30/2023] [Indexed: 12/19/2023]
Abstract
Underground resources, particularly hydrocarbons, are critical assets that promote economic development on a global scale. Drilling activities are necessary for the extraction and recovery of subsurface energy resources, and the rate of penetration (ROP) is one of the most important drilling parameters. This study forecasts the ROP using drilling data from three Iranian wells and hybrid LSSVM-GA/PSO algorithms. These algorithms were chosen due to their ability to reduce noise and increase accuracy despite the high level of noise present in the data. The study results revealed that the LSSVM-PSO method has an accuracy of roughly 97% and is more precise than the LSSVM-GA technique. The LSSVM-PSO algorithm also demonstrated improved accuracy in test data, with RMSE = 1.92 and R2 = 0.9516. Furthermore, it was observed that the accuracy of the LSSVM-PSO model improves and degrades after the 50th iteration, whereas the accuracy of the LSSVM-GA algorithm remains constant after the 10th iteration. Notably, these algorithms are advantageous in decreasing data noise for drilling data.
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Affiliation(s)
| | - Omid Hazbeh
- Faculty
of Earth Sciences, Shahid Chamran University, Ahwaz 6135743136, Iran
| | - Meysam Rajabi
- Department
of Mining Engineering, Birjand University
of Technology, Birjand 97198 66981, Iran
| | - Somayeh Tabasi
- Faculty
of Industry and Mining (Khash), University
of Sistan and Baluchestan, Zahedan 1489684511, Iran
| | - Sahar Lajmorak
- Department
of Earth Sciences, Institute for Advanced
Studies in Basic Sciences (IASBS), 444 Prof. Yousef Sobouti Blvd., Zanjan 45137-66731, Iran
| | - Hamzeh Ghorbani
- Young
Researchers
and Elite Club, Ahvaz Branch, Islamic Azad
University, Ahvaz 1477893855, Iran
- Doctoral
School of Materials Science and TechnologiesObuda University, Budapest 1034, Hungary
| | - Ahmed E. Radwan
- Faculty
of Geography and Geology, Institute of Geological Sciences, Jagiellonian University, Gronostajowa 3a, Kraków 30-387, Poland
| | - Mohammad Mudabbir
- Doctoral
School of Materials Science and TechnologiesObuda University, Budapest 1034, Hungary
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Ranjan RK, Kumar V. A systematic review on fruit fly optimization algorithm and its applications. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10451-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Al-Masnay YA, Al-Areeq NM, Ullah K, Al-Aizari AR, Rahman M, Wang C, Zhang J, Liu X. Estimate earth fissure hazard based on machine learning in the Qa' Jahran Basin, Yemen. Sci Rep 2022; 12:21936. [PMID: 36536056 PMCID: PMC9763334 DOI: 10.1038/s41598-022-26526-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
Earth fissures are potential hazards that often cause severe damage and affect infrastructure, the environment, and socio-economic development. Owing to the complexity of the causes of earth fissures, the prediction of earth fissures remains a challenging task. In this study, we assess earth fissure hazard susceptibility mapping through four advanced machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), Naïve Bayes (NB), and K-nearest neighbor (KNN). Using Qa' Jahran Basin in Yemen as a case study area, 152 fissure locations were recorded via a field survey for the creation of an earth fissure inventory and 11 earth fissure conditioning factors, comprising of topographical, hydrological, geological, and environmental factors, were obtained from various data sources. The outputs of the models were compared and analyzed using statistical indices such as the confusion matrix, overall accuracy, and area under the receiver operating characteristics (AUROC) curve. The obtained results revealed that the RF algorithm, with an overall accuracy of 95.65% and AUROC, 0.99 showed excellent performance for generating hazard maps, followed by XGBoost, with an overall accuracy of 92.39% and AUROC of 0.98, the NB model, with overall accuracy, 88.43% and AUROC, 0.96, and KNN model with general accuracy, 80.43% and AUROC, 0.88), respectively. Such findings can assist land management planners, local authorities, and decision-makers in managing the present and future earth fissures to protect society and the ecosystem and implement suitable protection measures.
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Affiliation(s)
- Yousef A. Al-Masnay
- grid.27446.330000 0004 1789 9163Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun, 130024 People’s Republic of China ,grid.216417.70000 0001 0379 7164Department of Surveying and Remote Sensing, School of Geosciences and Info-Physics, Central South University, Changsha, 410083 China
| | - Nabil M. Al-Areeq
- grid.444928.70000 0000 9908 6529Department of Geology and Environment, Thamar University, Thamar, Yemen
| | - Kashif Ullah
- grid.503241.10000 0004 1760 9015Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, People’s Republic of China
| | - Ali R. Al-Aizari
- grid.33763.320000 0004 1761 2484Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, 300072 China
| | - Mahfuzur Rahman
- grid.443015.70000 0001 2222 8047Department of Civil Engineering, International University of Business Agriculture and Technology (IUBAT), Dhaka, 1230 Bangladesh
| | - Changcheng Wang
- grid.216417.70000 0001 0379 7164Department of Surveying and Remote Sensing, School of Geosciences and Info-Physics, Central South University, Changsha, 410083 China
| | - Jiquan Zhang
- grid.27446.330000 0004 1789 9163Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun, 130024 People’s Republic of China ,grid.27446.330000 0004 1789 9163Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun, 130024 People’s Republic of China ,grid.27446.330000 0004 1789 9163State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Northeast Normal University, Changchun, 130024 People’s Republic of China
| | - Xingpeng Liu
- grid.27446.330000 0004 1789 9163Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun, 130024 People’s Republic of China ,grid.27446.330000 0004 1789 9163Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun, 130024 People’s Republic of China ,grid.27446.330000 0004 1789 9163State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Northeast Normal University, Changchun, 130024 People’s Republic of China
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Malekpour MM, Malekpoor H. Reservoir water level forecasting using wavelet support vector regression (WSVR) based on teaching learning-based optimization algorithm (TLBO). Soft comput 2022. [DOI: 10.1007/s00500-022-07296-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Management of Landslides in a Rural–Urban Transition Zone Using Machine Learning Algorithms—A Case Study of a National Highway (NH-44), India, in the Rugged Himalayan Terrains. LAND 2022. [DOI: 10.3390/land11060884] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Landslides are critical natural disasters characterized by a downward movement of land masses. As one of the deadliest types of disasters worldwide, they have a high death toll every year and cause a large amount of economic damage. The transition between urban and rural areas is characterized by highways, which, in rugged Himalayan terrain, have to be constructed by cutting into the mountains, thereby destabilizing them and making them prone to landslides. This study was conducted landslide-prone regions of the entire Himalayan belt, i.e., National Highway NH-44 (the Jammu–Srinagar stretch). The main objectives of this study are to understand the causes behind the regular recurrence of the landslides in this region and propose a landslide early warning system (LEWS) based on the most suitable machine learning algorithms among the four selected, i.e., multiple linear regression, adaptive neuro-fuzzy inference system (ANFIS), random forest, and decision tree. It was found that ANFIS and random forest outperformed the other proposed methods with a substantial increase in overall accuracy. The LEWS model was developed using the land system parameters that govern landslide occurrence, such as rainfall, soil moisture, distance to the road and river, slope, land surface temperature (LST), and the built-up area (BUA) near the landslide site. The developed LEWS was validated using various statistical error assessment tools such as the root mean square error (RMSE), mean square error (MSE), confusion matrix, out-of-bag (OOB) error estimation, and area under the receiver operating characteristic (ROC) curve (AUC). The outcomes of this study can help to manage landslide hazards in the Himalayan urban–rural transition zones and serve as a sample study for similar mountainous regions of the world.
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Abstract
Computational methods in fluid research have been progressing during the past few years, driven by the incorporation of massive amounts of data, either in textual or graphical form, generated from multi-scale simulations, laboratory experiments, and real data from the field. Artificial Intelligence (AI) and its adjacent field, Machine Learning (ML), are about to reach standardization in most fields of computational science and engineering, as they provide multiple ways for extracting information from data that turn into knowledge, with the aid of portable software implementations that are easy to adopt. There is ample information on the historical and mathematical background of all aspects of AI/ML in the literature. Thus, this review article focuses mainly on their impact on fluid research at present, highlighting advances and opportunities, recognizing techniques and methods having been proposed, tabulating, and testing some of the most popular algorithms that have shown significant accuracy and performance on fluid applications. We also investigate algorithmic accuracy on several fluid datasets that correspond to simulation results for the transport properties of fluids and suggest that non-linear, decision tree-based methods have shown remarkable performance on reproducing fluid properties.
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Intelligent flow discharge computation in a rectangular channel with free overfall condition. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07112-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Condemi C, Casillas-Pérez D, Mastroeni L, Jiménez-Fernández S, Salcedo-Sanz S. Hydro-power production capacity prediction based on machine learning regression techniques. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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11
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Streamflow Forecasting via Two Types of Predictive Structure-Based Gated Recurrent Unit Models. WATER 2021. [DOI: 10.3390/w13010091] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Data-intelligent methods designed for forecasting the streamflow of the Fenhe River are crucial for enhancing water resource management. Herein, the gated recurrent unit (GRU) is coupled with the optimization algorithm improved grey wolf optimizer (IGWO) to design a hybrid model (IGWO-GRU) to carry out streamflow forecasting. Two types of predictive structure-based models (sequential IGWO-GRU and monthly IGWO-GRU) are compared with other models, such as the single least-squares support vector machine (LSSVM) and single extreme learning machine (ELM) models. These models incorporate the historical streamflow series as inputs of the model to forecast the future streamflow with data from January 1956 to December 2016 at the Shangjingyou station and from January 1958 to December 2016 at the Fenhe reservoir station. The IGWO-GRU model exhibited a strong ability for mapping in streamflow series when the parameters were carefully tuned. The monthly predictive structure can effectively extract the instinctive hydrological information that is more easily learned by the predictive model than the traditional sequential predictive structure. The monthly IGWO-GRU model was found to be a better forecasting tool, with an average qualification rate of 91.66% in two stations. It also showed good performance in absolute error and peak flow forecasting.
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12
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Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods. COATINGS 2020. [DOI: 10.3390/coatings10111100] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott’s index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature’s identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems.
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Mosavi A, Shokri M, Mansor Z, Qasem SN, Band SS, Mohammadzadeh A. Machine Learning for Modeling the Singular Multi-Pantograph Equations. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1041. [PMID: 33286810 PMCID: PMC7597098 DOI: 10.3390/e22091041] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 08/10/2020] [Accepted: 08/11/2020] [Indexed: 11/16/2022]
Abstract
In this study, a new approach to basis of intelligent systems and machine learning algorithms is introduced for solving singular multi-pantograph differential equations (SMDEs). For the first time, a type-2 fuzzy logic based approach is formulated to find an approximated solution. The rules of the suggested type-2 fuzzy logic system (T2-FLS) are optimized by the square root cubature Kalman filter (SCKF) such that the proposed fineness function to be minimized. Furthermore, the stability and boundedness of the estimation error is proved by novel approach on basis of Lyapunov theorem. The accuracy and robustness of the suggested algorithm is verified by several statistical examinations. It is shown that the suggested method results in an accurate solution with rapid convergence and a lower computational cost.
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Affiliation(s)
- Amirhosein Mosavi
- Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam;
- Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Manouchehr Shokri
- Faculty of Civil Engineering, Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, 99423 Weimar, Germany;
| | - Zulkefli Mansor
- Fakulti Teknologi dan Sains Maklumat, Universiti Kebangsan Malaysia, Bangi 43600, Selangor, Malaysia;
| | - Sultan Noman Qasem
- Computer Science Department, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia;
- Computer Science Department, Faculty of Applied Science, Taiz University, Taiz 6803, Yemen
| | - Shahab S. Band
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques. WATER 2020. [DOI: 10.3390/w12061528] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This paper presents long- and short-term analyses and predictions of dammed water level in a hydropower reservoir. The long-term analysis was carried out by using techniques such as detrended fluctuation analysis, auto-regressive models, and persistence-based algorithms. On the other hand, the short-term analysis of the dammed water level in the hydropower reservoir was modeled as a prediction problem, where machine learning regression techniques were studied. A set of models, including different types of neural networks, Support Vector regression, or Gaussian processes was tested. Real data from a hydropower reservoir located in Galicia, Spain, qwew considered, together with predictive variables from upstream measuring stations. We show that the techniques presented in this paper offer an excellent tool for the long- and short-term analysis and prediction of dammed water level in reservoirs for hydropower purposes, especially important for the management of water resources in areas with hydrology stress, such as Spain.
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Cao Y, Yin K, Zhou C, Ahmed B. Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis. SENSORS 2020; 20:s20030845. [PMID: 32033307 PMCID: PMC7038680 DOI: 10.3390/s20030845] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 01/29/2020] [Accepted: 02/03/2020] [Indexed: 11/18/2022]
Abstract
The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA.
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Affiliation(s)
- Ying Cao
- Faculty of Engineering, China University of Geosciences, Wuhan 430074, China;
| | - Kunlong Yin
- Faculty of Engineering, China University of Geosciences, Wuhan 430074, China;
- Correspondence: (K.Y.); (C.Z.)
| | - Chao Zhou
- School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
- Correspondence: (K.Y.); (C.Z.)
| | - Bayes Ahmed
- Institute for Risk and Disaster Reduction, University College London (UCL), Gower Street, London WC1E 6BT, UK;
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Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis. ATMOSPHERE 2020. [DOI: 10.3390/atmos11010066] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.
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A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis. SUSTAINABILITY 2019. [DOI: 10.3390/su12010320] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
With rapid advancements in internet applications, the growth rate of recommendation systems for tourists has skyrocketed. This has generated an enormous amount of travel-based data in the form of reviews, blogs, and ratings. However, most recommendation systems only recommend the top-rated places. Along with the top-ranked places, we aim to discover places that are often ignored by tourists owing to lack of promotion or effective advertising, referred to as under-emphasized locations. In this study, we use all relevant data, such as travel blogs, ratings, and reviews, in order to obtain optimal recommendations. We also aim to discover the latent factors that need to be addressed, such as food, cleanliness, and opening hours, and recommend a tourist place based on user history data. In this study, we propose a cross mapping table approach based on the location’s popularity, ratings, latent topics, and sentiments. An objective function for recommendation optimization is formulated based on these mappings. The baseline algorithms are latent Dirichlet allocation (LDA) and support vector machine (SVM). Our results show that the combined features of LDA, SVM, ratings, and cross mappings are conducive to enhanced performance. The main motivation of this study was to help tourist industries to direct more attention towards designing effective promotional activities for under-emphasized locations.
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Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement. MATHEMATICS 2019. [DOI: 10.3390/math7121198] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Prediction of the remaining service life (RSL) of pavement is a challenging task for road maintenance and transportation engineering. The prediction of the RSL estimates the time that a major repair or reconstruction becomes essential. The conventional approach to predict RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise operational safety. In this paper, surface distresses of pavement are used to estimate the RSL to address the aforementioned challenges. To implement the proposed theory, 105 flexible pavement segments are considered. For each pavement segment, the type, severity, and extent of surface damage and the pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include falling weight deflectometer (FWD) and ground-penetrating radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include support vector regression (SVR), support vector regression optimized by the fruit fly optimization algorithm (SVR-FOA), and gene expression programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The correlation coefficient (CC), Nash–Sutcliffe efficiency (NSE), scattered index (SI), and Willmott’s index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement.
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