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Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution

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JOURNAL OF HYDROLOGY 2016年 534卷 104-112页

作者： Kisi, Ozgur Parmar, Kulwinder Singh Canik Basari Univ Fac Engn & Architecture Dept Civil Engn TR-55080 Samsun Turkey Dr BR Ambedkar Natl Inst Technol Dept Math Jalandhar India

This study investigates the accuracy of least square support vector machine (LSSVm), multivariate adaptive regression splines (mARS) and m5 model tree (m5tree) in modeling river water pollution. Various combinations of water quality parameters, Free Ammonia (Amm), Total Kjeldahl Nitrogen (TKN), Water Temperature (WT), Total Coliform (TC), Fecal Coliform (FC) and Potential of Hydrogen (pH) monitored at Nizamuddin, Delhi Yamuna River in India were used as inputs to the applied models. Results indicated that the LSSVm and mARS models had almost same accuracy and they performed better than the m5tree model in modeling monthly chemical oxygen demand (COD). The average root mean square error (RmSE) of the LSSVm and m5tree models was decreased by 1.47% and 19.1% using mARS model, respectively. Adding TC input to the models did not increase their accuracy in modeling COD while adding FC and pH inputs to the models generally decreased the accuracy. The overall results indicated that the mARS and LSSVm models could be successfully used in estimating monthly river water pollution level by using Amm, TKN and WT parameters as inputs. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Chemical oxygen demand (COD) mathematical modeling Estimation Least square support vector machine multivariate adaptive regression splines m5 model tree

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Pan evaporation modeling using four different heuristic approaches

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COmPUTERS AND ELECTRONICS IN AGRICULTURE 2017年 140卷 203-213页

作者： Wang, Lunche Niu, Zigeng Kisi, Ozgur Li, Chang'an Yu, Deqing China Univ Geosci Sch Earth Sci Lab Crit Zone Evolut Lumo Rd 388 Wuhan 430074 Hubei Peoples R China Ilia State Univ Sch Nat Sci & Engn Tbilisi Georgia Remote Sensing Ctr Hunan Prov Changsha 410007 Hunan Peoples R China

Evaporation plays important roles in regional water resources management, climate change and agricultural production. This study investigates the abilities of fuzzy genetic (FG), least square support vector regression (LSSVR), multivariate adaptive regression spline (mARS), m5 model tree (m5tree) and multiple linear regression (mLR) in estimating daily pan evaporation (Ep). Daily climatic data, air temperature (Ta), surface temperature (Ts), wind speed (Ws), relative humidity (RH) and sunshine hours (Hs) at eight stations in the Dongting Lake Basin, China are used for model development and validation. The first part of this study focuses on testing the model accuracies at each station using local input and output data. The results show that LSSVR and FG models with more input variables perform better than the mARS, m5tree and mLR models in predicting daily Ep at most stations with respect to mean absolute errors (mAE), root mean square errors (RmSE) and determination coefficient (R-2). In the second part of this study, the models are tested using cross-validation method in two different applications. The daily Ep of Yueyang station is estimated using the input and output data of Jingzhou and Changsha, respectively. Comparisons of the models indicate that the FG, LSSVR and mARS models outperform the m5tree model, Ts, Hs and Ta are major influencing factors and adding Ws or RH into model inputs significantly improve the model performances. The overall results indicate that above models can be successfully used for estimating daily Ep using local input and output data while the FG and LSSVR generally perform better than the other models without local input and outputs. 2017 Elsevier B.V. All rights reserved.

关键词： Fuzzy genetic Least square support vector regression m5 model tree multivariate adaptive regression spline Dongting Lake Evaporation estimation

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multi-Step-Ahead Rainfall-Runoff modeling: Decision tree-Based Clustering for Hybrid Wavelet Neural- Networks modeling

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WATER RESOURCES mANAGEmENT 2024年第13期38卷 5195-5214页

作者： molajou, Amir Nourani, Vahid Tajbakhsh, Ali Davanlou Variani, Hossein Akbari Khosravi, mina Iran Univ Sci & Technol Fac Civil Engn Dept Water Resources Engn Tehran Iran Univ Tabriz Fac Civil Engn Dept Water Resources Engn Tabriz Iran Khajeh Nasir al Din Toosi Univ Technol Fac Civil Engn Dept Water Resources Engn Tehran Iran Iran Univ Sci & Technol Fac Civil Engn Dept Water Resources Engn Tehran Iran Univ Tabriz Ctr Excellence Hydroinformat Tabriz Iran Univ Tabriz Fac Civil Engn Tabriz Iran World Peace Univ Dept Civil Engn Lefkosa Iran

This paper introduces a novel hybrid approach for predicting the rainfall-runoff (r-r) phenomenon across different data division scenarios (50%-50%, 60%-40%, and 75%-25%) within two distinct watersheds, encompassing both monthly and daily scales. Additionally, the effectiveness of this newly proposed hybrid method is evaluated in multi-step ahead prediction (mSAP) scenarios. The proposed method comprises three primary steps. Initially, to address the non-stationarity of the runoff and rainfall time series, these series are decomposed into multiple sub-time series using the wavelet (WT) decomposition method. Subsequently, in the second step, the decomposed sub-series are utilized as input data for the m5 model tree, a decision tree-based model. The m5 model tree classifies the samples of decomposed runoff and rainfall time series into distinct classes. Finally, each class is modeled using an artificial neural network (ANN). The results demonstrate the superior efficiency of the proposed WT-m5-ANN method compared to other available hybrid methods. Specifically, the calculated R2 was 0.93 for the proposed WT-m5-ANN method, whereas it was 0.89 and 0.81 for the WT-ANN (WANN) and WT-m5 methods, respectively, for the Lobbs Hole Creek watershed at the daily scale.

关键词： Artificial neural network multi-step ahead predicting m5 model tree Rainfall-runoff modeling Wavelet transform

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Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting

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APPLIED ENERGY 2018年 217卷 422-439页

作者： AL-musaylh, mohanad S. Deo, Ravinesh C. Li, Yan Adamowski, Jan F. Univ Southern Queensland Inst Agr & Environm IAg&E Sch Agr Computat & Environm Sci Toowoomba Qld 4350 Australia Southern Tech Univ Management Tech Coll Basrah Iraq McGill Univ Fac Agr & Environm Sci Dept Bioresource Engn Quebec City PQ H9X 3V9 Canada

Real-time energy management systems that are designed to support consumer supply and demand spectrums of electrical energy continue to face challenges with respect to designing accurate and reliable real-time forecasts due to the stochasticity of model construction data and the model's inability to disseminate both the short- and the long-term electrical energy demand (G) predictions. Using real G data from Queensland, Australia's second largest state, and employing the support vector regression (SVR) model integrated with an improved version of empirical mode decomposition with adaptive noise (ICEEmDAN) tool, this study aims to propose a novel hybrid model: ICEEm-DAN-PSO-SVR. Optimization of the model's weights and biases was performed using the particle swarm optimization (PSO) algorithm. ICEEmDAN was applied to improve the hybrid model's forecasting accuracy, addressing non-linear and non-stationary issues in time series inputs by decomposing statistically significant historical G data into intrinsic mode functions (ImF) and a residual component. The ICEEmDAN-PSO-SVR model was then individually constructed to forecast ImIs and the residual datasets and the final G forecasts were obtained by aggregating the ImF and residual forecasted series. The performance of the ICEEmDAN-PSO-SVR technique was compared with alternative approaches: ICEEmDAN-multivariate adaptive regression spline (mARS) and ICEEm-DAN-m5 model tree, as well as traditional modelling approaches: PSO-SVR, mARS and m5 model tree algorithms. To develop the models, data were partitioned into different subsets: training (70%), validation (15%), and testing (15%), and the tuned forecasting models with near global optimum solutions were applied and evaluated at multiple horizons: short-term (i.e., weekends, working days, whole weeks, and public holidays), and long-term (monthly). Statistical metrics including the root-mean square error (RmSE), mean absolute error (mAE) and their relative to observed means

关键词： SVR PSO Improved CEEmDAN Electricity demand mARS m5 model tree Energy management system

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Run-up on vertical piles due to regular waves: Small-scale model tests and prediction formulae

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COASTAL ENGINEERING 2016年 118卷 1-11页

作者： Bonakdar, Lisham Oumeraci, Hocine Etemad-Shahidi, Amir Tech Univ Carolo Wilhelmina Braunschweig Leichtweiss Inst Hydraul Engn & Water Resources Beethovenstr 51a D-38106 Braunschweig Germany Griffith Univ Griffith Sch Engn Gold Coast Campus Gold Coast Qld 4222 Australia

In wave-structure interaction, one of the most important phenomena clearly identified is wave run-up on offshore structures. In this study, wave run-up on a slender pile due to non-breaking regular waves is investigated by means of small-scale experiments performed in the 2 m-wide wave flume of Leichtweiss-Institute for Hydraulic Engineering and Water Resources (LWI) in Braunschweig Germany. The test programme is designed to generate a comprehensive data set covering a broader range of wave conditions including not only deep and intermediate water conditions but also nearly shallow and shallow water conditions, which are missing in the available laboratory studies on wave run-up on piles. The relative wave height (H/h), relative water depth (h/L) and slenderness of pile (D/L) are identified as the key parameters governing the relative wave run-up (R-u/H). Based on these parameters, new formulae covering the range of tested conditions (0.028 <= H/h 5 0.593, 0.042 <= h/L <= 0.861, 0.003 <= D/L <= 0.206) are developed to predict regular non-breaking wave run-up on single piles using a combination of the m5 model tree and nonlinear regression techniques. Using statistical accuracy metrics such as agreement index I-a, squared correlation coefficient R-2 and scatter index SI, the performance of the developed formulae is evaluated. It is shown that the new formulae outperform the current formulae in predicting regular wave run-up on single piles. This success is in part due to the explicit account for the water depth in the new experiments and formulae. The proposed model is valid for a wider range of wave conditions and, therefore, more appealing for engineering practice compared to those available for the estimation of regular wave run-up. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Wave run-up Vertical pile Regular wave Water depth condition m5 model tree Prediction formulae

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Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach

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WATER RESOURCES mANAGEmENT 2021年第15期35卷 5383-5407页

作者： Roy, Dilip Kumar Saha, Kowshik Kumar Kamruzzaman, mohammad Biswas, Sujit Kumar Hossain, mohammad Anower Bangladesh Agr Res Inst Irrigat & Water Management Div Gazipur 1701 Bangladesh Tech Univ Berlin Dept Agromechatron D-10623 Berlin Germany Bangladesh Rice Res Inst Farm Machinery & Postharvest Technol Div Gazipur 1701 Bangladesh

Reference evapotranspiration (ET0) is a crucial element for deriving irrigation scheduling of major crops. Thus, precise projection of ET0 is essential for better management of scarce water resources in many parts of the globe. This study evaluates the potential of a Hierarchical Fuzzy System (HFS) optimized by Particle Swarm Optimization (PSO) algorithm (PSO-HFS) to predict daily ET0. The meteorological variables and estimated ET0 (using FAO-56 Penman-monteith equation) were employed as inputs and outputs, respectively, for the PSO-HFS model. The prediction accuracy of PSO-HFS was compared with that of a Fuzzy Inference System (FIS), m5 model tree (m5tree), and a Regression tree (RT) model. Ranking of the models was performed using the concept of Shannon's Entropy that accounts for a set of performance evaluation indices. Results revealed that the PSO-HFS model performed better (with Entropy weight = 0.93) than the benchmark models (Entropy weights of 0.77, 0.74, and 0.90 for the FIS, RT, and m5tree, respectively). Furthermore, the generalization capabilities of the proposed models were evaluated using the dataset from a test station. Generalization performances revealed that the models performed equally well with the unseen test dataset and that the PSO-HFS model provided superior performance (with R = 0.93, RmSE = 0.59 mm d(-1) and IOA = 0.94) while the RT model (with R = 0.82, RmSE = 0.90 mm d(-1), and IOA = 0.83) exhibited the worst performance for the test dataset. The overall results imply that the PSO-HFS model could effectively be utilized to model ET0 quite efficiently and accurately.

关键词： Reference evapotranspiration Hierarchical fuzzy systems Fuzzy inference system Regression tree m5 model tree Shannon's entropy

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Evaporation modelling using different machine learning techniques

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INTERNATIONAL JOURNAL OF CLImATOLOGY 2017年第Sup1期37卷 1076-1092页

作者： Wang, Lunche Kisi, Ozgur Hu, Bo Bilal, muhammad Zounemat-Kermani, mohammad Li, Hui China Univ Geosci Sch Earth Sci Lab Crit Zone Evolut Luoyu Rd Wuhan 430074 Hubei Peoples R China Chinese Acad Sci Inst Atmospher Phys State Key Lab Atmospher Boundary Phys & Atmospher Beijing Peoples R China Int Black Sea Univ Ctr Interdisciplinary Res Tbilisi Georgia Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Kowloon Hong Kong Peoples R China Shahid Bahonar Univ Kerman Dept Water Engn Kerman Iran

Accurate prediction of pan evaporation (Ep) is critical for water resource management. This article investigates the capabilities of three different soft computing methods at estimating monthly Ep at six stations in the Yangtze River Basin using climatic factors, including the air temperature (Ta), solar radiation (Rg), air pressure (Pa) and wind speed (Ws) for the period of 1961-2000. The first part of the study focused on testing and comparing model accuracy levels at each station using local input combinations. The results indicate that the fuzzy genetic (FG) model generally produces better results than adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP) and m5 model tree (m5tree) specifications in terms of the root mean square error, mean absolute error and coefficient of determination values. The performance of the above models was also examined using cross-station applications (estimating Ep without local input or output data) in the second part of the study. The third part focused on estimating Ep using generalized FG, ANFIS-GP and m5 tree models. Collectively, the results demonstrate that the FG model can be successfully used to estimate Ep without any local inputs and outputs and that a single generalized FG model can also be used at six different locations.

关键词： pan evaporation fuzzy genetic algorithm ANFIS-GP m5 model tree cross-station application

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Applying several machine learning approaches for prediction of unconfined compressive strength of stabilized pond ashes

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NEURAL COmPUTING & APPLICATIONS 2020年第13期32卷 9019-9028页

作者： Suthar, manju Maharaja Agrasen Univ Dept Civil Engn Baddi 174103 Himachal Prades India

This paper evaluates the potential of five modeling approaches, namely m5 model tree, random forest, artificial neural networks, support vector machines and Gaussian processes, for the prediction of unconfined compressive strength of stabilized pond ashes with lime and lime sludge. The study not only presents five models for the same set of data but also compares the overall performance of them. Dataset used consists of 255 samples acquired from laboratory experiments. Out of the total, 170 randomly chosen samples were used for training and remaining 85 were used for testing the models. Input dataset consists of eight parameters (uniformity coefficient, coefficient of curvature, maximum dry density, optimum moisture content, lime, lime sludge, curing period and 7-day soaked California bearing ratio), while the output is UCS value at 7, 28, 45, 90 and 180 days of curing. Comparisons of results propose that Gaussian processes modeling strategy works well and the overall performance was substantially nearer to the exact agreement line. As a result of GP model, higher value of CC = 0.997 and lower values of RmSE = 23.016 kPa and mAE = 16.455 were obtained for testing the dataset. Sensitivity analysis suggests that lime, lime sludge, curing period and California bearing ratio are the significant parameters for predicting the unconfined compressive strength of stabilized pond ashes. The results confirmed that GP models are in a position to predict the unconfined compressive strength of stabilized pond ashes with an excessive degree of accuracy;however, GP modeling approach proves that this approach is more economical and less difficult in comparison with tedious laboratory work.

关键词： Unconfined compressive strength m5 model tree Random forest Artificial neural network Support vector machines Gaussian processes

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Polynomial chaos expansion and response surface method for nonlinear modelling of reference evapotranspiration

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HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2019年第6期64卷 720-730页

作者： Keshtegar, Behrooz Kisi, Ozgur Zounemat-Kermani, mohammad Univ Zabol Dept Civil Engn Zabol Iran Ilia State Univ Fac Nat Sci & Engn Tbilisi Georgia Shahid Bahonar Univ Kerman Water Engn Dept Kerman Iran

The feasibility of polynomial chaos expansion (PCE) and response surface method (RSm) models is investigated for modelling reference evapotranspiration (ET0). The modelling results of the proposed models are validated against the m5 model tree and multi-layer perceptron neural network (mLPNN) methods. Two meteorological stations, Isparta and Antalya, in the mediterranean region of Turkey, are inspected. Various input combinations of daily air temperature, solar radiation, wind speed and relative humidity are constructed as input attributes for the ET0. Generally, the modelling accuracy is increased by increasing the number of inputs. Including wind speed in the model inputs considerably increases their accuracy in modelling ET0. mean absolute error (mAE), root mean square error (RmSE), agreement index (d) and Nash-Sutcliffe efficiency (NSE) are used as comparison criteria. The PCE is the most accurate model in estimating daily ET0, giving the lowest mAE (0.036 and 0.037 mm) and RmSE (0.047 and 0.050 mm) and the highest d (0.9998 and 0.9999) and NSE (0.9992 and 0.9996) with the four-input PCE models for Isparta and Antalya, respectively.

关键词： reference evapotranspiration polynomial chaos expansion mLPNN m5 model tree response surface method

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Non-tuned data intelligent model for soil temperature estimation: A new approach

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GEODERmA 2018年 330卷 52-64页

作者： Sanikhani, Hadi Deo, Ravinesh C. Yaseen, Zaher mundher Eray, Okan Kisi, Ozgur Univ Kurdistan Agr Fac Water Engn Dept Sanandaj Iran Univ Southern Queensland Inst Agr & Environm Int Ctr Appl Climate Sci Sch Agr Computat & Environm Sci Toowoomba Qld 4300 Australia Ton Duc Thang Univ Fac Civil Engn Sustainable Dev Civil Engn Res Grp Ho Chi Minh City Vietnam Int Black Sea Univ Fac Comp Technol & Engn Tbilisi Georgia Ilia State Univ Fac Nat Sci & Engn Tbilisi Georgia

In knowledge-based decision-support systems, soil temperature (ST) estimation can be considered as the core modeling task used for investigating the dynamics of solar energy exchange between the land surface and the sub-surface soil layers. In addition, the impacts of meteorological processes on energy uptake into the underlying soils and the design of more resilient and sustainable agricultural systems for improved crop health and resource management. In this paper, monthly ST estimation at various soil depths (i.e., 5, 50 and 100 cm) is performed by applying data-intelligent machine learning models: extreme learning machine (ELm), artificial neural network (ANN) and m5 model tree (m5 tree). The predictive models are trained using meteorological information from two stations (i.e., mersin and Adana, Turkey). The models are constructed using monthly input variables, including air temperature (T-air), windspeed (W), relative humidity (RH), solar radiation (SR), while the objective variable is the soil temperature measurement at 5, 50 and 100 cm depths for the period 1986-2010. multi-objective performance criteria are applied to diagnose the predictive accuracy of the data-intelligent models, entailing the correlation coefficient (R), root mean square error (RmSE), mean absolute error (mAE), Willmott's Index (WI), Nash Sutcliffe's coefficient (Nash) and the Legates & mcCabe's Index (LmI). Based on the tested dataset, the ELm model is yielded the most accurate performance for mersin station compared to the lower performance of ANN and m5 tree models. For this case, the most accurate performance is attained for the ST estimation at a depth of 50 cm, with the highest value of R = 0.992, WI = 0.999, Nash = 0.981, LmI = 0.879 and the lowest value of the relative RmSE and mAE values (i.e., 4.7 and 4.0%, respectively), attained using T-air, W, RH and periodicity as the predictor variables. The performance results for the Adana station behave differently, where ELm is seen to

关键词： Decision-support systems Extreme learning machine Artificial neural network m5 model tree Agricultural precision Soil temperature

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