IRJET- Estimation of Water Level Variations in Dams Based on Rainfall Data using ANN

INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056
VOLUME: 06 ISSUE: 04 | APR 2019 WWW.IRJET.NET P-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 3227
Estimation of Water Level Variations in Dams based on Rainfall Data
using ANN
Ankita Bhapkar1, Sumit Bante2, Shubham Deshmukh3, Mr.Rajesh Shekokar4
1,2,3Student, Department of Electronics and Telecommunication, RMD Sinhgad school of Engineering,
Warje, Pune-58
Assistant Professor4, Department of Electronics and Telecommunication, RMD Sinhgad school of Engineering,
Warje, Pune-58
-------------------------------------------------------------------------***------------------------------------------------------------------------
Abstract:- A method for estimating water level at Sungai Bedup in Sarawak is presented here. The method makes use of
Artificial Neural Network (ANN) – a new tool that is capable of modeling various nonlinear hydrological processes. ANN was
chosen based on its ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. In this
study, the networks were developed to forecast daily water level for Sungai Bedup station. Specially designed networks were
simulated using data obtained from Drainage and Irrigation Department with MATLAB 6.5 computer software. Various
training parameters were considered to achieve the best result. ANN Recurrent Network using Backpropagation algorithm
was adopted for this study.
Keywords: Artificial Neural Network, Water Level Prediction, Flood Forecasting.
1. INTRODUCTION
Rainfall brings the most important role in the matter of human life in all kinds of weather happenings. The effect of rainfall
for human civilization is very colossal. Rainfall is natural climatic phenomena whose prediction is challenging and
demanding. Accurate information on rainfall is essential for the planning and management of water resources and also
crucial for reservoir operation and flooding prevention. Additionally, rainfall has a strong influence on traffic, sewer
systems and other human activities in the urban areas. Nevertheless, rainfall is one of the most complex and difficult
elements of the hydrology cycle to understand and to model due to the complexity of the atmospheric processes that
generate rainfall and the tremendous range of variation over a wide range of scales both in space and time. Thus, accurate
rainfall prediction is one of the greatest challenges in operational hydrology, despite many advances in weather
forecasting in recent decades. Rainfall means crops; and crop means life. Rainfall prediction is closely related to agriculture
sector, which contributes significantly to the economy of the nation.
On a worldwide scale, large numbers of attempts have been made by different researchers to predict rainfall accurately
using various techniques. But due to the nonlinear nature of rainfall, prediction accuracy obtained by these techniques is
still below the satisfactory level. Artificial neural network algorithm becomes an attractive inductive approach in rainfall
prediction owing to their highly nonlinearity, flexibility.[1]
1.1. Artificial Neural Network (ANN)
The development of Artificial Neural Networks began approximately 50 years ago, inspired by a desire to understand the
human brain and emulate its functioning. Within the last two decades, it has experienced a huge resurgence due to the
development of more sophisticated algorithms and the emergence of powerful computation tools. It has been proved that
ANN models show better results in river stage-discharge modeling in comparison to traditional models. The human brain
always stores the information as a pattern. Any capability of the brain may be viewed as a pattern recognition task.
The high efficiency and speed with which the human brain processes the patterns inspired the development of ANN and its
application in field of pattern recognition. ANN is a computing model that tries to mimic the human brain and the nervous
system in a very primitive way to emulate the capabilities of the human being in a very limited sense. ANNs have been
developed as a generalization of mathematical models of human cognition or neural biology. Comparison to a conventional
statistical stage-discharge model shows the superiority of an approach.
using ANN. Basic principle of ANN is shown in Fig. 1.1
Enlargement of ANN is based on the following rules:
1. Information processing occurs at nodes that are single elements and are also denoted as units, neurons or cells.
2. Signals are passed between nodes through connection links.

3. Each connection link has an associated weight that represents its connection strength.
4. Each node typically applies a nonlinear transformation called an activation function to its net input to determine
its output signal.[2]
Fig.1 Working of ANN
1.2 FLOW DIAGRAM
The above figure is the basic flow diagram of our system. The previous year rainfall data will be fetched from the database.
This data will be in the form of cell array. First of all the cell array will be converted into mat array. After that, this data will
be send for training. Various input parameters are taken into consideration. Out of them six input parameters have been
considered in our system namely- Initial storage, Daily inflow, West and East Diversion, Power generation 1 and 2.
These input parameters will be send for testing. The tested input and target input will be compared to obtain output. This
predicted output will be the future water level.
2. STUDY AREA
The study area isSukhi Reservoir project, it is a part of the major Water Resources projects envisaged, constructed and
developed by Govt. of Gujarat at the confluence of river Sukhi and Bharaj river near village Sagadhra and Khos in
LoadData
TrainData
Input
Parameters
TestData
Predicted
Output

Pavijetpur and Chhota-UdepurTaluka of Vadodara District in Gujarat State, India and it lies between longitudes of 73˚ 53’
00” E and latitudes of 22˚ 26’ 00” N. The location of Sukhi Reservoir is shown in Fig. 2.
The region experiences a typical semi-arid climate (aridity index 15-20%); the average annual rainfall is 700-1000 mm
with 30 to 45 rainy days and has dependability of 50-60%. Mean annual temperature is 26-270 ˚C with mean maximum
and minimum temperatures of 410 ˚C and 110 ˚C respectively and the range of extremes is 460 ˚C to 500 ˚C. The average
annual wind speed is 5 to 15km/hr. Interestingly a pocket in Panchmahals experiences, a high wind velocity of more than
15 km/hr. Winds blow from West and South-West for most part of the year. The average relative humidity is 60-65 %. The
potential evapotranspiration is about 2250 mm. The Climate of Vadodara station is semi-arid with fairly dry and hot
summer. Winter is fairly cold and sets in, the month of November and continues till the middle of February. Summer is hot
and dry which commences from mid of February and ends by the month of June. Monsoon sets in around end of June to
mid-July.[3]
3. METHODOLOGY
The whole data set comprises of total of 23 years, out of which 16 years data was used for training and rest of the 07 years
of were used for validating the models. Forecasting was done by evaluating the model fitness and performance in 10 days
ahead forecasting. The desired outputs were the Water levels corresponding to the 10 days ahead forecast. Inputs were
feature group elements of inflow, water level and release. The testing patterns are used for evaluating accuracy of the ANN
trained model that is developed using ANN. Several patterns of input data have been employed to develop the optimum
ANN model for the reservoir Water level. Three different NN models; Cascade, Elman and Feedforward back propagation
were developed. A three-layered architecture with 10 neurons in the hidden layer and one neuron in the output layer was
adapted for all the models after trial and error. The number of neurons in the input layer varied with the number of
elements in the input vector for each model. Three elements from each feature group were joined to the input vector
elements by the relation given in equation (1). = ܰ∑ ௞ ௞ୀ ଵ ………….(1) Where, N is the number of elements in a data set
vector; k is the number of feature groups; Ni is the number of elements per feature group. First, real time information has
got to be inheritable from reservoirs through the file based mostly date transfer system like SFTP or transfer over by emails
manually. It includes daily information should also be added along with historical information over amount of time into the
database. Behavioural research and end-use analysis require not just information but also the context for energy use. These
slow dynamical datasets should be updated regularly, and also the modification of sources over time should be done
themselves. As a result, an automatic information ingest system has got different sizes as well as rates to support discrete
information acquisition, and be approachable for necessities and different information sources. Data inherited is kept into
the temporary storage and afterwards has got to be keep and shared with comparatively large system. To researchers
mining and exploring information for correlations and gaining information is most vital.[4] The information collaboration
has got to be balanced against the considerations of knowledge security. Data-driven forecasting models are essential and
for it data-driven models are trained using the existing data, and utilize large-scale options that are direct and indirect
indicators of water levels. In particular, our demand foretelling models use ANN time-series to supply high accuracy, uses
the feedforward back propagation for playacting such analytics. The greatest advantage of data-driven models is that the
convenience of automatically building a model without having deep technical information on the system. The models also
can be easily well-kept up to now by grooming them over new data that is collected. Further, it permits data analysts to try
completely different combos of options to find those that most significantly impact the water levels. This can help to outline

the limits for data collection, or alternatively can provide us the ways to reduce usage of water. This tends to noticethat
there's no one size fits alluniversal model, and a set of models are used for various functions, however the results of
exploitation ANN model provides most as regards to actual results. The information of the water level, inflow and release is
collected.The level of reservoir is plotted for the info collected for the past years and to see the patterns and behaviour. Fig.
3 and Fig. 4 shows observed water level for training and validation period. It is observed that the level volume unit are less
consistent across some of the years.[5]
4. RESULT
The most important part of ANN model is its ability to forecast future events accurately. This Water level forecasting is an
essential topic in water management affecting reservoir operations and effective multipurpose water storage. The statistical
evaluation criteria used in the present study are Root Mean Square Error (RMSE), Correlation Coefficient (R) Coefficient of
variation (R 2 ) Coefficient of Determination (D). Table 1 shows the values of evaluation parameters for training set of data.
After observing the evaluation parameters, it is observed that Feed Forward Back propagation gives the best output having
RMSE; 0.92, Correlation Coefficient, R ; 0.97, Coefficient of Determination, R 2 ; 0.95 and Discrepancy Ratio, D ; 1.00. The
ANN model using Feed Forward Back Propagation predicts the water levels that measure very close or near to the actual
levels, as depicted in the form of graph in Fig 5 and Fig.6. Less variation measure seen with the forecasted and that of the
actual provides the closeness of predication to the actual values.[6]

The forecast accuracy is validated on the remaining 30% of the data by evaluating the following statistic performance
indicators: Root Mean Square Error (RMSE), Correlation Coefficient (R), Coefficient of determination (R 2 ) and Discrepancy
Ratio (D) described in Table 2
Fig. 7 shows the variation of water levels between the forecasted and that of the actually observed.

5. CONCLUSION
The neural networks (NN) models developed in this study were able to forecast the water levels of Sukhi Reservoir for ten
daily consecutive days beginning after a given day and given data for ten consecutive days prior to that day. Thus, NN
provide an effective and timely method for forecasting water levels in the reservoir. This can help in water-use
formulation and scheduling for domestic, agricultural and municipal uses. Timely forecasting can also help in disaster
monitoring, response and control in areas prone to floods. For power generation, effective and timely reservoir level
forecasting can help in predicting power loads and management of power generation for efficiency and optimisation. The
number of feature groups and the number of elements in each feature group used as inputs greatlyinfluence the ability of
NN to forecast reservoir levels accurately. The main conclusions obtained are as below: 1. Soft computing technique like
ANN is reliable and more accurate than conventional methods. 2. On the basis of performance evaluation of models, ANN
model using Feed Forward Back Propagation gave best results among three developed ANN models. 3. This paper also
demonstrates that ANN technique give good results for more number of inputs. Feed Forward Back propagation gives the
best output having RMSE; 0.92, Correlation Coefficient, R;0.97, Coefficient of Determination, R 2; 0.95 and Discrepancy
Ratio, D;1.00 in comparison of Cascade which gives the output having RMSE; 0.73, Correlation Coefficient, R; 0.98,
Coefficient of Determination, R 2; 0.96 and Discrepancy Ratio, D; 1.00 and Elman which gives the output having RMSE;0.81,
Correlation Coefficient, R;0.98, Coefficient of Determination, R 2 ; 0.95 and Discrepancy Ratio, D; 1.00. Based on these
results, it can be concluded that amongst the three methods used for this study, ANN using Feed Forward Backpropagation
is an appropriate predictor for real-time Water Level forecasting of Sukhi Reservoir Project.
6. REFERENCES
[1] ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (April 2000). Artificial Neural
Networks in Hydrology. I: Preliminary Concepts. Journal of Hydrologic Engineering, 5(2): 115-123
[2] S. Haykin, 1999: Neural Networks- A Comprehensive Foundation. Addison Wesley Longman.
[3] Bisht D. C. S., M. M. Raju, M. C. Joshi, “ANN based river stage-Discharge modelling for Godavari river, India”, Computer
Modelling and New Technologies, vol. 14, no. 3, pp. 48-62, 2010.
[4] Golabi M, Radmanesh F,Akhondali A and Kashefipoor M., “Prediction of Seasonal Precipitation Using Artificial Neural
Networks (Case Study: Selected Stations of (Iran) Khozestan Province), ISSN 2090-4304, Journal of Basic and Applied
Scientific Research”, 2013.
[5] Jain S. K. and Chalisgaonkar , “Setting up stage-discharge relations using ANN”, Journal of Hydrologic Engg., vol. 5, no. 4,
pp. 428-433.[6] Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. Time Series Analysis Forecasting and Control. McGraw-Hill Inc.,
USA, 1994.
[6] Mahmood, Mustafa and Muhammed, “Application of Artificial Neural Networks to Forecast theRelease Water from
Haditha Dam”, Special Issue of Engineering and Development Journal ISSN 1813-7822, 2012.

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