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Monitoring and Predictive Estimations of Atmospheric Parameters in the Catchment Area of Lake Baikal
2021
Atmosphere
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inflow into the lake. ...
The paper is concerned with a methodological approach to monitoring the state of atmospheric parameters in the catchment area of Lake Baikal, including real-time analysis of actual distributed data with ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/atmos13010049
fatcat:rki27ify4ffwnekbqekzjptbna
Generalized versus non-generalized neural network model for multi-lead inflow forecasting at Aswan High Dam
2011
Hydrology and Earth System Sciences
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Real inflow data collected over the last 130 years at Lake Nasser was used to train, test and validate the proposed model. ...
Using Generalized Neural Network (GNN) helped avoid over-fitting of training data which was observed as a limitation of classical ANN models. ...
Support from the former chairperson of the National Water Research Center, Egypt, Mona El-Kady is highly appreciated. Edited by: H. Cloke ...
doi:10.5194/hess-15-841-2011
fatcat:hchofr2gonfxvduj7fngrnqh2e
Modeling of monthly rainfall and runoff of Urmia lake basin using "feed-forward neural network" and "time series analysis" model
2014
Water Resources and Industry
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There are various methods for time-series based forecasting; in the presented study Feed-forward Neural Network and Autocorrelation Regressive Integrated Moving Average (ARIMA) models were applied to forecast ...
The results showed that the estimated values of monthly rainfall through Feed-forward NN were close to ARIMA model with coefficient of correlation 0.62 and the root mean square error of 12.43 mm over the ...
The LMBP algorithm is used to train the proposed network model [12] . In this research neural network with LMBP was applied for prediction of monthly rainfall using MATLAB software. ...
doi:10.1016/j.wri.2014.10.003
fatcat:nns2zzv3z5cuvl7ok25mpch2mm
Predicting the Water Level Fluctuation in an Alpine Lake Using Physically Based, Artificial Neural Network, and Time Series Forecasting Models
2015
Mathematical Problems in Engineering
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This study utilizes four model approaches to predict water levels in the Yuan-Yang Lake (YYL) in Taiwan: a three-dimensional hydrodynamic model, an artificial neural network (ANN) model (back propagation ...
neural network, BPNN), a time series forecasting (autoregressive moving average with exogenous inputs, ARMAX) model, and a combined hydrodynamic and ANN model. ...
Acknowledgments This research was conducted with the support of the National Science Council and Academia Sinica, Taiwan, Grants nos. 101-2625-M-239-001 and AS-103-TP-B15, respectively. ...
doi:10.1155/2015/708204
fatcat:5nvtew37nbbsvahpfnq3l55rle
Generation of prognostic interval estimates of water inflows to hydroelectric reservoirs using multiparametric neural network
2021
E3S Web of Conferences
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The article discusses the practical application of the neural network for hydropower and water management systems. ...
Various models of neural networks are understood, their advantages and disadvantages for a particular subject area. ...
Fig. 2 Example of formation of regions with significant correlation coefficients for useful inflow to Lake Baikal and swirl indices for July with advance equal to zero on atmosphere layer 500 GPa Among ...
doi:10.1051/e3sconf/202128901003
fatcat:onz5q6wtkvc5rmkxkpyzc4nqfq
Prognostic Maps of Climatic Indicators Based on a Multivariate Neural Network
2022
Energy Systems Research
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The paper is concerned with the construction of maps of climatic indicators using a multivariate neural network. ...
An alternative neural model is considered within the framework of the multivariate neural network evolution. ...
FWEU-2021-0003. reg. number AAAA-A21-121012090014-5 of the Fundamental Research Program of the Russian Federation 2021-2025. ...
doi:10.38028/esr.2022.04.0003
fatcat:5yrzlgkq5zd6narddtsppygdsa
Comparison of ANN, Fuzzy Logic and Regression Tree Modelsfor Reservoir Inflow Forecasting
2022
Zenodo
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The present study demonstrates the capability of three forecasting techniques namely Artificial Neural Network consisting of two layer feed forward neural network with back propagation algorithm, Mamdani ...
type Fuzzy Model and Regression Tree Method consisting of coarse, medium and fine tree models applied in the prediction of weekly inflow values of the Deer Creek Reservoir in Utah, United States. ...
This was done by analyzing the actual inflow versus the predicted inflow plot obtained for the different inputs. The closer he points are with the line of fit, the more accurate is the forecast. ...
doi:10.5281/zenodo.6218805
fatcat:42xsdixu5jc47iu4nxv5xswd5m
Long range lake water level estimation using artificial intelligence methods
2020
e-zbornik. Elektronički zbornik radova Građevinskog fakulteta
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This paper covers the estimation of the water levels of Beysehir Lake, located in middle of Turkey, using the artificial intelligence (AI) such as the neural networks (NN) and the fuzzy logic (FL). ...
It is seen that the Generalized Regression Neural Network (GRNN) showed relatively superior performance compared with the other two artificial neural networks, i.e. the Radial Basis Function (RBF) and ...
ARTIFICIAL INTELLIGENCE METHODS In this study three artificial neural networks methods and adaptive network based fuzzy method are used to forecast the water level of Lake Beysehir. ...
doi:10.47960/2232-9080.2020.20.10.1
fatcat:dfeit5bzizfstefdcf5lbqn5oe
Forecasting Reservoir Water Level-A Case Study of Bhadar-1 using Artificial Neural Network
2018
International Journal for Research in Applied Science and Engineering Technology
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So the forecasting of the reservoir level is very useful for the flood management. The aim of this research work is to forecast the BHADAR-1 reservoir water level using Artificial Neural Network. ...
Reservoir water level depends on the rainfall, inflow and outflow. Rainfall is mainly affect on the increases and decreases of the reservoir water level and inflow. ...
Therefore, MODEL-2 developed, to obtain a next day water level of reservoir using inflow predicted by ANN. ...
doi:10.22214/ijraset.2018.4690
fatcat:hme4tuliqrda5azq44u2o5orhm
Modelling of Hydropower Reservoir Variables for Energy Generation: Neural Network Approach
2013
Ethiopian Journal of Environmental Studies and Management
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The neural network summary yielded a good forecast for Kainji and Jebba hydropower reservoirs with correlation coefficients of 0.89 and 0.77 respectively. ...
These values of the correlation coefficient showed that the networks are reliable for modeling energy generation as a function of reservoir variables for future energy prediction. ...
The author would also like to thank the Department of Civil Engineering, University of Ilorin for providing technical support. ...
doi:10.4314/ejesm.v6i3.12
fatcat:7oba7oyrprgdxh23oefcxva3ty
Prediction of Fish Yields in Lakes and Reservoirs from simple Empirical Models using Artificial Neural Network (ANN) : An Review
2019
International Journal of Scientific Research in Computer Science Engineering and Information Technology
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Many mathematical or applied mathematics and Artificial Neural Networks models were developed to predict fish production forecast of reservoirs. ...
Accurate modelling to predict fish yield of the reservoirs and lakes helps to understand behaviour of the system and managers can formulate appropriate management practices to improve fish yield. ...
., & She, N. (2007) predict reservoir water quality using Artificial Neural Network with back-propagation algorithm. ...
doi:10.32628/cseit195110
fatcat:yy7b3l7z4vgazb5lae3nuhh25a
Prediction of groundwater levels from lake levels and climate data using ann approach
2019
Water S.A
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As a case study an artificial neural network (ANN) methodology is developed for estimating the groundwater levels (upper Floridan aquifer levels) as a function of monthly averaged precipitation, evaporation ...
In a lake groundwater system that is typical of many karst lakes in Florida, a large part of the groundwater outflow occurs by means of vertical leakage through the underlying confining unit to a deeper ...
on the hidden layers of the feed forward neural network. ...
doi:10.4314/wsa.v34i2.183640
fatcat:whmjfrce2zhorjydizs4uxfcsa
Wetland Water-Level Prediction in the Context of Machine-Learning Techniques: Where Do We Stand?
2023
Environments
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The state-of-the-art literature summarizes that artificial neural networks (ANNs) are some of the most effective tools in wetland water-level prediction. ...
Wetlands are simply areas that are fully or partially saturated with water. Not much attention has been given to wetlands in the past, due to the unawareness of their value to the general public. ...
Acknowledgments: The authors would like to thank Ranjan Sarukkalige from Curtin University, Australia and Nadeeshani Nanayakkara from the University of Peradeniya, Sri Lanka for their initial guidance ...
doi:10.3390/environments10050075
fatcat:f67efinb6jccviy57gl6js4fnm
A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting
2021
Alexandria Engineering Journal
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This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. ...
along with the most common optimization techniques. ...
Artificial Neural Networks (ANNs) Back Propagation Neural Network (BPNN) Multilayer Perceptron (MLP) Recurrent Neural Network (RNN) Generalized Regression Neural Network (GRNN) Convolutional Neural Network ...
doi:10.1016/j.aej.2021.04.100
fatcat:fppujuentje67dekhgnfe5qt3y
Optimal Reservoir Operations using Long Short-Term Memory Network
[article]
2021
arXiv
pre-print
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A reliable forecast of inflows to the reservoir is a key factor in the optimal operation of reservoirs. ...
Real-time inflow forecast, in other words, daily inflow at the reservoir helps in efficient operation of water resources. ...
of temporal neural networks for reservoir operations. ...
arXiv:2109.04255v1
fatcat:m7wbcaj4vngh7f6lm4x2kjh4lm
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