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Integrating piecewise and symbolic regression with remote sensing data for spatiotemporal analysis of surface water total dissolved solids in the Karun River, Iran

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RESULTS IN ENGINEERING 2025年 25卷

作者： Zahiri, Javad Nikoo, mohammad Reza moradi-Sabzkouhi, Adell Cheraghi, mitra Nawi, Nazmi mat Agr Sci & Nat Resources Univ Khuzestan Dept Water Engn Ahvaz Iran Sultan Qaboos Univ Dept Civil & Architectural Engn Muscat Oman Agr Sci & Nat Resources Univ Khuzestan Dept Nat Engn Mollasani Iran Univ Putra Malaysia Inst Plantat Studies Serdang Malaysia

monitoring water quality, including total dissolved solids (TDS), across various spatial and temporal scales is essential for comprehending water health level. The Karun River's water is a vital resource for drinking water supply and agricultural irrigation, making accurate monitoring of its quality crucial for ensuring public health and sustainable resource management. This study presents a novel hybrid modeling approach that combines piecewise and symbolic regression (SR) models with Landsat imagery to predict TDS levels in river systems. Specific spectral bands, including the red and near-infrared reflectance from Landsat-9, were used as input variables. Field sampling of TDS, as the output variable, was conducted along the Karun River on three dates, synchronized with Landsat satellite images. The innovative use of a fuzzy-based uncertainty analysis, coupled with AHP weighting, allowed for a comprehensive assessment of TDS estimation accuracy and uncertainty. The Composite Uncertainty Index (CUI) approach revealed that the multivariate Adaptive Regression Splines (mARS) and m5 models performed better than other models, with CUI values of 0.83 and 0.72, respectively. mARS demonstrated higher accuracy under low uncertainty conditions, while m5P excelled in scenarios of elevated uncertainty due to its reduced sensitivity and strong Nash-Sutcliffe coefficient. The hybrid modeling approach presented in this study offers a unique contribution to remote sensing-based water quality monitoring. By leveraging advanced regression techniques and uncertainty quantification, the findings enable more reliable predictions of TDS levels, which are crucial for sustainable river management.

关键词： Total dissolved solids Symbolic regression model m5 model tree mARS model Landsat imagery

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model trees as an alternative to neural networks in rainfall-runoff modelling

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HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2003年第3期48卷 399-411页

作者： Solomatine, DP Int Inst Infrastruct Hydraul & Environm Engn IHE NL-2601 DA Delft Netherlands Dept Hydrol & Meteorol Kathmandu Nepal

This paper investigates the comparative performance of two data-driven modelling techniques, namely, artificial neural networks (ANNs) and model trees (mTs), in rainfall-runoff transformation. The applicability of these techniques is studied by predicting runoff one, three and six hours ahead for a European catchment. The result shows that both ANNs and mTs produce excellent results for 1-h ahead prediction, acceptable results for 3-h ahead prediction and conditionally acceptable result for 6-h ahead prediction. Both techniques have almost similar performance for 1-h ahead prediction of runoff, but the result of the ANN is slightly better than the mT for higher lead times. However, the advantage of the mT is that the result is more understandable and allows one to build a family of models of varying complexity and accuracy.

关键词： rainfall runoff artificial neural networks m5 model tree prediction committee machine

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modeling reference evapotranspiration using three different heuristic regression approaches

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AGRICULTURAL WATER mANAGEmENT 2016年第0期169卷 162-172页

作者： Kisi, Ozgur Canik Basari Univ Architectural & Engn Fac Dept Civil Engn TR-55080 Samsun Turkey

modeling reference evapotranspiration (ETo) is important in reservoir management, planning regional water resources and evaluation of drinking-water supplies. The study investigates the ability of three different heuristic regression approaches, least square support vector regression (LSSVR), multivariate adaptive regression splines (mARS) and m5 model tree (m5tree) in modeling ET0. The first part of the study focused on testing the accuracy of the LSSVR, mARS and m5tree models in estimating the ET0 data of Antalya and Isparta stations located in mediterranean Region of Turkey. Cross-validation method was utilized in the applications. The LSSVR models were observed to be better than the mARS and m5tree models in estimating ET0 of Antalya and Isparta stations with local input and output data. The accuracy of the applied methods was investigated in estimation of ET0 using air temperature, solar radiation, relative humidity and wind speed inputs from nearby station in the second part of the study (cross-station application without local input data). The results showed that the mARS models provided better accuracy than the LSSVR and m5tree models with respect to SI, mean absolute error (mAE) and determination coefficient (R-2). In the third part of the study, the accuracy of the applied models was investigated in ET0 estimation using input and output data from nearby station. The results showed that the m5tree models outperformed the other models with respect to SI, mAE and R-2. The overall results showed that the LSSVR could be successfully used in estimating ET0 by using local input and output data. In case of without local inputs, however, the mARS model performed better than the LSSVR and m5tree models while the m5tree was observed to be the best alternative for estimating ET0 in the absence of local input and output data. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Reference evapotranspiration Heuristic regression approaches Least square support vector regression multivariate adaptive regression splines m5 model tree modeling

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Forecast of chlorophyll-a concentration as an indicator of phytoplankton biomass in El Val reservoir by utilizing various machine learning techniques: A case study in Ebro river basin, Spain

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JOURNAL OF HYDROLOGY 2024年 639卷

作者： Garcia-Nieto, Paulino Jose Garcia-Gonzalo, Esperanza Fernandez, Jose Ramon Alonso muniz, Cristina Diaz Univ Oviedo Fac Sci Dept Math Oviedo 33007 Spain Spanish Minist Ecol Transit & Demog Challenge Cantabrian Basin Author Oviedo 33071 Spain Asturias Water Consortium C Santa Susana 15 Oviedo 33007 Spain

The trophic condition of bodies of water, such as oceans, lakes, and reservoirs, can be accurately assessed thanks to the use of chlorophyll-a, or Chl-a, as an indicator of phytoplankton biomass and abundance. In fact, the main molecule in charge of photosynthesis is Chl-a. This work presents a powerful and reliable nonparametric method for predicting the concentration of Chl-a in El Val reservoir using a dataset containing 240,765 samples: the Support Vector Regression (SVR) with different kinds of kernels. This mathematical SVR-relied model was constructed using five years (2018-2022) of water quality variable monitoring (physico-chemical independent variables) in the El Val reservoir (located in the northeast of Spain). For comparison, m5 model trees, a the multilayer Perceptron (mLP), that is a particular type of artificial neural network (ANN), and multivariate linear regression (mLR) were also used for the same observed data. The Grid Search (GS) algorithm was employed as an optimizer;this approach greatly improves the regression precision by allowing the optimal kernel parameters to be chosen during the SVR training phase. There are two ways to sum up the findings of this investigation. First, it is determined how relevant each input variable is to the Chl-a concentration in the El Val reservoir. Second, this hybrid GS/SVR-relied model with PUK kernel can accurately predict the Chl-a (R2 and r values were 0.8989 and 0.9499, respectively). The model's agreement with the observed data amply proves the remarkable efficacy of this creative strategy.

关键词： Chlorophyll -a Support vector regression (SVR) multilayer perceptron (mLP) m5 model tree multilinear regression Reservoir water quality

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A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment

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NEURAL COmPUTING & APPLICATIONS 2022年第1期34卷 783-812页

作者： Basakin, Eyyup Ensar Ekmekcioglu, Omer Citakoglu, Hatice Ozger, mehmet Istanbul Tech Univ Civil Engn Dept Hydraul Div TR-34469 Istanbul Turkey Erciyes Univ Dept Civil Engn Kayseri Turkey

In this research, monthly wind speed time series of the Kirsehir was investigated using the stand-alone, hybrid and ensemble models. The artificial neural networks, Gaussian process regression, support vector machines and multivariate adaptive regression splines were employed as stand-alone machine learning models, while the discrete wavelet transform was utilized as a pre-processing technique to create hybrid models. moreover, for the first time in wind speed predictions, we generated a multi-stage ensemble model by using the m5 model tree (m5) algorithm to increase the model accuracies. Two major tasks considered to be necessary, in which the first is to obtain the lag times by using autocorrelation functions, and the latter is to determine the optimum mother wavelet as well as the decomposition level to reduce the uncertainties in wavelet modeling. The results revealed that the hybrid wavelet models outperformed the stand-alone models, while a significant improvement was also observed in m5 ensemble models as the highest Nash-Sutcliffe efficiency coefficient values were obtained in m5 hybrid wavelet multi-stage ensemble models for each lead time prediction. The findings of the study were assessed with respect to the various performance indicators and Kruskal-Wallis test to indicate whether the results are statically significant. The proposed multi-stage ensemble framework also benchmarked with the classical tree-based ensembles, such as Random forest, AdaBoost and XGBoost.

关键词： Discrete wavelet decomposition Ensemble model m5 model tree Soft computing Uncertainty assessment Wind speed

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Forecasting multi-Step Ahead monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm

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Computer modeling in Engineering & Sciences 2020年第11期125卷 699-723页

作者： Xianghui Lu Junliang Fan Lifeng Wu Jianhua Dong School of Hydraulic and Ecological Engineering Nanchang Institute of TechnologyNanchang330099China Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education Northwest A&F UniversityYangling712100China Faculty of Agriculture and Food Kunming University of Science and TechnologyKunming650500China

It is important for regional water resources management to know the agricultural water consumption information several months in *** reference evapotranspiration(ET_(0))in the next few months is important for irrigation and reservoir *** on forecasting of multiple-month ahead ET_(0) using machine learning models have not been reported ***,machine learning models such as the XGBoost model has multiple parameters that need to be tuned,and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal *** study investigated the performance of the hybrid extreme gradient boosting(XGBoost)model coupled with the Grey Wolf Optimizer(GWO)algorithm for forecasting multi-step ahead ET_(0)(1-3 months ahead),compared with three conventional machine learning models,i.e.,standalone XGBoost,multi-layer perceptron(mLP)and m5 model tree(m5)models in the subtropical zone of *** results showed that theGWO-XGB model generally performed better than the other three machine learning models in forecasting 1-3 months ahead ET_(0),followed by the XGB,m5 and mLP models with very small differences among the three *** GWO-XGB model performed best in autumn,while the mLP model performed slightly better than the other three models in *** is thus suggested to apply the mLP model for ET_(0) forecasting in summer but use the GWO-XGB model in other seasons.

关键词： Reference evapotranspiration extreme gradient boosting Grey Wolf Optimizer multi-layer perceptron m5 model tree

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Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models for

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JOURNAL OF HYDROLOGY 2018年 566卷 668-684页

作者： Lu, Xianghui Ju, Yan Wu, Lifeng Fan, Junliang Zhang, Fucang Li, Zhijun Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Jiangxi Peoples R China Northwest A&F Univ Minist Educ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Yangling 712100 Shaanxi Peoples R China

Accurate estimation of pan evaporation (Ep) is required for many applications, e.g., water resources management, irrigation system design and hydrological modeling. However, the estimation of Ep for a target station can be difficult as a result of partial or complete lack of local meteorological data under many conditions. In this study, daily Ep was estimated from local (target-station) and cross-station data in the Poyang Lake Watershed of China using four empirical models and three tree-based machine learning models, including m5 model tree (m5tree), random forests (RFs) and gradient boosting decision tree (GBDT). Daily meteorological data during 2001-2010 from 16 weather stations were used to train the models, while the data from 2011 to 2015 were used for testing. Two cross-station applications were considered between each of the 16 stations and the other 15 stations. The results showed that the radiation-based Priestley-Taylor model (on average RmSE = 1.13 mm d(-1), NSE = 0.53, R-2 = 0.57, mBE = 0.21 mm d(-1)) gave the most accurate daily Ep estimates among the four empirical models during testing, while the mass transfer-based Trabert model (on average RmSE = 1.38 mm d(-1), NSE = 0.25, R-2 = 0.46, mBE = 0.65 mm d(-1)) performed worst. The GBDT model outperformed the RFs model, m5tree model and the empirical models under the same input combinations in terms of prediction accuracy (on average RmSE = 0.86 mm d(-1), NSE = 0.68, R-2 = 0.73, mBE = 0.07 mm d(-1)) and model stability (average percentage increase in testing RmSE = 16.3%). The RmSE values generally increased with the increase in the distance of two cross stations. A distance of less than 100 km between two cross stations is highly recommended for cross-station applications with satisfactory prediction accuracy (median percentage increase in RmSE <5% for cross-station application #1 and <20% for application #2) in the Poyang Lake Watershed of China and maybe elsewhere with similar climates.

关键词： Cross station m5 model tree Random forests Gradient boosting decision tree Poyang Lake Watershed

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Different methods for modeling absorption heat transformer powered by solar pond

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ENERGY CONVERSION AND mANAGEmENT 2007年第3期48卷 724-735页

作者： Sencan, Arzu Kizilkan, Onder Bezir, Nalan C. Kalogirou, Soteris A. Higher Tech Inst Dept Mech Engn CY-2152 Nicosia Cyprus Suleyman Demirel Univ Tech Educ Fac Dept Mech Educ TR-32260 Isparta Turkey Suleyman Demirel Univ SciLiterature Fac Dept Phys TR-32260 Isparta Turkey

Solar ponds are a type of solar collector used for storing solar energy at temperature below 90 degrees C. Absorption heat transformers (AHTs) are devices used to increase the temperature of moderately warm fluid to a more useful temperature level. In this study, a theoretical modelling of an absorption heat transformer for the temperature range obtained from an experimental solar pond with dimensions 3.5 x 3.5 x 2 in is presented. The working fluid pair in the absorption heat transformer is aqueous ternary hydroxide fluid consisting of sodium, potassium and caesium hydroxides in the proportions 40:36:24 (NaOH: KOH:CsOH). Different methods such as linear regression (LR), pace regression (PR), sequential minimal optimization (SmO), m5 model tree, m5' rules, decision table and back propagation neural network (BPNN) are used for modelling the absorption heat transformer. The best results were obtained by the back propagation neural network model. A new formulation based on the BPNN is presented to determine the flow ratio (FR) and the coefficient of performance (COP) of the absorption heat transformer. The BPNN procedure is more accurate and requires significantly less computation time than the other methods. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： solar ponds absorption heat transformers pace regression SmO m5 model tree m5 ' rules decision table back propagation neural network

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Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: Application on the perennial rivers in Iran and South Korea

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JOURNAL OF HYDROLOGY 2019年 572卷 470-485页

作者： Rezaie-Balf, mohammad Kim, Sungwon Fallah, Hossein Alaghmand, Sina Grad Univ Adv Technol Kerman Dept Civil Engn POB 76315-116 Kerman Iran Dongyang Univ Dept Railrd Construct & Safety Engn Yeongju 36040 South Korea Monash Univ Dept Civil Engn 23 Coll Walk Clayton Vic 3800 Australia

Developing hydrologic models based on data-driven approaches (DDA) is very complicated due to the complex nature of meteorological data. For example, a high degree of irregularities, periodicities, jumps, and other forms of stochastic behavior influence the accuracy of river flow forecasting. In this study, m5 model tree (m5tree) and multivariate adaptive regression spline (mARS) models were developed to forecast one and multi-day-ahead river flow. moreover, ensemble empirical mode decomposition (EEmD), a robust data pre-processing technique, was used to enhance m5tree and mARS models' forecasting. Also, mallows' coefficient (C-p), one of the procedures to determine the input variables, was used to obtain the optimum values of hydrological time series. The developed models were validated using two different meteorological stations (e.g., Kordkheyl in Iran and Hongcheon in South Korea). Forecasting performance of developed models (e.g., m5tree, mARS, EEmD-m5tree, and EEmD-mARS) was evaluated using six different statistical criteria. Comparing the results between standalone and hybrid models indicated that a data pre-processing technique can enhance the performance of standalone models (e.g., m5tree and mARS). EEmD-mARS model (NSE = 0.819 and RmSE = 7.206 m(3)/s (Kordkheyl station) and NSE = 0.738 and RmSE = 50.426 m(3)/s (Hongcheon station)) outperformed m5tree, mARS, and EEmD-m5tree models based on two-day-ahead river flow forecasting in validation stage, respectively. Results showed that EEmD-mARS model was an efficient and robust tool to forecast one and multi-day-ahead (e.g., two, three, and four-day-ahead) river flow.

关键词： River flow forecasting m5 model tree multivariate adaptive regression spline Ensemble empirical mode decomposition mallows' coefficient (C-p)

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A Wavelet Based Data mining Technique for Suspended Sediment Load modeling

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WATER RESOURCES mANAGEmENT 2019年第5期33卷 1769-1784页

作者： Nourani, Vahid molajou, Amir Tajbakhsh, Ali Davanlou Najafi, Hessam Univ Tabriz Dept Water Resources Engn Fac Civil Engn Tabriz Iran Near East Univv Fac Civil & Environm Engn Via Mersin 10 Lefkosa North Cyprus Turkey Iran Univ Sci & Technol Dept Water Resources Engn Fac Civil Engn Tehran Iran

` The suspended sediment load (SSL) modeling generated within a catchment is a significant issue in the environmental and water resources planning and management of watersheds. The estimation methods of SSL are limited by the important parameters and boundary conditions (which are based on the flow and sediment properties). In this situation, soft computing approaches have proven to be an efficient tool in modelling the sediment load of rivers. In this study, the hybrid Wavelet-m5 model was introduced to model SSL of two different rivers (Lighvanchai and Upper Rio Grande) at both daily and monthly scales. In this way, first, the runoff and suspended sediment load time series were decomposed using the wavelet transform to several sub-time series to handle the non-stationary of the runoff and sediment time series. Then, the obtained sub-series were applied to m5 model tree as inputs. The obtained results for the Upper Rio Grande River at daily time scale, showed the better performance of Wavelet-m5 model in comparison with individual Artificial Neural Network (ANN) and m5 models so that the obtained Nash-Sutcliffe efficiency (NSE) was 0.94 by the hybrid Wavelet-m5 model while it was calculated as 0.89 and 0.77 by Wavelet-ANN (WANN) and m5 tree models, respectively. Also, the obtained NSE for the Lighvanchai River at monthly time scale was 0.90 by the hybrid Wavelet-m5 model while it was calculated as 0.78 and 0.69 by Wavelet-ANN (WANN) and m5 tree models in the verification step, respectively.

关键词： Suspended sediment load modeling Runoff Decision tree m5 model tree Wavelet transform Artificial neural network

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