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2018
Modelling suspended sediment load (SSL) from rivers is a complex problem in river basin management. This chapter presents hybrid framework multivariate empirical mode decomposition (MEMD) and stepwise linear regression (SLR) for estimation of SSL from riverflows demonstrated to a case study in Mahanadi River Basin, India. The method involves two major steps: first, the multivariate dataset comprising SSL of current time along with lagged inputs of streamflow and SSL are decomposed into different modes using MEMD; then, the obtained modes are estimated independently by SLR fitting engaging the statistically significant inputs at respective time scales. The sum of the predicted modes gives the desired SSL. The effectiveness of the presented method is evaluated for five models by considering different combinations of inputs, and their performance is compared with traditional multiple linear regression (MLR) and model tree (MT) models. The performance statistics of models showed that fo...
Sediment load in fluvial systems is one of the critical factors shaping the river geomorphological and hydraulic characteristics. A detailed understanding of the total sediment load (TSL) is required for the protection of physical, environmental, and ecological functions of rivers. This study develops a robust methodological approach based on multiple linear regression (MLR) and support vector regression (SVR) models modified by principal component analysis (PCA) to predict the TSL in rivers. A database of sediment measurement from large-scale physical modelling tests with 4759 datapoints were used to develop the predictive model. A dimensional analysis was performed based on the literature, and ten dimensionless parameters were identified as the key drivers of the TSL in rivers. These drivers were converted to uncorrelated principal components to feed the MLR and SVR models (PCA-based MLR and PCA-based SVR models) developed within this study. A stepwise PCA-based MLR and a 10-fold ...
Water Resources Management
Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series2014 •
ABSTRACT In this study, a nonparametric technique to set up a river stage forecasting model based on empirical mode decomposition (EMD) is presented. The approach is based on the use of the EMD and artificial neural networks (ANN) to forecast next month's monthly streamflows. The proposed approach is applied to a real case study. The data from station on the Kizilirmak River in Turkey was used. The mean square errors (MSE), mean absolute errors (MAE) and correlation coefficient (R) statistics were used for evaluating the accuracy of the EMD-ANN model. The accuracy of the EMD-ANN model was then compared to the artificial neural networks (ANN) model. The results showed that EMD-ANN approach performed better than the ANN in predicting stream flows. The most accurate EMD-ANN model had MSE=0.0132, MAE=0.0883 and R=0.8012 statistics, respectively.
Water Science and Technology
The improvement of wavelet-based multilinear regression for suspended sediment load modeling by considering the physiographic characteristics of the watershedThe aim of this study is to model a relationship between the amount of the suspended sediment load by considering the physiographic characteristics of the Lake Urmia watershed. For this purpose, the information from different stations was used to develop the sediment estimation models. Ten physiographic characteristics were used as input parameters in the simulation process. The M5 model tree was used to select the most important features. The results showed that the four factors of annual discharge, average annual rainfall, form factor and the average elevation of the watershed were the most important parameters, and the multilinear regression models were created based on these factors. Furthermore, it was concluded that the annual discharge was the most influential parameter. Then, the stations were divided into two homogeneous classes based on the selected features. To improve the efficiency of the M5 model, the non-stationary rainfall and runoff signals were decomposed into sub-...
ISH Journal of Hydraulic Engineering
Estimation of suspended sediment load using regression trees and model trees approaches (Case study: Hyderabad drainage basin in Iran)2016 •
Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs...
2018 •
Sustainability
Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans ClusteringThe accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data se...
The river stage–discharge relationship has an important impact on modeling, planning, and management of river basins and water resources. In this study, the capability of the Gaussian Process Regressions (GPR) kernel-based approach was assessed in predicting the daily river stage–discharge (RSD) relationship. Three successive hydrometric stations of the Housatonic River were considered, and based on the flow characteristics during the period of 2002–2006 several models were developed and tested via GPR. To enhance the applied model efficiency, two pre-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used. Also, two states of the RSD modeling were investigated. In state 1, each station's own data was used and in state 2, the upstream stations’ datasets were used as input to model the RSD downstream of the river. The single and integrated model results showed that the integrated WT- and EEMD-GPR models resulted in more acc...
2024 •
Teorema Revista Internacional De Filosofia
Conciencia y dualismo2008 •
Journal de Thérapie Comportementale et Cognitive
« Re-considérer » la thérapie d’exposition concernant la peur-évitement de la douleur chronique2017 •
Cambridge Archaeological Journal
Dances with Zigzags in Toro Muerto, Peru: Geometric Petroglyphs as (Possible) Embodiments of Songs2024 •
Perspectives in Biology and Medicine
Valuing the acute subjective experience2023 •
2023 •
Una città e il suo profeta, Firenze di fronte al Savonarola
"Lazare veni foras!": Savonarola and the Franco-Flemish Motet2001 •
2016 •
Brazilian Journal of Botany
Spiral root hairs in Spiranthinae (Cranichideae: Orchidaceae)2015 •
Journal of Plant Ecology
Species abundance is jointly determined by functional traits and negative density dependence in a subtropical forest in southern China2021 •
Psicoperspectivas. Individuo y Sociedad
Postcarrera: Una experiencia de los jubilados en trabajos puenteSAÚDE PÚBLICA NO SÉCULO XXI
Prevalência De Aleitamento Materno Em Crianças Menores De Cinco Anos De Idade De Comunidades Rurais e Ribeirinhas, Amazonas, Brasil2021 •
Journal of Intelligent & Robotic Systems
Design and Development of a Robust Control Platform for a 3-Finger Robotic Gripper Using EMG-Derived Hand Muscle Signals in NI LabVIEW2024 •