scholarly journals Flood forecasting and flood flow modeling in a river system using ANN

Author(s):  
S. Agarwal ◽  
P. J. Roy ◽  
P. Choudhury ◽  
N. Debbarma

Abstract In terms of predicting the flow parameters of a river system, such as discharge and flow depth, the continuity equation plays a vital role. In this research, static- and routing-type dynamic artificial neural networks (ANNs) were incorporated in the multiple sections of a river flow on the basis of a storage parameter. Storage characteristics were presented implicitly and explicitly for various sections in a river system satisfying the continuity norm and mass balance flow. Furthermore, the multiple-input multiple-output (MIMO) model form having two base architectures, namely, MIMO-1 and MIMO-2, was accounted for learning fractional storage and actual storage variations and characteristics in a given model form. The model architecture was also obtained by using a trial-and-error approach, while the network architecture was acquired by employing gamma memory along with use of the multi-layer perceptron model form. Moreover, this paper discusses the comparisons and differences between both models. The model performances were validated using various statistical criteria, such as the root-mean-square error (whose value is less than 10% from the observed mean), the coefficient of efficiency (whose value is more than 0.90), and various other statistical parameters. This paper suggests applicability of these models in real-time scenarios while following, continuity norm.

2020 ◽  
Vol 10 (4) ◽  
pp. 6092-6101
Author(s):  
G. O. Ajisegiri ◽  
F. L. Muller

This paper addresses the application of the Agent-Based Model (ABM) to simulate the evolution of Multiple Input Multiple Output (MIMO) eco-industrial parks to gain insight into their behavior. ABM technique has proven to be an effective tool that can be used to express the evolution of eco-industrial parks. The ABM represents autonomous entities, each with dynamic behavior. The agents within the eco-industrial park are factories, market buyers, and market sellers. The results showed that the Réseau agent-based model allowed the investigation of the behaviors exhibited by different agents in exchange for materials in the industrial park.


2014 ◽  
Vol 11 (2) ◽  
pp. 22-28
Author(s):  
A. Efremov

Abstract There are two possible general forms of multiple input multiple output (MIMO) regression models, which are either linear with respect to their parameters or non-linear, but in order to estimate their parameters, at a certain stage it could be assumed that they are linear. This is in fact the basic assumption behind the linear approach for parameters estimation. There are two possible representations of a MIMO model, which at a certain level could be fictitiously presented as linear functions of its parameters. One representation is when the parameters are collected in a matrix and hence, the regressors are in a vector. The other possible case is the parameters to be in a vector, but the regressors at a given instant to be placed in a matrix. Both types of representations are considered in the paper. Their advantages and disadvantages are summarized and their applicability within the whole experimental modelling process is also discussed.


Author(s):  
Mario Garcia-Sanz ◽  
Irene Eguinoa ◽  
Marta Barreras ◽  
Samir Bennani

This paper deals with the design of robust control strategies to govern the position and attitude of a Darwin-type spacecraft with large flexible appendages. The satellite is one of the flyers of a multiple spacecraft constellation for a future ESA mission. It presents a 6×6 high order multiple-input–multiple-output (MIMO) model with large uncertainty and loop interactions introduced by the flexible modes of the low-stiffness appendages. The scientific objectives of the satellite require very demanding control specifications for position and attitude accuracy, high disturbance rejection, loop-coupling attenuation, and low controller order. The paper demonstrates the feasibility of a sequential nondiagonal MIMO quantitative feedback theory (QFT) strategy controlling the Darwin spacecraft and compares the results with H-infinity and sequential diagonal MIMO QFT designs.


1996 ◽  
Vol 122 (3) ◽  
pp. 507-513 ◽  
Author(s):  
Douglas G. Walker ◽  
Jeffrey L. Stein ◽  
A. Galip Ulsoy

Model order deduction algorithms have been developed in an effort to automate the production of accurate, minimal complexity models of dynamic systems in order to aid in the design of these systems. Previous algorithms, MODA and Extended MODA, deduce models independent of system inputs and outputs. FD-MODA uses frequency response methods to deduce models of a single input-output pair. In this paper, an input-output criterion based on controllability and observability is combined with the frequency based criterion used by MODA. The new model deduction algorithm, IO-MODA, compares the ratio of the adjacent diagonal values of the system gramian to a user specified threshold. The gramian is computed from a balanced realization of the system. IO-MODA generates an accurate multiple-input multiple-output model of minimum order with physically meaningful states. This model is called a proper MIMO model. An example problem is used to demonstrate this new model deduction algorithm. [S0022-0434(00)02103-1]


2021 ◽  
Author(s):  
S. Agarwal ◽  
P. J. Roy ◽  
P. S. Choudhury ◽  
N. Debbarma

Abstract ANN was used to create a storage-based concurrent flow forecasting model. River flow parameters in an unsteady flow must be modeled using a model formulation based on learning storage change variable and instantaneous storage rate change. Multiple input-multiple output (MIMO) and multiple input-single output (MISO models in three variants were used to anticipate flow rates in the Tar River Basin in the United States. Gamma memory neural networks, as well as MLP and TDNNs models, are used in this study. When issuing a forecast, storage variables for river flow must be considered, which is why this study includes them. While considering mass balance flow, the proposed model can provide real-time flow forecasting. Results obtained are validated using various statistical criteria such as RMS error and coefficient of correlation. For the models, a coefficient of correlation value of more than 0.96 indicates good results. While considering the mass balance flow, the results show flow fluctuations corresponding to expressly and implicitly provided storage variations.


Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1416 ◽  
Author(s):  
Faizan Qamar ◽  
Maraj Uddin Ahmed Siddiqui ◽  
MHD Nour Hindia ◽  
Rosilah Hassan ◽  
Quang Ngoc Nguyen

With an extensive growth in user demand for high throughput, large capacity, and low latency, the ongoing deployment of Fifth-Generation (5G) systems is continuously exposing the inherent limitations of the system, as compared with its original premises. Such limitations are encouraging researchers worldwide to focus on next-generation 6G wireless systems, which are expected to address the constraints. To meet the above demands, future radio network architecture should be effectively designed to utilize its maximum radio spectrum capacity. It must simultaneously utilize various new techniques and technologies, such as Carrier Aggregation (CA), Cognitive Radio (CR), and small cell-based Heterogeneous Networks (HetNet), high-spectrum access (mmWave), and Massive Multiple-Input-Multiple-Output (M-MIMO), to achieve the desired results. However, the concurrent operations of these techniques in current 5G cellular networks create several spectrum management issues; thus, a comprehensive overview of these emerging technologies is presented in detail in this study. Then, the problems involved in the concurrent operations of various technologies for the spectrum management of the current 5G network are highlighted. The study aims to provide a detailed review of cooperative communication among all the techniques and potential problems associated with the spectrum management that has been addressed with the possible solutions proposed by the latest researches. Future research challenges are also discussed to highlight the necessary steps that can help achieve the desired objectives for designing 6G wireless networks.


2013 ◽  
Vol 16 (1) ◽  
pp. 68-79

<p>Since a spectrum of hydrological and geomorphological conditions produce flood pulse environment in a riverine or a deltaic system, it is essential to have the knowledge on spatial and temporal distributions of river flow and dependent processes for environmental flow requirements, ecosystem maintenance, water resources management, and hydrological forecasting among others. Such systems being complex as the exchange of flows between the main channel and the flood plains are not well understood, flow partitioning dynamics between the various channels on large water bodies are often difficult to represent even with sophisticated models. In view of this, an attempt has been made to apply a short-term stochastic forecasting model-an Auto Regressive Integrated Moving Average (ARIMA) aided by Artificial Neural Networks (ANNs) to partition flows into the downstream tributaries, viz.: Lopis and Gadikwe channels from the Khiandiandavhu-Maunachira (K-M) Junction Junction (the main river channel) river system of the iconic Okavango delta in Botswana. As such, observed monthly flow data between October 2005 and September 2008 at the K-M Junction, and the two downstream tributaries were used to test the performance of these hybrid models for the complex deltaic system. It was found that the partitioned flows at Lopis and Gadikwe agree very well with observations when using a Single Input Multiple Output (SIMO) ANN (i.e. an inverse variant of the widely used Multi Input Single Output (MISO) ANN architecture) and an ARIMA (1,1,1) model. The Mean Squared Errors (MSEs) in the forecasts were also minimal, thus giving some hope on the use of such a hybrid mode for the rest of the branched river networks of the whole Okavango delta.</p>


Author(s):  
D., A., L., A. Putri

Tectonic activity in an area could result in various impacts such as changes in elevation, level of slope percentages, river flow patterns and systems, and the formation of geological structures both locally and regionally, which will form a new landscape. The tectonic activity also affects the stratigraphic sequences of the area. Therefore, it is necessary to study morphotectonic or landscape forms that are influenced by active tectonic activities, both those occur recently and in the past. These geological results help provide information of the potential of natural resources in and around Tanjung Bungo area. Morphological data are based on three main aspects including morphogenesis, morphometry, and morphography. The data are collected in two ways, the first is field survey by directly observing and taking field data such as measuring geological structures, rock positions, and outcrop profiles. The second way is to interpret them through Digital Elevation Model (DEM) and aerial photographs by analyzing river flow patterns and lineament analysis. The field measurement data are processed using WinTensor, Dips, and SedLog Software. The supporting data such as Topographic Maps, Morphological Elevation Maps, Slope Maps, Flow Pattern Maps, and Lineament Maps are based on DEM data and are processed using ArcGis Software 10.6.1 and PCI Geomatica. Morphotectonically, the Tanjung Bungo area is at a moderate to high-class level of tectonic activity taken place actively resulted in several joints, faults, and folds. The formation of geological structures has affected the morphological conditions of the area as seen from the development of steep slopes, structural flow patterns such as radial, rectangular, and dendritic, as well as illustrated by rough surface relief in Tanjung Bungo area. This area has the potential for oil and gas resources as indicated by the Telisa Formation, consisting of calcareous silts rich in planktonic and benthonic fossils, which may be source rocks and its contact with the Menggala Formation which is braided river system deposits that could be good reservoirs. Further research needs to be done since current research is only an interpretation of surface data. Current natural resources being exploited in Tanjung Bungo region are coals. The coals have thicknesses of 5-7 cm and are classified as bituminous coals.


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