scholarly journals Application of artificial neural network for predicting water levels in Hooghly estuary, India

2020 ◽  
Vol 3 (1) ◽  
pp. 401-415 ◽  
Author(s):  
Kalyan Kumar Bhar ◽  
Susmita Bakshi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Tidal Cycle ◽  
Water Levels ◽  
Hooghly Estuary ◽  
Tidal Water ◽  
Level Data ◽  
Water Level Data ◽  
Artificial Neural

Abstract Hydrodynamic models for morphodynamic studies in estuaries require continuous tidal water level data as boundary conditions. However, for the Hooghly estuary in India, measurement of continuous tidal water elevation data at the most downstream point is a very difficult task because of the remote location and the confluence with the deep sea. The tidal water level data at this station are measured for a half tidal cycle which is not useful for hydrodynamic modeling. However, at other upstream stations, tide water level data are measured continuously. Accordingly, in this study, an attempt is made to generate continuous tidal water level data at the remote station, using the data of the neighboring stations as input to an artificial neural network (ANN) model. A three-layered feed-forward backpropagation (FFBP) network with two hidden layers is selected and five different combinations of input vectors are used. Simulated water level data obtained from each model are compared with the observed data graphically as well as by estimating the standard error parameters. The best model suitable for prediction of continuous tidal elevation during any time of the tidal cycle and applicable throughout the year is then identified. It is found that tidal data from the nearest neighboring station are more suitable for training.

scholarly journals MODEL PREDIKSI LEVEL AIR DI LAHAN PERKEBUNAN KELAPA SAWIT DENGAN JARINGAN SARAF TIRUAN BERDASARKAN PENGUKURAN SENSOR RAIN GAUGE DAN ULTRASONIK

2021 ◽  
Vol 10 (1) ◽  
pp. 104
Author(s):  
Hasan Al Banna ◽  
Bayu Dwi Apri Nugroho
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Water Level ◽  
Rain Gauge ◽  
Water Levels ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Artificial Neural ◽  
Water Level Prediction

Monitoring and regulating water levels in oil palm swamps has an essential role in providing sufficient water for crops and conserving the land to not easily or quickly deteriorate. Presently, water level is still manual and has weaknesses, one of which is the accuracy of the data taken depending on the observer. Technology such as sensors integrated with artificial neural network is expected to observe and regulate water levels. This study aims to build a prediction model of water levels in oil palm plantations with artificial neural networks based on the rain gauge and ultrasonic sensors installed on Automatic Weather Station (AWS). The obtained results showed that the prediction model runs well with an R2 value of 0,994 and RMSE 1,16 cm. The water level prediction model in this research then tested for accuracy to prove the model's success rate. Testing the water level prediction model's accuracy in the dry season obtained an R2 value of 0,96 and an RMSE of 1,99 cm. Testing the water level prediction model's accuracy in the rainy season obtained an R2 value of 0,85 and an RMSE value of 4,2 cm. Keywords : artificial neural network, automatic weather station, palm oil, water level

scholarly journals Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower system

RBRH ◽  
2021 ◽  
Vol 26 ◽  
Author(s):  
João Paulo Lyra Fialho Brêda ◽  
Rodrigo Cauduro Dias de Paiva ◽  
Olavo Corrêa Pedrollo ◽  
Otávio Augusto Passaia ◽  
Walter Collischonn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Balance ◽  
Water Level ◽  
Integrated Approach ◽  
Water Levels ◽  
Ann Model ◽  
Storage Reservoirs ◽  
Ann Models ◽  
Artificial Neural

ABSTRACT Reservoirs considerably affect river streamflow and need to be accurately represented in environmental impact studies. Modeling reservoir outflow represents a challenge to hydrological studies since reservoir operations vary with flood risk, economic and demand aspects. The Brazilian Interconnected Energy System (SIN) is an example of a unique and complex system of coordinated operation composed by more than 160 large reservoirs. We proposed and evaluated an integrated approach to simulate daily outflows from most of the SIN reservoirs (138) using an Artificial Neural Network (ANN) model, distinguishing run-of-the-river and storage reservoirs and testing cases whether outflow and level data were available as input. Also, we investigated the influence of the proposed input features (14) on the simulated outflow, related to reservoir water balance, seasonality, and demand. As a result, we verified that the outputs of the ANN model were mainly influenced by local water balance variables, such as the reservoir inflow of the present day and outflow of the day before. However, other features such as the water level of 4 large reservoirs that represent different regions of the country, which infers about hydropower demand through water availability, seemed to influence to some extent reservoirs outflow estimates. This result indicates advantages in using an integrated approach rather than looking at each reservoir individually. In terms of data availability, it was tested scenarios with (WITH_Qout) and without (NO_Qout and SIM_Qout) observed outflow and water level as input features to the ANN model. The NO_Qout model is trained without outflow and water level while the SIM_Qout model is trained with all input features, but it is fed with simulated outflows and water levels rather than observations. These 3 ANN models were compared with two simple benchmarks: outflow is equal to the outflow of the day before (STEADY) and the outflow is equal to the inflow of the same day (INFLOW). For run-of-the-river reservoirs, an ANN model is not necessary as outflow is virtually equal to inflow. For storage reservoirs, the ANN estimates reached median Nash-Sutcliffe efficiencies (NSE) of 0.91, 0.77 and 0.68 for WITH_, NO_ and SIM_Qout respectively, compared to a median NSE of 0.81 and 0.29 for the STEADY and INFLOW benchmarks respectively. In conclusion, the ANN models presented satisfactory performances: when outflow observations are available, WITH_Qout model outperforms STEADY; otherwise, NO_Qout and SIM_Qout models outperform INFLOW.

scholarly journals Intelligent Data Analysis for Forecasting Threats in Complex Distributed Systems

2020 ◽  
pp. paper79-1-paper79-12
Author(s):  
Evgeny Palchevsky ◽  
Olga Khristodulo ◽  
Sergey Pavlov ◽  
Artur Kalimgulov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distributed Systems ◽  
Water Level ◽  
Historical Data ◽  
Mathematical Formulation ◽  
Prediction Method ◽  
Water Levels ◽  
Average Deviation ◽  
Artificial Neural

A threat prediction method based on the intellectual analysis of historical data in complex distributed systems (CDS) is proposed. The relevance of the chosen research topic in terms of considering the flood as a physical process of raising the water level, which is measured at stationary and automatic hydrological posts, is substantiated. Based on this, a mathematical formulation of the problem is formulated, within the framework of which an artificial neural network based on the freely distributed TensorFlow software library is implemented. The analysis of the effectiveness of the implemented artificial neural network was carried out, according to which the average deviation of the predicted water level when forecasting for one day at a stationary hydrological post was 3.323%. For further research on forecasting water levels, an algorithm is proposed for evaluating historical data at automatic posts, which will allow using these data to predict water levels according to the proposed method and at automatic posts. Thus, the neural network allows predicting the flood situation with acceptable accuracy, which allows special services to take measures to counter this threat.

scholarly journals Water level prediction by artificial neural network in a flashy transboundary river of Bangladesh

2014 ◽  
Vol 16 (2) ◽  
pp. 432-444 ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Climatic Factors ◽  
Water Levels ◽  
Training Procedure ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Hidden Neurons ◽  
Water Level Prediction

<p>This paper presents the sensitivity analysis results of feed forward multilayer perceptron based Artificial Neural Network model for water level prediction in a data constraint transnational Surma River of Bangladesh. Catchment characteristics, hydro-geomorphological, meteorological and headwater information of the upper catchment area are not available to the authors. As such past daily total rainfall and water levels data available within the country are utilized in this study. Logistic sigmoid activation function with unit steepness parameter is exercised for non-linear transformations in both hidden and output layers. Synaptic weights are adjusted using modified delta rule through error back propagation algorithm. Batch mode of training is adopted for global error minimization. Finally, statistical indicators are used to evaluate the prediction performance of the neural network. The model is then applied to predict water levels with twenty four and forty eight hours lead time.</p> <div> <p>It is found that a single hidden layer with two hidden neurons are adequate to train the network. A higher number of hidden neurons is speeding up the training procedure, but with an unacceptable generalization for the application. The authors have successfully created a model that recognizes the intricate pattern of water levels, without having the spatially distributed geomorphic characteristics of the watershed and the time-series of the climatic factors.</p> </div> <p>&nbsp;</p>

Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network 

2021 ◽  
Author(s):  
Sascha Flaig ◽  
Timothy Praditia ◽  
Alexander Kissinger ◽  
Ulrich Lang ◽  
Sergey Oladyshkin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Meteorological Data ◽  
Water Levels ◽  
Typical Structure ◽  
Water Abstraction ◽  
Physical Knowledge ◽  
Artificial Neural ◽  
The Impact

&lt;p&gt;In order to prevent possible negative impacts of water abstraction in an ecologically sensitive moor south of Munich (Germany), a &amp;#8220;predictive control&amp;#8221; scheme is in place. We design an artificial neural network (ANN) to provide predictions of moor water levels and to separate hydrological from anthropogenic effects. As the moor is a dynamic system, we adopt the &amp;#8222;Long short-term memory&amp;#8220; architecture.&lt;/p&gt;&lt;p&gt;To find the best LSTM setup, we train, test and compare LSTMs with two different structures: (1) the non-recurrent one-to-one structure, where the series of inputs are accumulated and fed into the LSTM; and (2) the recurrent many-to-many structure, where inputs gradually enter the LSTM (including LSTM forecasts from previous forecast time steps). The outputs of our LSTMs then feed into a readout layer that converts the hidden states into water level predictions. We hypothesize that the recurrent structure is the better structure because it better resembles the typical structure of differential equations for dynamic systems, as they would usually be used for hydro(geo)logical systems. We evaluate the comparison with the mean squared error as test metric, and conclude that the recurrent many-to-many LSTM performs better for the analyzed complex situations. It also produces plausible predictions with reasonable accuracy for seven days prediction horizon.&lt;/p&gt;&lt;p&gt;Furthermore, we analyze the impact of preprocessing meteorological data to evapotranspiration data using typical ETA models. Inserting knowledge into the LSTM in the form of ETA models (rather than implicitly having the LSTM learn the ETA relations) leads to superior prediction results. This finding aligns well with current ideas on physically-inspired machine learning.&lt;/p&gt;&lt;p&gt;As an additional validation step, we investigate whether our ANN is able to correctly identify both anthropogenic and natural influences and their interaction. To this end, we investigate two comparable pumping events under different meteorological conditions. Results indicate that all individual and combined influences of input parameters on water levels can be represented well. The neural networks recognize correctly that the predominant precipitation and lower evapotranspiration during one pumping event leads to a lower decrease of the hydrograph.&lt;/p&gt;&lt;p&gt;To further demonstrate the capability of the trained neural network, scenarios of pumping events are created and simulated.&lt;/p&gt;&lt;p&gt;In conclusion, we show that more robust and accurate predictions of moor water levels can be obtained if available physical knowledge of the modeled system is used to design and train the neural network. The artificial neural network can be a useful instrument to assess the impact of water abstraction by quantifying the anthropogenic influence.&lt;/p&gt;

scholarly journals Study of Water Level-Discharge Relationship Using Artificial Neural Network (ANN) in Sungai Gumum, Tasik Chini Pahang Malaysia

2010 ◽  
Vol 5 (1) ◽  
pp. 20-26 ◽  
Author(s):  
Othman Jaafar ◽  
Mohd Ekhwan Hj. Toriman ◽  
Mushrifah Hj. Idris ◽  
S.A. Sharifah M ◽  
Hafizan Hj. Juahir ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Tasik Chini ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals Artificial Neural Network Model for Estimation of Suspended Sediment Load in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 3701-3705
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Suspended Sediment ◽  
River Basin ◽  
Flood Control ◽  
Sediment Load ◽  
Water Discharge ◽  
Suspended Sediment Load ◽  
Artificial Neural

The correct assessment of amount of sediment during design, management and operation of water resources projects is very important. Efficiency of dam has been reduced due to sedimentation which is built for flood control, irrigation, power generation etc. There are traditional methods for the estimation of sediment are available but these cannot provide the accurate results because of involvement of very complex variables and processes. One of the best suitable artificial intelligence technique for modeling this phenomenon is artificial neural network (ANN). In the current study ANN techniques used for simulation monthly suspended sediment load at Vijayawada gauging station in Krishna river basin, Andhra Pradesh, India. Trial & error method were used during the optimization of parameters that are involved in this model. Estimation of suspended sediment load (SSL) is done using water discharge and water level data as inputs. The water discharge, water level and sediment load is collected from January 1966 to December 2005. This approach is used for modelled the SSL. By considering the results, ANN has the satisfactory performance and more accurate results in the simulation of monthly SSL for the study location.

Dam Safety Reliability Analysis Based on Artificial Neural Network

2011 ◽  
Vol 255-260 ◽  
pp. 3620-3625
Author(s):  
Hai Wei ◽  
Hua Shu Yang ◽  
Liang Wu ◽  
Yue Gui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Reliability Index ◽  
Safety Evaluation ◽  
Mathematic Model ◽  
Dam Safety ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Artificial Neural

There are many factors, such as climate, flood, material, geology, structure, management, to influence dam safety. So dam safety evaluation, involving many fields, is very complicated, and very difficult to establish mathematic model for assessment. Artificial Neural Network (ANN) has many obvious advantages to deal with these problems influenced by multi-factor, consequently is widely used in engineering fields. This paper considered water level, temperature, main factors influencing dam deformation, as random variables, employed ANN and statistical model to establish performance function of dam hidden trouble deformation and abnormal deformation. Then reliability theory was used to analyze dam safety reliability and sensitivity. The results show that temperature has great effect on probability of dam hidden trouble deformation and abnormal deformation than reservoir water level, due to great variability of temperature. Change of Reliability index of dam is contrary to reservoir water level. Temperature, especially average temperature in 10 days and 5 days, has great effect on sensitivity of reliability index than water level.

Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm

2022 ◽  
pp. 1118-1129
Author(s):  
Nawaf N. Hamadneh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Water Level ◽  
Firefly Algorithm ◽  
Sea Water ◽  
Dead Sea ◽  
Water Levels ◽  
Artificial Neural ◽  
The Dead ◽  
Predicted Values

In this study, the performance of adaptive multilayer perceptron neural network (MLPNN) for predicting the Dead Sea water level is discussed. Firefly Algorithm (FFA), as an optimization algorithm is used for training the neural networks. To propose the MLPNN-FFA model, Dead Sea water levels over the period 1810–2005 are applied to train MLPNN. Statistical tests evaluate the accuracy of the hybrid MLPNN-FFA model. The predicted values of the proposed model were compared with the results obtained by another method. The results reveal that the artificial neural network (ANN) models exhibit high accuracy and reliability for the prediction of the Dead Sea water levels. The results also reveal that the Dead Sea water level would be around -450 until 2050.

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