免疫粒子群算法与支持向量机在枯水期月径流预测中的应用

针对支持向量机(SVM)最佳算法参数难以确定以及基本粒子群算法(PSO)易陷入局部极值等不足,提出免疫粒子群算法(IAPSO),利用IAPSO算法搜寻SVM学习参数,构建IAPSO-SVM预测模型,并与PSO-SVM、GA-SVM模型作为对比,以云南省某水文站枯水期月径流预测为例进行实例研究,利用实例前43年和后10年资料对模型进行训练和预测。结果表明:IAPSO-SVM模型对实例后10年枯水期1-3月月均径流预测的平均相对误差绝对值分别为3.32%、6.52%和6.55%,精度优于PSO-SVM和GA-SVM模型,表明IAPSO-SVM模型具有较高的预测精度和泛化能力。IAPSO算法利用浓度选择机制及免疫接种原理,改进了基本粒子群优化算法的全局寻优能力和收敛速度,具有较强的全局寻优能力。利用IAPSO算法优化得到的SVM学习参数可有效提高SVM模型的预测精度和泛化能力。     

Fuzzy Systems Tuned By Swarm Based Optimization Algorithms for Predicting Stream flow

River flow prediction is an important phenomenon in water resources for which different methods and perspective have been used. Using fuzzy system with black box perspective is one of them. Fuzzy systems have some parameters and properties that have to be determined. This is an optimization problem that can be solved by swarm optimization techniques among several techniques. Swarm optimization are developed by inspiring from the behavior of the animals living as swarm. The study presents two achievements fuzzy system that tuned by swarm optimization algorithms can be used for prediction of monthly mean streamflow and which swarm optimization algorithm is better than the others for tuning fuzzy systems. Three swarm optimization algorithms, hunter search, firefly, artificial bee colony are used in this study. These algorithms are compared with mean performance values and convergence speed. Monthly streamflow data of three stream gauging stations in Susurluk Basin are used for the case study. The results show, swarm optimization algorithms can be used for prediction of monthly mean streamflow and ABC algorithm has better performance values than other optimization algorithms.     

长江上游径流混沌动力特性及其集成预测研究

针对长江上游干流主要站点月径流时间序列强非线性和非平稳特征,引入混沌理论和AdaBoost.RT集成极限学习机方法对其月径流时间序列进行分析和预测。首先,以流域径流非线性动力系统混沌特征参数辨识为切入点,研究并发现了流域内在特性作用下月径流时间序列动力响应的混沌现象,推求了月径流时间序列相空间重构的延迟时间和最佳嵌入维数,在此基础上,以重构相空间时间序列作为输入变量,引入基于自适应动态阈值的改进AdaBoost.RT算法改进极限学习机模型的学习性能,得到最佳的混沌集成学习月径流时间序列预测模型。实例研究结果表明,所提方法和模型能够显著提高单一极限学习机模型的泛化性和稳定性,从而获得更优越的预报性能。     

基于蚁群算法优化极限学习机模型的滑坡位移预测

采用高精度的优化算法对于提高滑坡位移预测模型的准确性具有重要意义,然而已有文献中很少对多种优化算法进行对比研究。以三峡库区的八字门滑坡为例,以极限学习机（ELM）理论为基础进行滑坡位移预测,同时运用多种算法对建立模型过程中的参数选择进行优化以期提高预测效果。为提高预测精度,以移动平均法为基础,将滑坡位移分解为趋势项和周期项,趋势项位移使用多项式函数进行预测,周期项位移使用MATLAB自编程序的极限学习机模型进行预测,两项预测值相加即可得到最终的累计位移预测值。计算结果表明：单一的ELM模型能够较为准确地预测具有阶跃式曲线的滑坡累计位移,预测结果的平均误差为23.5 mm,拟合优度为0.973。与粒子群算法和遗传算法相比,蚁群算法（ACO）在计算用时和优化效果上更优,蚁群算法优化极限学习机模型对位移的预测精度也最高,平均误差为10.1 mm,拟合优度为0.998,可在类似滑坡的位移预测研究中进行推广。     

A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions

Prediction of long-term rainfall patterns is a highly challenging task in the hydrological field due to random nature of rainfall events. The contribution of monthly rainfall is important in agriculture and hydrological tasks. This paper proposes two data-driven models, namely biogeography-based extreme learning machine (BBO-ELM) and deep neural network (DNN), to predict one, two, and three month-ahead rainfall over India (All-India and six other homogeneous regions). Three other data-driven models called ELM, genetic algorithm (GA)-based ELM, and particle swarm optimization (PSO)-based ELM are used to compare the performance of the proposed models. Firstly, partial autocorrelation function (PACF) is applied in all datasets to select the optimal number of lags for input to the models. Secondly, the wavelet-based data pre-processing technique is applied in selected optimal lags and feed to the proposed models for achieving higher prediction performance. To investigate the performance of proposed models, a non-parametric statistical test, Anderson–Darling’ Normality test, is performed in all India dataset. The wavelet-based proposed hybrid models show better prediction capability compared to optimal lag-based proposed models. This study shows the successful application of time-series data using proposed techniques (optimal lags-based BBO-ELM and wavelet-based DNN) in the hydrological field which may be used for risk mitigation from dreadful natural events.

     

A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework

Some previous studies have proved that prediction models using traditional overall decomposition sampling (ODS) strategy are unreasonable because the subseries obtained by the ODS strategy contain future information to be predicted. It is, therefore, necessary to put forward a new sampling strategy to fix this defect and also to improve the accuracy and reliability of decomposition-based models. In this paper, a stepwise decomposition sampling (SDS) strategy according to the practical prediction process is introduced. Moreover, an innovative input selection framework is proposed to build a strong decomposition-based monthly streamflow prediction model, in which sunspots and atmospheric circulation anomaly factors are employed as candidate input variables to enhance the prediction accuracy of monthly streamflow in addition to regular inputs such as precipitation and evaporation. Meanwhile, the partial correlation algorithm is employed to select optimal input variables from candidate input variables including precipitation, evaporation, sunspots, and atmospheric circulation anomaly factors. Four basins of the U.S. MOPEX project with various climate characteristics were selected as a case study. Results indicate that: (1) adding teleconnection factors into candidate input variables helps enhance the prediction accuracy of the support vector machine (SVM) model in predicting streamflow; (2) the innovative input selection framework helps to improve the prediction capacity of models whose candidate input variables interact with each other compared with traditional selection strategy; (3) the SDS strategy can effectively prevent future information from being included into input variables, which is an appropriate substitute of the ODS strategy in developing prediction models; (4) as for monthly streamflow, the hybrid variable model decomposition-support vector machine (VMD-SVM) models, using an innovative input selection framework and the SDS strategy, perform better than those which have not adopted this framework in all study areas. Generally, the findings of this study showed that the hybrid VMD-SVM model combining the SDS strategy and innovative input selection framework is a useful and powerful tool for practical hydrological prediction work in the context of climate change.

     

Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting

Input variable selection plays a key role in data-driven streamflow forecasting models. In this study, we propose a two-stage wrapper model to drive one-month-ahead streamflow forecasting in the context of high-dimensional candidate input variables. Initially, the Boruta algorithm, a feature selection method, was applied to select all the relevant input variables for the streamflow series. Then, a novel binary grey wolf optimizer (BGWO)-regularized extreme learning machine (RELM) wrapper was derived. We carried out experiments on two US catchments with 132 candidate input variables, including local meteorological information, global climatic indices, and lags of the streamflow series. Furthermore, the sensitivities of the proposed model in terms of the optimal objective function were compared. The results indicate two important findings. First, the proposed model outperformed commonly used models in terms of four error evaluation criteria. Second, for the proposed model, the root mean square error is a more suitable criterion than the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for the optimal objective function. These findings are of great reference value for developing ELM models for streamflow forecasting.

     

Monthly Streamflow Forecasting Using Convolutional Neural Network

Monthly streamflow forecasting is vital for managing water resources. Recently, numerous studies have explored and evidenced the potential of artificial intelligence (AI) models in hydrological forecasting. In this study, the feasibility of the convolutional neural network (CNN), a deep learning method, is explored for monthly streamflow forecasting. CNN can automatically extract critical features from numerous inputs with its convolution–pooling mechanism, which is a distinct advantage compared with other AI models. Hydrological and large-scale atmospheric circulation variables, including rainfall, streamflow, and atmospheric circulation factors are used to establish models and forecast streamflow for Huanren Reservoir and Xiangjiaba Hydropower Station, China. The artificial neural network (ANN) and extreme learning machine (ELM) with inputs identified based on cross-correlation and mutual information analyses are established for comparative analyses. The performances of these models are assessed with several statistical metrics and graphical evaluation methods. The results show that CNN outperforms ANN and ELM in all statistical measures. Moreover, CNN shows better stability in forecasting accuracy.

     

基于改进PSO-RF算法的大坝变形预测模型

针对传统随机森林参数寻优方法的不足,引入均衡惯性权重和自适应变异对粒子群优化算法进行改进,提出了一种基于改进粒子群优化算法和随机森林算法(改进PSO-RF算法)的大坝变形预测模型。实例验证结果表明,在计算效率方面,与传统网格搜索法相比,改进PSO-RF算法显著提升了模型的寻优速度;在预测精度和稳定性方面,基于改进PSO-RF算法的大坝变形预测模型明显优于长短期记忆网络、支持向量机和BP神经网络模型。     

基于PSO-SVM的大坝渗流监测时间序列非线性预报模型

大坝渗流监测分析是大坝安全监控的重要内容，预测分析的难点之一在于渗流监测数据往往具有复杂的非线性特点。本文充分利用支持向量机的结构风险最小化与粒子群算法快速全局优化的特点，采用粒子群算法快速优化支持向量机的模型参数，通过该模型对非线性监测数据进行拟合，建立了基于PSO_SVM的大坝渗流监测的时间序列非线性预报模型。本模型应用于隔河岩水电站的坝基渗流量的预测，计算结果与实际监测值吻合良好。     

基于MF-DFA法和PSO-ELM模型的基坑变形规律研究

为准确掌握基坑变形的发展趋势,实现对基坑施工的准确指导,针对基坑变形序列的非线性和复杂性,提出利用MF-DFA法和PSO-ELM模型对基坑的变形规律进行研究。首先,利用MF-DFA法对基坑变形速率序列进行多重分形特征分析,以判断基坑的变形趋势;其次,利用PSO-ELM模型对基坑累计变形序列进行预测,得到基坑变形的预测值;最后,对比两变形序列的分析结果,综合判断基坑的变形趋势。同时,采用实例检验分析思路的准确性。结果表明:MF-DFA法能有效分析基坑变形速率序列的多重分形特征, PSO-ELM模型在基坑变形预测中也具有较高的预测精度,且两者对基坑变形规律的判断的一致性较好,相互佐证了两者分析结果的准确性,为基坑变形规律研究提供了一种新的思路。     

面向EMD分解的径流分量重构方法对比研究

水文序列非平稳与非线性的复杂变化导致水文序列中长期预测的准确性备受质疑。"分解-预测-重构"模式作为一种新的有效的预测思路近年来备受业界和学者关注。但受到高频分量预测误差大、趋势走向不确定等问题困扰,这种模式在发展过程中仍有诸多需要改进的地方。其中,径流分量的重构方法是控制高频分量误差,提高整体预测精度的关键性措施,其优劣对预测效果实现有着重要的意义。基于经验模态分解(EMD)和自回归模型(AR)建立"分解-预测"耦合模型,结合粒子群优化(PSO)算法,提出PSO重构系数优化法和高频分量剔除+重构系数优化法两种重构方法,结合前人提出的高频分量剔除法,以陕北丁家沟站、关中华县站、陕南白河站为算例,对不同重构方法的效果进行对比研究。研究结果表明:基于高频分量剔除法、PSO重构系数优化法、高频分量剔除+重构系数优化法三种重构方法的预测效果均较好,五项误差评价指标均优于标准重构法,三种重构方法均可不同程度地提高预测精度。对比研究发现:高频分量剔除法在重构过程中剔除了最不稳定且最难预测的高频分量,提高了预测精度,但提升效果有限;PSO重构系数优化法对所有径流分量赋予优化重构系数并重构,可最大程度地实现分量间的平差,有效提高了预测精度;高频分量剔除+重构系数优化法综合上述两种方法的优势,取得了比其他方法更好的预测效果。     

基于鸡群算法优化相关向量机的混凝土坝变形预报模型

针对混凝土坝变形监控模型中大坝变形与环境影响因素之间的复杂非线性问题,为提升大坝变形监控模型的预报能力,提出了一种基于鸡群算法(CSO)优化相关向量机(RVM)的混凝土坝变形预报模型。考虑到相关向量机核函数参数的选取直接影响其回归分析性能,采用鸡群算法对其核函数参数进行寻优处理。据此,构造了基于鸡群算法优化的相关向量机模型,进而提升相关向量机的预报精度和泛化能力。以某混凝土坝长期变形监测资料分析表明,基于鸡群算法优化的相关向量机模型预报可有效挖掘大坝变形与环境因素间复杂的非线性函数关系,相比传统的相关向量机模型,该模型的拟合与预报精度更优,有效验证了所提方法的合理性与有效性,为大坝变形分析与预测提供新的模型方法。     

基于两阶段粒子群优化算法的新型逐步分解集成径流预测模型

传统分解集成径流预测模型首先将整个径流序列分解成若干个子序列,再将这些子序列划分为训练期和验证期进行建模,错误地将验证期内预报因子数据视作已知数据处理,难以应用于实际径流预报工作中。并且,这类模型的预测结果仅为一个确定数值,难以描述由于径流序列随机性和波动性而导致的预测不确定性。为解决以上问题,本文结合变分模态分解方法、支持向量机模型和核密度估计方法,提出了一种可同时进行点预测和区间预测的新型逐步分解集成(VMD-SVM-KDE)模型,并提出了一种两阶段粒子群优化(TSCPSO)算法来优化模型参数。选用黄河流域月径流数据评估模型性能,研究结果表明:(1)VMD-SVM-KDE模型将单一SVM-KDE模型的确定系数(R²)和纳什效率系数(NSE)值由0.145~0.630提升至0.872~0.921,区间平均偏差(INAD)值由0.046~95.844降低至0.005~0.034,说明VMD-SVM-KDE模型显著改进了单一SVM-KDE模型的点预测和区间预测性能;(2)相较于一阶段PSO算法,TSCPSO优化算法将单一模型的R²和NSE值由0.145~0.480提升至0.309~0.630,INAD值由48.813~95.844降低至0.046~0.195,将分解集成模型的R²和NSE值由0.872~0.912提升至0.876~0.921,INAD值由0.007~0.034降低至0.005~0.014,说明TSCPSO优化算法可以克服SVM的过拟合问题,并能提高单一模型和分解集成模型的预测精度;(3)VMD-SVM-KDE-TSCPSO有效解决了传统分解集成预测模型存在的错误使用验证期内预报因子数据的问题,并在各站的R²和NSE值均约为0.9,INAD值的范围为0.005~0.014,具有更高的点预测和区间预测精度。文中模型可为优化径流预测模型和非平稳非线性水文序列预报提供新思路。     

基于PSO-SVM的碾压混凝土坝变形预测模型

为提升大坝变形预测能力,提出了一种基于粒子群算法(PSO)优化支持向量机(SVM)的混凝土重力坝变形预测模型.通过粒子群算法对支持向量机惩罚函数C与核函数σ进行寻优,避免了拟合过程中易陷入局部最优解的问题,提高了模型的拟合精度.以新疆北疆某碾压混凝土坝2014年～ 2019年变形监测数据为例,建立了逐步回归、SVM、P...     

鸡群优化算法-投影寻踪洪旱灾害评估模型

利用15个复杂函数对鸡群优化算法进行仿真验证,并同狼群算法、粒子群算法、鱼群算法和遗传算法进行对比。利用鸡群优化算法搜寻投影寻踪模型最佳投影方向,建立鸡群优化算法-投影寻踪洪旱灾害评估模型。以文山州1990—2013年洪旱灾害评估为例,分别选取受灾人口等5个洪灾评估指标及农作物受灾面积等4个旱灾评估指标,利用洪旱灾害投影系列均值及标准差构造洪旱灾害评估分级标准对实例进行评估。结果表明:鸡群优化算法具有较好的计算鲁棒性和全局寻优能力。将该算法用于投影寻踪模型最佳投影方向的选取,可有效提高评估精度,避免最佳投影方向寻优结果变化范围过大的缺陷。     

混沌粒子群优化算法在马斯京根模型参数优化中的应用

针对目前马斯京根模型参数率定中存在的求解复杂、精度不高等问题,本文将混沌搜索机制引入粒子群优化算法中,构建混沌粒子群优化算法对马斯京根模型参数进行率定。这种方法利用混沌运动的遍历性,改善了粒子群优化算法的全局寻优能力,避免算法陷入局部极值,使得粒子群体的进化速度加快,提高了算法的收敛速度和精度。通过实例应用表明,混沌粒子群优化算法可以有效地估算出马斯京根模型参数,优化效果明显优于粒子群优化算法及试错法,因此该算法具有很好的实用性。     

基于聚集度自适应反向学习粒子群算法在水库优化调度中的应用

为高效、快速求解水库优化调度问题,提出基于聚集度自适应反向学习粒子群算法。此算法首先采用聚集度策略分析种群的聚散状态,并在此基础上,提出自适应反向学习策略,生成种群中心的反向解参与进化,引导种群改变聚散状态,进一步平衡算法的勘探与开发能力。将基于聚集度自适应反向学习粒子群算法与经典的和最新的高水平粒子群算法进行比较,在所测的基准函数中,本算法在5个基准函数上都取得最优解,验证了其对连续变量函数的优化能力强于所对比算法。在求解水布垭、隔河岩和高坝洲梯级水库优化调度问题上,本算法求得总发电量为86.335 71×10~8 kW·h,求解所需时间为721 ms,相较所对比算法的调度结果,总发电量最大提高了11.860 2×10~8 kW·h,所需计算时间最大降低了21 380 ms,由此验证了基于聚集度自适应反向学习粒子群算法对水库优化问题的可行性。     

基于EMD分解法的大坝变形预测模型及应用

针对传统模型对脉动时间序列的预测效果较差的情况,结合经验模态分解(EMD)、相关向量机(RVM)理论以及改进粒子群算法(IPSO)的优点,提出一种基于EMD分解法的大坝变形预测模型。首先利用EMD分解法对大坝变形时间序列进行分解和重构,使非平稳的大坝变形时间序列平稳化,再以RVM理论为基础进行预测,核函数选用高斯核函数,并采用改进粒子群算法(IPSO)进行寻优,最终建立EMD-RVM(IPSO)大坝变形预测模型。通过实例计算得到,SVM、RVM和EMDRVM(IPSO)三种模型的平均残差分别为5.29 mm、3.13 mm、0.97 mm,并且EMD-RVM(IPSO)模型的预测值误差均控制在5%以内。这证明EMD分解法对非平稳时间序列的预处理可有效提高预测精度,相比于标准SVM模型和RVM模型,EMD-RVM(IPSO)模型的预测精度更高,且结构稀疏度更好,在实际工程中具有一定的可行性。     

异构多种群粒子群优化算法在水位流量关系拟合中的应用

通过8个复杂函数对一种异构多种群粒子群优化算法进行仿真验证,并与传统单种群粒子群优化算法进行对比。针对水位流量关系拟合中相关参数难以确定的不足,利用异构多种群粒子群优化算法优化水位流量关系相关参数,以云南省龙潭站、西洋站水位流量关系拟合为例进行实例研究,并与粒子群优化算法、最小二乘法拟合结果进行对比。结果表明:异构多种群粒子群优化算法收敛精度远远优于粒子群优化算法,具有较好的计算鲁棒性和全局寻优能力。该算法对龙潭站和西洋站水位流量关系拟合的平均相对误差绝对值分别仅为0.27%和0.50%,拟合精度优于粒子群优化算法和最小二乘法。利用异构多种群粒子群优化算法优化水位流量关系可以获得更好的拟合效果。