The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR). According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO), which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability

Crop Yield Prediction by Hybrid Technique with Crop Datasets

Agriculture is one of the intense domains across the globe which has greater impact on the development of a country.  There are various tools and techniques developed for the farmers and they are taking advantages of it. Also, the power of artificial intelligence is realized in agriculture field with the application of machine learning and deep learning algorithms. Numerous models have been proposed using the conventional algorithms, but still it is needed to improve the prediction accuracy. Therefore, in the proposed model a hybrid technique is designed by combining the Machine learning, deep learning algorithms and optimization with particle swarm optimization PSO methods to improve the prediction accuracy. In the proposed model, SVM is used as Machine leaning algorithm and RNN-LSTM is used as deep learning algorithm. The crop data sets of Maharashtra for previous years are used as input to the model and prediction will be done for the coming years. The proposed model has potential in improving the yield prediction for various crops like onion, grapes, cotton etc. produced in the Maharashtra State of India

Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms

Rainfall-runoff model requires comprehensive computation as its relation is a complex natural phenomenon. Various inter-related processes are involved with factors such as rainfall intensity, geomorphology, climatic and landscape are all affecting runoff response. In general there is no single rainfallrunoff model that can cater to all flood prediction system with varying topological area. Hence, there is a vital need to have custom-tailored prediction model with specific range of data, type of perimeter and antecedent hour of prediction to meet the necessity of the locality. In an attempt to model a reliable rainfall-runoff system for a flood-prone area in Malaysia, 3 different approach of Artificial Neural Networks (ANN) are modelled based on the data acquired from Sungai Pahang, Pekan. In this paper, the ANN rainfall-runoff models are trained by the Levenberg Marquardt (LM), Bayesian Regularization (BR) and Particle Swarm Optimization (PSO). The performances of the learning algorithms are compared and evaluated based on a 12-hour prediction model. The results demonstrate that LM produces the best model. It outperforms BR and PSO in terms of convergence rate, lowest mean square error (MSE) and optimum coefficeint of correlation. Furthermore, the LM approach are free from overfitting, which is a crucial concern in conventional ANN learning algorithm. Our case study takes the data of rainfall and runoff from the year 2012 to 2014. This is a case study in Pahang river basin, Pekan, Malaysia

Time Series Predictive Analysis based on Hybridization of Meta-heuristic Algorithms

This paper presents a comparative study which involved five hybrid meta-heuristic methods to predict the weather five days in advance. The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning technique namely Least Squares Support Vector Machines (LS-SVM). For experimental purposes, a total of 6 independent inputs are considered which were collected based on daily weather data. The efficiency of the MFO-LSSVM, CS-LSSVM, ABC-LSSVM, FA-LSSVM, and DE-LSSVM was quantitatively analyzed based on Theil’s U and Root Mean Square Percentage Error. Overall, the experimental results demonstrate a good rival among the identified methods. However, the superiority goes to FA-LSSVM which was able to record lower error rates in prediction. The proposed prediction model could benefit many parties in continuity planning daily activities

메타 휴리스틱 최적화를 이용한 기계학습 기반 선박 건조 공정 리드 타임 예측

학위논문(석사) -- 서울대학교대학원 : 공과대학 조선해양공학과, 2021.8. 하오유주.In the shipbuilding industry, each production process has a respective lead time; that is, the duration between start and finish times. Lead time is basic data that is necessary for high-efficiency production planning and systematic production management. Therefore, lead time must be accurate. However, the traditional method of lead time management is not scientific because it mostly makes the plan by calculating the average lead times derived from historical data. Therefore, to understand the complex relationship between lead time and other influencing factors, this study proposes to use machine learning (ML) algorithms, support vector machine (SVM) and artificial neural network (ANN), which are frequently applied in prediction fields. Moreover, to improve prediction accuracy, this study proposes to apply meta-heuristic algorithms to optimize the parameters of the ML models. This thesis builds hybrid models, including meta-heuristic-ANN, meta-heuristic-SVM models. In addition, this study compares model’s performance with each other. In searching for the ML model’s parameters, the results point out that the new self-organizing hierarchical particle swarm optimization (PSO) with jumping time-varying acceleration coefficients (NHPSO-JTVAC) algorithm is superior in terms of performance. More importantly, the test results demonstrate that the integrated models, based on NHPSO-JTVAC, have the smallest mean absolute percentage error (MAPE) test error in the three shipyard block process data sets, 11.79%, 16.03% and 16.45%, respectively. The results also demonstrate that the built models based on NHPSO-JTVAC can achieve further meaningful enhancements in terms of prediction accuracy. Overall, the NHPSO–JTVAC-SVM, NHPSO–JTVAC-ANN models are feasible for predicting the lead time in shipbuilding.조선 산업에서 각 공정은 리드 타임을 가진다. 리드 타임이란 공정 시작과 종료 간에 시간으로, 고효율의 생산계획과 체계적 생산관리를 위해 매우 중요한 지표이다. 특히, 생산 계획 단계에서 정확한 리드타임 예측은 납기 준수를 위한 계획 수립을 위해 매우 중요하다. 그러나 기존의 예측 방법은 과거 데이터의 평균값을 사용했기 때문에 정확도가 매우 떨어졌다. 따라서 본 연구에서는 리드 타임과 다른 영향 요인 간의 복잡한 관계를 이해하기 위해 예측 분야에서 자주 적용되는 머신 러닝 (ML) 모델인 서포트 벡터 머신 (SVM) 및 인공 신경망 (ANN) 적용을 제안한다. 또한, 기계학습 모델 예측 정확도를 향상시키기 위해 메타 휴리스틱 알고리즘을 적용하여 모델의 파라미터를 최적화하고자 한다. 본 연구는 meta-heuristics-ANN, meta-heuristics-SVM 모델을 포함하는 하이브리드 모델을 구축한다. 더불어, 본 연구는 메타 휴리스틱 알고리즘 기반으로 최적화된 기계학습 모델의 성능을 서로 비교한다. 연구 결과를 통해, ML 모델의 파라미터를 탐색하는 과정에서 particle swam optimization (PSO)의 enhanced 버전인 NHPSO-JTVAC 알고리즘이 탐색 성능 면에서 다른 알고리즘보다 우수하다는 것을 알 수 있다. 뿐만 아니라 테스트 결과를 살펴보면 NHPSO-JTVAC에 기반한 하이브리드 모델이 조선소 세 개의 블록 공정 데이터에서 (각각 11.79%, 16.03% 및 16.45%) 가장 작은 MAPE 테스트 오차임을 알 수 있다. 이것은 NHPSO-JTVAC를 기반으로 구축된 모델이 예측 정확도 측면에서 의미 있는 향상을 더 달성할 수 있음을 보여준다. 전반적으로 NHPSO-JTVAC-SVM, NHPSO-JTVAC-ANN 모델은 조선소 블록 공정의 리드 타임을 예측하는 데 적합하다는 것을 확인할 수 있다.Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Related Works 3 1.2.1 Related Works for Lead Time Prediction 3 1.2.2 Related Works for Hybrid Predictive Model 4 1.3 Thesis Organization 6 Chapter 2 Machine Learning 7 2.1 Support Vector Machine 7 2.1.1 Support Vector Machine Algorithm 7 2.1.2 Hyperparameter Optimization for SVM 10 2.2 Artificial Neural Network 11 2.2.1 Artificial Neural Network Algorithm 11 2.2.2 Hyperparameter Optimization for ANN 15 Chapter 3 Meta-heuristic Optimization Algorithms 17 3.1 Particle Swarm Optimization 17 3.2 NHPSO-JTVAC: An Advanced Version of PSO 18 3.3 Bat Algorithm 19 3.4 Firefly Algorithm 21 3.5 Grasshopper Optimization Algorithm 22 3.6 Moth Search Algorithm 24 Chapter 4 Hybrid Artificial Intelligence Models 27 4.1 Hybrid Meta-heuristic-SVM Models 27 4.1.1 Hybrid PSO-SVM Model 29 4.1.2 Hybrid NHPSO-JTVAC-SVM Model 30 4.1.3 Hybrid BA-SVM Model 31 4.1.4 Hybrid FA-SVM Model 33 4.1.5 Hybrid GOA-SVM Model 34 4.1.6 Hybrid MSA-SVM Model 35 4.2 Hybrid Meta-heuristic-ANN Models 36 4.2.1 Hybrid PSO-ANN Model 38 4.2.2 Hybrid NHPSO-JTVAC-ANN Model 39 4.2.3 Hybrid BA-ANN Model 40 4.2.4 Hybrid FA-ANN Model 41 4.2.5 Hybrid GOA-ANN Model 42 4.2.6 Hybrid MSA-ANN Model 43 Chapter 5 Lead Time Prediction Based on Hybrid AI Models 44 5.1 Data and Preparation 44 5.1.1 Data Normalization 45 5.1.2 Feature Selection 45 5.2 Lead Time Prediction 46 5.3 Performance Metrics 47 Chapter 6 Experimental Results 49 6.1 Results Based on Hybrid SVM-based Models 49 6.2 Results Based on Hybrid ANN-based Models 55 6.3 Overall Results 60 Chapter 7 Conclusions and Future Works 62 Bibliography 63 Appendix A 68 Abstract in Korean 69석

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems

Large scale integration of renewable energy system with classical electrical power generation system requires a precise balance to maintain and optimize the supply–demand limitations in power grids operations. For this purpose, accurate forecasting is needed from wind energy conversion systems (WECS) and solar power plants (SPPs). This daunting task has limits with long-short term and precise term forecasting due to the highly random nature of environmental conditions. This paper offers a hybrid variational decomposition model (HVDM) as a revolutionary composite deep learning-based evolutionary technique for accurate power production forecasting in microgrid farms. The objective is to obtain precise short-term forecasting in five steps of development. An improvised dynamic group-based cooperative search (IDGC) mechanism with a IDGC-Radial Basis Function Neural Network (IDGC-RBFNN) is proposed for enhanced accurate short-term power forecasting. For this purpose, meteorological data with time series is utilized. SCADA data provide the values to the system. The improvisation has been made to the metaheuristic algorithm and an enhanced training mechanism is designed for the short term wind forecasting (STWF) problem. The results are compared with two different Neural Network topologies and three heuristic algorithms: particle swarm intelligence (PSO), IDGC, and dynamic group cooperation optimization (DGCO). The 24 h ahead are studied in the experimental simulations. The analysis is made using seasonal behavior for year-round performance analysis. The prediction accuracy achieved by the proposed hybrid model shows greater results. The comparison is made statistically with existing works and literature showing highly effective accuracy at a lower computational burden. Three seasonal results are compared graphically and statistically.publishedVersio

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features