Improving long-range hydrological forecasts with extended Kalman filters

Muluye, Getnet Y.

doi:10.1080/02626667.2011.608068

Cited by 14 publications

(4 citation statements)

References 16 publications

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“…In the foreseeable future, it is unlikely that the joint estimation of state and parameter would replace batch calibration completely. Nevertheless, such an operation may help to extend the "efficient period", considering that the short period of influence is one of the obstacles that limits the application of KF DA in operational hydrological forecasting (Muluye 2011, Agboma and Lye 2014, Grassi and De Magistris 2014.…”

Section: Discussionmentioning

confidence: 99%

Review of the Kalman-type hydrological data assimilation

Sun

Seidou

Nistor

et al. 2016

Hydrological Sciences Journal

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There is great potential in Data Assimilation (DA) for the purposes of uncertainty identification, reduction and real-time correction of hydrological models. This paper reviews the latest developments in Kalman filters (KFs), particularly the Extended KF (EKF) and the Ensemble KF (EnKF) in hydrological DA. The hydrological DA targets, methodologies and their applicability are examined. The recent applications of the EKF and EnKF in hydrological DA are summarized and assessed critically. Furthermore, this review highlights the existing challenges in the implementation of the EKF and EnKF, especially error determination and joint parameter estimation. A detailed review of these issues would benefit not only the Kalmantype DA but also provide an important reference to other hydrological DA types.

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Section: Discussionmentioning

confidence: 99%

Review of the Kalman-type hydrological data assimilation

Sun

Seidou

Nistor

et al. 2016

Hydrological Sciences Journal

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“…The artificial neurons were organized in the layers and sent their signals forward, and at the same time, the errors were propagated backward. The hidden and output neurons had sigmoid and pure linear transfer functions, respectively (Muluye, 2011). Also, the sigmoidal and hyperbolic tangent activation function was adopted, which is most suitable, continuously differentiable, monotonic, and non-linear (Yonaba et al, 2010).…”

Section: Modeling Of Streamflow and Suspended Sediment Concentration ...mentioning

confidence: 99%

Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya, Uttarakhand

Rautela

Kumar

Gandhi

et al. 2022

RBRH

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The estimation of stream discharge is an essential component of planning and decision-making. It is highly correlated with many development activities involving water resources. The study of transportation of sediments in the rivers will help us to develop policies and plans for soil conservation, flood control, irrigation, navigation, and aquatic biodiversity problems. Using data-driven models such as Artificial Neural Networks (ANNs), modeling of streamflow and sediment transport is frequently adopted due to their applicability and problem-solving ability. This study has used three training algorithms such as Scaled Conjugate Gradient (SCG), Bayesian Regularization (BR), and Levenberg-Marquardt (LM) to simulate the streamflow and Suspended Sediments Concentration (SSC). After optimizing the best training algorithm based on the model efficiency parameters, L-M based-ANN model has been used to predict streamflow for two years and the modeling of suspended sediments was validated with the help of observed data. The result shows that the simulated results tracked the streamflow as well as SSC with the desired accuracy based on the model efficiency parameters such as coefficient of Determination (R2), Nash Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Root Mean Square Deviation (RMSD). The study's outcomes reveal that in the streamflow the concentration of suspended sediments is significantly affected by the base rock material, glaciers covered by debris, and moraine-laden ice. The transportation of the sediments is high in the Alaknanda basin as compared to the other basins and the previous studies. This might happen due to the severe anthropogenic activities in the surrounding basin.

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“…This study therefore attempts to explore the techniques of the EKF in achieving this goal. The EKF has been widely applied in hydrological forecasts (Muluye, 2011;Hamid et al, 2005) object tracking (e.g., Yang and Baum, 2017;Santosh andMohan, 2014), data science (e.g., David et al, 2017;Wenzel et al, 2006), earth science applications (Soroush et al, 2011), machine learning (e.g., Bishop, 2013Belhajem, 2016;Zibar et al, 2016) and predictive analysis (Liu et al, 2006). Its application in practical engineering problems is also well-known (Bertsekas & Rhodes, 1971;Petersen & Savkin, 1999;James & Petersen, 1998;Kallapur et al, 2009), especially with the introduction of the set membership state estimation approach by Bertsekas and Rhodes (1971).…”

Section: Introductionmentioning

confidence: 99%

An Extended Kalman Filtering Technique for Geodetic First Order Data Assimilation

Kalu

Ndehedehe

Okwuashi

et al. 2021

Preprint

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Data assimilation allows merging of different sources of data to estimate possible states of a system as it evolves in time. This therefore supports the idea of combining classical observations with Global Positioning System (GPS) observations to improve the integrity of first order geodetic controls in Nigeria. Given that these geodetic controls, which were established using traditional techniques and whose algorithms are still in use, the task of optimizing the coordinate values of these monuments to improve efficiency and accuracy in conventional geodetic operations around Nigeria is still a challenge. This study introduces the Extended Kalman Filter (EKF) technique for the modeling of these observations and their uncertainties in addition to exogenous noise, which is handled by an approximate set-valued state estimator. The proposed EKF provides a feasible linearization process in merging classical and GPS data collection modes as shown in our study. For each discrete time in the analysis step, it employs the Kalman gain computation, which attempts to weigh and balance uncertainties between the estimate and observation before proceeding to the analysis step. In this setup, the EKF constrains the system state in order to balance and strengthen the integrity of these first order monuments. The relationship of the derived system state with GPS coordinates (R2 = 0.85) and classical observations (R2 = 0.92) over Nigeria using a multi linear regression analysis is considerably strong. This outcome provides insight to the performance of the test algorithm and builds on the usefulness of data assimilation techniques in geodetic operations.

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Improving long-range hydrological forecasts with extended Kalman filters

Cited by 14 publications

References 16 publications

Review of the Kalman-type hydrological data assimilation

Review of the Kalman-type hydrological data assimilation

Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya, Uttarakhand

An Extended Kalman Filtering Technique for Geodetic First Order Data Assimilation

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