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m5 model tree based modelling of reference evapotranspiration

HYDROLOGICAL PROCESSES 2009年第10期23卷 1437-1443页

作者： Pal, mahesh Deswal, Surinder Natl Inst Technol Dept Civil Engn Kurukshetra 136119 Haryana India

This paper investigates the potential of m5 model tree based regression approach to model daily reference evapotranspiration using climatic data of Davis station maintained by California irrigation management Information System (CImIS). Four inputs including solar radiation, average air temperature, average relative humidity, and average wind speed whereas reference evapotranspiration Calculated using a relation provided by the CImIS was used as output. To compare the performance of m5 model tree in predicting the reference evapotranspiration, FAO-56 Pennian-monteith equation and calibrated Hargreaves-Samani relation was used. A comparison of results suggests that m5 model tree approach works well in comparison to both FAO-56 and calibrated Hargreaves-Samani relations. To judge the generalization capability of m5 model tree approach, model created by using the Davis data set was tested with the datasets of four different sites. Results from this part of the study Suggest that m5 model tree could successfully be employed in modeling the reference evapotranspiration. Further, sensitivity analysis with m5 model tree approach suggests the suitability of solar radiation, average air temperature, average relative humidity, and average wind speed as input parameters to model the reference evapotranspiration Copyright (C) 2009 John Wiley & Sons, Ltd.

关键词： reference evapotranspiration FAO-56 Pennian-monteith equation Hargreaves-Samani relation m5 model tree

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m5 model tree for pier scour prediction using field dataset

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KSCE JOURNAL OF CIVIL ENGINEERING 2012年第6期16卷 1079-1084页

作者： Pal, mahesh Singh, N. K. Tiwari, N. K. NIT Kurukshetra Dept Civil Engn Kurukshetra 136119 Haryana India MIT Moradabad Dept Civil Engn Moradabad 244001 India

This paper investigates the potential of m5 model tree, a tree based regression approach to predict the local scour around bridge piers using field dataset. The dataset consisting of 232 pier scour measurements were used. Out of the total dataset, 154 measurements were used to train m5 model tree and the remaining 78 to test the trained model. Comparison of results with four predictive equations suggests an improved performance by m5 model tree in predicting the pier scour depth with dimensioned data and found it performing equally well to a back propagation neural network. A sensitivity analysis with field dataset indicates that the pier width, depth and flow velocity are important factors in predicting the scour depth with m5 model tree. Furthermore, m5 model tree based regression approach provides a linear regression function that can be used by field engineers to predict the pier scour.

关键词： pier scour scour modeling m5 model tree neural network HEC-18 froehlich design equation

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m5 model tree and monte Carlo simulation for efficient structural reliability analysis

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APPLIED mATHEmATICAL modelLING 2017年 48卷 899-910页

作者： Keshtegar, Behrooz Kisi, Ozgur Univ Zabol Dept Civil Engn PB 9861335-856 Zabol Iran Int Black Sea Univ Canter Interdisciplinary Res Tbilisi Georgia

Practically, the performance of many engineering problems can be defined using a complex implicit limit state function. Approximation of the accurate failure probability is very time-consuming and inefficient based on monte Carlo simulation (mCS) for complex performance functions. m5 model tree (m5tree) model is robust approach for simulation and prediction phenomena, which provides ability to dealing with complex implicit problems by dividing them into smaller problems. By improving the efficiency of reliability method using accurate approximated failure probability, an efficient reliability method using the mCS and m5tree is proposed to calibrate the performance function and estimate the failure probability, respectively. The superiorities including simplicity and accuracy of m5tree meta-model are investigated to evaluate the actual performance function through five nonlinear complex mathematical and structural reliability problems. The proposed reliability method-based mCS and m5tree improved the computational efforts for evaluating the performance function in reliability analysis. The m5tree significantly increased the efficiency of reliability analysis with accurate failure probability. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Reliability analysis monte Carlo simulation m5 model tree Failure probability Performance function

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m5 model tree application in daily river flow forecasting in Sohu Stream, Turkey

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WATER RESOURCES 2013年第3期40卷 233-242页

作者： Sattari, m. Taghi Pal, mahesh Apaydin, Halit Ozturk, Fazli Tabriz Univ Fac Agr Dept Water Engn Tabriz Iran Natl Inst Technol Dept Civil Engn Kurukshetra 36119 Haryana India Ankara Univ Dept Farm Struct & Irrigat Fac Agr TR-06110 Ankara Turkey

This study investigate the potential of m5 model tree in predicting daily stream flows in Sohu river located within the municipal borders of Ankara, Turkey. The results of the m5 model tree was compared with support vector machines. Both modelling approaches were used to forecast up to 7-day ahead stream flow. A comparison of correlation coefficient and root mean square value indicates that m5 model tree approach works equally well to the SVm for same day discharge prediction. The m5 model tree also works well up to 7-day ahead discharge forecasting in comparison of SVm with this data set. An advantage of using m5 model tree approach is the availability of simple linear models to predict the discharge as well use of less computational time.

关键词： m5 model tree support vector machine Daily river flow Sohu Turkey

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m5 model tree based predictive modeling of road accidents on non-urban sections of highways in India

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ACCIDENT ANALYSIS AND PREVENTION 2016年第0期96卷 108-117页

作者： Singh, Gyanendra Sachdeva, S. N. Pal, mahesh Deenbandhu Chhotu Ram Univ Sci & Technol Dept Civil Engn Sonepat Haryana India Natl Inst Technol Dept Civil Engn Kurukshetra Haryana India

This work examines the application of m5 model tree and conventionally used fixed/random effect negative binomial (FENB/RENB) regression models for accident prediction on non-urban sections of highway in Haryana (India). Road accident data for a period of 2-6 years on different sections of 8 National and State Highways in Haryana was collected from police records. Data related to road geometry, traffic and road environment related variables was collected through field studies. Total two hundred and twenty two data points were gathered by dividing highways into sections with certain uniform geometric characteristics. For prediction of accident frequencies using fifteen input parameters, two modeling approaches: FENB/RENB regression and m5 model tree were used. Results suggest that both models perform comparably well in terms of correlation coefficient and root mean square error values. m5 model tree provides simple linear equations that are easy to interpret and provide better insight, indicating that this approach can effectively be used as an alternative to RENB approach if the sole purpose is to predict motor vehicle crashes. Sensitivity analysis using m5 model tree also suggests that its results reflect the physical conditions. Both models clearly indicate that to improve safety on Indian highways minor accesses to the highways need to be properly designed and controlled, the service roads to be made functional and dispersion of speeds is to be brought down. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Predictive modeling Road safety m5 model tree Random effect negative binomial model

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Comparison of m5 model tree and Artificial Neural Network's methodologies in modelling Daily Reference Evapotranspiration from NOAA Satellite Images

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WATER RESOURCES mANAGEmENT 2016年第9期30卷 3063-3075页

作者： Rahimikhoob, Ali Univ Tehran Dept Irrigat & Drainage Engn Coll Aburaihan Pakdasht Iran

The objective of this study was to compare feed-forward artificial neural network (ANN) and m5 model tree for estimating reference evapotranspiration (ET0) only on the basis of the remote sensing based surface temperature (T-s) data. The input variables for these models were the daytime surface temperature at the cold pixel obtained from the AVHRR/NOAA sensor and extraterrestrial radiation (R-a). The study has been carried out in five irrigated units that cultivate sugar cane, which located in the Khuzestan plain in the southwest of Iran. A total of 663 images of NOAA-AVHRR level 1b during the period 1999-2009, covering the area of this study were collected from the Satellite Active Archive of NOAA. The FAO-56 Penman-monteith model was used as a reference model for assessing the performance of the two above approaches. The study demonstrated that modelling of ET0 through the use of m5 model tree gave better estimates than the ANN technique. However, differences with the ANN model are small. Root mean square error and R-2 for the comparison between reference and estimated ET0 for the tested data set using the proposed m5 model are 13.7 % and 0.96, respectively. For the ANN model these values are 14.3 % and 0.95, respectively.

关键词： Reference evapotranspiration AVHRR data Cold pixel m5 model tree ANN model

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A Comparison Between Conventional and m5 model tree methods for Converting Pan Evaporation to Reference Evapotranspiration for Semi-Arid Region

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WATER RESOURCES mANAGEmENT 2013年第14期27卷 4815-4826页

作者： Rahimikhoob, Ali Asadi, maryam mashal, mahmood Univ Tehran Dept Irrigat & Drainage Engn Tehran Iran

In this study, the performance of m5 model tree and conventional method for converting pan evaporation data (E-p) to reference evapotranspiration (ET0) were assessed in semi-arid regions. Conventional method uses pan coefficient (K-p) as a factor to convert E-p to ET0. Two common K-p equations for pans with dry fetch (Allen et al. 1998;Abdel-Wahed and Snyder in J Irrig Drain Eng 134(4):425-429, 2008) were considered for the comparison. The values of ET0 derived using these three methods were compared to those estimated using the reference FAO Penmane monteith (FAO-Pm) method under semi-arid conditions of the Khuzestan plain (Southwest Iran). The results showed that the m5 model is the best one to estimate ET0 over test sites (0.5 mm d(-1) of root mean square error (RmSE) and 0.98 of coefficient of determination (R-2). Conversely, the performance of the two K-p equations was poor.

关键词： Reference evapotranspiration Pan evaporation m5 model tree FAO-56 Penman-monteith equation

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Stream Flow Forecasting of Poorly Gauged mountainous Watershed by Least Square Support Vector machine, Fuzzy Genetic Algorithm and m5 model tree Using Climatic Data from Nearby Station

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WATER RESOURCES mANAGEmENT 2018年第14期32卷 4469-4486页

作者： Adnan, Rana muhammad Yuan, Xiaohui Kisi, Ozgur Adnan, muhammad mehmood, Asif Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China Ilia State Univ Fac Nat Sci & Engn GE-0162 Tbilisi Georgia Chinese Acad Sci Geog & Nat Res Inst Beijing 100000 Peoples R China

Forecasting stream flow is a very importance issue in water resources planning and management. The ability of three soft computing methods, least square support vector machine (LSSVm), fuzzy genetic algorithm (FGA) and m5 model tree (m5T), in forecasting daily and monthly stream flows of poorly gauged mountainous watershed using nearby hydro-meteorological data is investigated in the current study. In the first application, monthly stream flows of Hunza river are forecasted using local stream flow data of Hunza and precipitation and temperature data of nearby station. LSSVm provides slightly better forecasts than the FGA and m5T models. Stream flow and temperature inputs generally give better forecasts compared to other inputs. In the second application, daily stream flows of Hunza river are forecasted using local stream flow data of Hunza and precipitation and temperature data of nearby station. Better results are obtained from the models comprising only stream flow inputs. In general, a better accuracy is obtained from LSSVm models in relative to the FGA and m5T. The results indicate that the monthly and daily stream flows of Hunza can be accurately forecasted by using only nearby climatic data. In the third application, daily stream flows of Hunza river are forecasted using local stream flow and climatic data and the models' accuracy is slightly increased in relative to the previous applications. LSSVm generally performs superior to the FGA and m5T in forecasting daily stream flow of Hunza river using local stream flow and climatic inputs.

关键词： Fuzzy logic approach Genetic algorithm Least square support vector machine m5 model tree Streamflow forecasting

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Estimation of Suspended Sediment Concentration by m5 model tree Based on Hydrological and moderate Resolution Imaging Spectroradiometer (mODIS) Data

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WATER RESOURCES mANAGEmENT 2020年第12期34卷 3725-3737页

作者： Zahiri, Javad mollaee, Zeynab Ansari, mohammad Reza Agr Sci & Nat Resources Univ Khuzestan Dept Water Engn Khuzestan Iran Agr Sci & Nat Resources Univ Khuzestan Dept Soil Sci Khuzestan Iran

The Estimation of suspended sediment concentration (SSC) is an important factor in river engineering, which is used as an indicator of land-use change, water quality studies, and all projects related to constructions in rivers. In this research, the m5 model tree and the moderate Resolution Imaging Spectroradiometer (mODIS) data were utilized to estimate the SSC at Ahvaz station on the Karun River. In this study, 135 cloud-free images of the mODIS sensor on the Terra satellite were taken for days corresponding to field SSC data, during the years 2000 to 2015. Input parameters of the model tree in this study were flow discharge, derived from hydrological data, and red (R), near-infrared (NIR) bands, and NIR/R ratio extracted from mODIS imagery. The results of statistical analysis illustrate that the m5 model outperforms the sediment rating curve (SRC) method, which is the most common method of estimating suspended sediment load. The Nash-Sutcliffe efficiency index for the m5 model tree of 0.58 was achieved, which was much better than that of the SRC method (0.26). At high fluxes, the efficiency of the SRC method significantly reduced, while the model tree provides acceptable results. The global sensitivity analysis on the m5 model pointed out that 93% of output variance was established by the main effects of input parameters, and less than 7% belong to the interaction effects. 73% and 12% of output variance specified by the main effects of flow discharge and NIR/R ratio, respectively.

关键词： Suspended sediment concentration m5 model tree mODIS Global sensitivity

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Biochemical oxygen demand prediction: development of hybrid wavelet-random forest and m5 model tree approach using feature selection algorithms

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ENVIRONmENTAL SCIENCE AND POLLUTION RESEARCH 2020年第27期27卷 34322-34336页

作者： Golabi, mohammad Reza Farzi, Soheila Khodabakhshi, Fariba Sohrabi Geshnigani, Fatemeh Nazdane, Fatemeh Radmanesh, Feridon Shahid Chamran Univ Ahvaz Fac Water Sci Dept Water Resources Engn Ahwaz Iran Razi Univ Dept Water Engn Campus Agr & Nat Resources Kermanshah Iran Univ Zabol Dept Water Engn Zabol Iran Shahrekord Univ Dept Water Engn Fac Agr Shahrekord Iran

Supplying adequate water to individuals and maintaining water supplies to support human life, particularly to rapidly urbanizing communities, are of paramount importance in the development of urban areas in each country worldwide. In turn, maintaining water resource quality and avoiding permanent damage as a consequence of environmental pollution and unsustainable off-take from sources such as rivers and aquifers should be considered as important as the water supply quantity. In this study, random forest (RF) and m5 model tree (m5) models were used to predict water biochemical oxygen demand (BOD). Having decomposed the input variables by wavelet transform, based on the feature selection algorithms (FS) (relief (RA), correlation (CA), principal component analysis (PCA), and ant colony optimization (ACO) algorithms), the important components were recognized and inserted into the RF and m5 models. The proposed approach was applied to Karun River in Ahvaz station on a monthly basis from 2006 to 2018. The results showed that the RF model had better performance withR= 0.872, mAE = 0.0312, and RmSE = 0.0332 values for the variable of BOD compared with the m5 model withR= 0.751, mAE = 0.0377, and RmSE = 0.0468 values. In addition, comparing RF and hybrid models, the purposed hybrid models were considered as viable options to improve the prediction accuracy of BOD. The findings also showed that, among the hybrid models, the WRF-PCA model withR= 0.927, mAE = 0.0198, and RmSE = 0.0241 values was the best model for the prediction of BOD values.

关键词： BOD modeling RF algorithm m5 model tree Wavelet transform Feature selection Karun River

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