riverflow (Qflow) is a hydrological process that considerably impacts the management and sustainability of water resources. The literature has shown great potential for nature-inspired optimized algorithms (NIOAs), l...
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riverflow (Qflow) is a hydrological process that considerably impacts the management and sustainability of water resources. The literature has shown great potential for nature-inspired optimized algorithms (NIOAs), like hybrid artificial intelligence (HAI) models, for Qflowmodeling. Qflow forecasting needs to be accurate, robust, reliable, and capable of resolving complex non-linear problems to support the decision authority in local and national governments and NGOs. This extensive survey provides a literature review of 100-plus high-impact factor journal articles on developing NIOAs models during 2000-2022. This encompasses a comprehensive review of the established research in different climatic zones, NIOA types, artificial intelligence (AI) models, the input pa-rameters used for model development, Qflow on different time scales, and model evaluation using a wide range of performance metrics. The review also assessed and evaluated several components of relevant literature, along with detailing the existing research gaps. Moreover, the global research gap with future direction is discussed based on current research limitations and possibilities. The data availability evaluation and futuristic suggestions are drafted logically. The review revealed the superiority of the NIOAs among all applied algorithms in the literature. Further, the review concludes that there is a need to improve technical aspects of Qflow forecasting and bridge the gap between scientific research, hydrometeorological model development, and real-world flood and drought management using probabilistic nature inspired (NI) forecasts, especially through effective communication.
In the absence of many gauging stations in the major and mighty river systems, there is a need for satellite-based observations to estimate temporal variations in the river water storage and associated water managemen...
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In the absence of many gauging stations in the major and mighty river systems, there is a need for satellite-based observations to estimate temporal variations in the river water storage and associated water management. In this study, SARAL/AltiKa application for setting up hydraulic model (HEC-RAS) and riverflow simulations over Tapi river India has been discussed. Waveform data of 40Hz from Ka band altimeter has been used for water levels retrieval in the Tapi river. SARAL/AltiKa retrieved water levels were converted to discharge in the upstream location (track-926) using the rating curve available for the nearby gauging site and using linear spatial interpolation technique. Steady state simulations were done for various flow conditions in the upstream. Validation of riverflow model was done in the downstream location (track-367) by comparing simulated and altimeter retrieved water levels (RMSE 0.67 m). Validated model was used to develop rating curve between water levels and simulated discharge for the downstream location which enables to monitor discharge variations from satellite platform in the absence of in situ observations. It has been demonstrated that SARAL/AltiKa data has potential for riverflow monitoring and modeling which will feed for flood disaster forecasting, management and planning.
This study searches the feasibility of a new hybrid extreme leaning machine tuned with improved reptile search algorithm (ELM-IRSA), in river flow modeling. The outcomes of the new method were compared with single ELM...
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This study searches the feasibility of a new hybrid extreme leaning machine tuned with improved reptile search algorithm (ELM-IRSA), in river flow modeling. The outcomes of the new method were compared with single ELM and hybrid ELM-based methods including ELM with salp swarm algorithm (SSA), ELM with equilibrium optimizer (EO) and ELM with reptile search algorithm (RSA). The methods were evaluated using different lagged inputs of temperature, precipitation and riverflow data obtained from Upper Indus Basin located in Pakistan. Models performance evaluation was based on common statistics such as root mean square errors (RMSE), mean absolute errors, determination coefficient and Nash-Sutcliffe Efficiency. The prediction accuracy of single ELM model with respect to RMSE was improved by 2.8%, 7.7%, 15% and 20.7% using SSA, EO, RSA and IRSA metaheuristic algorithms in the test period, respectively. The ELM-IRSA model with lagged temperature and riverflow inputs provided the best predictions with the RMSE improvement of 20.7%.
We examine the tail behaviour and extremal cluster characteristics of two-state Markov-switching autoregressive models where the first regime behaves like a random walk, the second regime is a Stationary autoregressio...
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We examine the tail behaviour and extremal cluster characteristics of two-state Markov-switching autoregressive models where the first regime behaves like a random walk, the second regime is a Stationary autoregression, and the generating noise is light-tailed. Under additional technical conditions we prove that the stationary solution has asymptotically exponential tail and the extremal index is smaller than one. The extremal index and the limiting cluster size distribution of the process are calculated explicitly for some noise distributions, and simulated for others. The practical relevance of the results is illustrated by examining extremal properties of a regime-switching autoregressive process with Gamma-distributed noise, already applied successfully in river flow modeling. The limiting aggregate excess distribution is shown to possess Weibull-like tail in this special case. (c) 2007 Elsevier Ltd. All rights reserved.
The main problem presented in this paper is the safety inlet navigation of the waterway below the bridge in the city of Kaunas in Lithuania. The analyzed reach is located in the Nemunas river downstream of the Kaunas ...
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The main problem presented in this paper is the safety inlet navigation of the waterway below the bridge in the city of Kaunas in Lithuania. The analyzed reach is located in the Nemunas river downstream of the Kaunas dam. It is a part of the waterway E-41 leading to the Klaipeda harbor on the southern coast of the Baltic Sea. The work was initiated by the Lithuanian company UAB "Inzinerinis projektavimas" with funds from the project called European Union Trans-European Transport Network (EU TEN-T). The main requirement imposed along this reach is to keep sufficient depth even in the range of the lowest flows. The depth is sufficient if it is not lower than 1.15 m for minimum flows such as Q(95%) and Q(95%) with ice. The hydraulic conditions for maximum flow Q(50%), Q(5%), and Q(1%) are also taken into account for control because the threat of hydraulic jump generation was also noticed. The research is based on georeferenced data from public and non-public sources. The hydrologic data were received from the Lithuanian Hydrometeorological Service. The physical model was created in the Water Laboratory of the Department of Hydraulic and Sanitary Engineering at Poznan University of Life Sciences, Poland. The preprocessing of spatial data in ArcGIS 10.8.2 and rules of hydraulic similarity were implemented in the process of physical model preparation. Three experiments were conducted in the laboratory with scaled values of Q(95%), Q(5%), and Q(1%). The measurements of the water surface and evaluations of the average velocity were used to validate the 2D numerical model prepared in HEC-RAS 6.3.1. The basic layers of the HEC-RAS model were preprocessed in ArcGIS 10.8.2 by ESRI company. The numerical model was implemented to test different values of unknown roughness of the channel bottom. The simulations were conducted for the real values of Q(95%) and Q(95%) with ice and Q(50%). The results of the simulations were depth and Froude number maps. These maps were classified in
The purpose of the paper was to present selected techniques for the control of riverflow and sediment transport computations with the programming language Python. The base software for modeling of river processes was...
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The purpose of the paper was to present selected techniques for the control of riverflow and sediment transport computations with the programming language Python. The base software for modeling of river processes was the well-known and widely used HEC-RAS. The concepts were tested on two models created for a single reach of the Warta river located in the central part of Poland. The ideas described were illustrated with three examples. The first was a basic simulation of a steady flow run from the Python script. The second example presented automatic calibration of model roughness coefficients with Nelder-Mead simplex from the SciPy module. In the third example, the sediment transport was controlled by Python script. Sediment samples were accessed and changed in the sediment data file stored in XML format. The results of the sediment simulation were read from HDF5 files. The presented techniques showed good effectiveness of this approach. The paper compared the developed techniques with other, earlier approaches to control of HEC-RAS computations. Possible further developments were also discussed.
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