IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Research articleerd

Event-Based Rainfall-Runoff Forecasting in Pampanga RiverBasin, Philippines using Artificial Neural Networks (ANN)JCOB C. MALAGUIT*
University of the Philippines Los Baños, Laguna, Philippines
Email: jcmalaguit@up.edu.ph

MARRIANE F. MAKAHIYA
University of the Philippines Los Baños, Laguna, Philippines

MARK LEXTER D. DE LARA
University of the Philippines Los Baños, Laguna, Philippines
Received 15 December 2016

Accepted 5 May 2017(*Corresponding Author)

Abstract This study developed a series of rainfall-runoff forecasting models that can be used indesigning the flood warning system around Pampanga River Basin. The data regarding rainfall
and water level of the river was obtained from the Hydrometeorological Division (HMD) ofPhilippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA).
Data were collected from 2014 and 2015 hourly water level reading and rainfall reading.
Feedforward Backpropagation Model, a variant of the Artificial Neural Network (ANN), wasused in the study along with Gradient Descent with Adaptive Learning Rate Algorithm as alearning technique for the network. MATLAB R2009b was used to train and design thenetworks. A total of 45 networks were trained. Results of the training gave reasonablepredictions for most of the stations with a minimum accuracy of 96%. Inaccuracy of training insome stations were attributed to the inconsistency in data and other factors.
Keywords Artificial Neural Networks, river basin, event-based forecasting,Feedforward Backpropagation, rainfall-runoff model

INTRODUCTION
Flooding has been one of the most destructive phenomena in the Philippines. Flood causes loss of lives,
damage to personal assets and real properties, and adverse economic, environment and agriculturalimpacts (Chen and Graham, 2005). In 2014, it was reported that 153 out of 324 natural disasters thatoccurred in the country were hydrological disasters (Guha-Sapir et al., 2015) and in the last decade, thecountry experienced frequent flooding due to continuous raining and typhoons brought about by its
archipelagic characteristic.
In Central Luzon, Philippines, one of the regions that have been affected by recent disastrous
events including Typhoon Nona (Typhoon Melor) and Lando (Typhoon Koppu), there situates thesecond largest river in Luzon region, the Pampanga River. It traverses the provinces of Pampanga,
Nueva Ecija, Tarlac and Bulacan. Pampanga-Agno river basin serves as the catch basin for Tarlac,
Nueva Ecija, Pampanga and Bulacan provinces. Severe flooding of this river can swallow the wholeprovince of Pampanga and its neighboring provinces. Pampanga River Basin is continuously being
monitored by Philippine Atmospheric Geophysical, Astronomical Services Administration (PAGASA)
since it is one of the major land reservoir that affects the region.
The use of conventional deterministic models in this case requires extensive data and costs since itconsiders various physical factors that are not only tedious to collect but are also complex in nature.
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Hence, the use of Artificial Neural Networks (ANN) attracted many researchers because of its simple
yet powerful architecture to deal with complex nature of problems without sacrificing the results. The
black-box nature of this model needs no requirement to fully understand the system in the aggregate(Brath and Toth, 2002). The model, given sufficient data, can be used as a reliable forecasting toolespecially in forecasting the flood in Pampanga River Basin.
The use of ANN models as a rainfall-runoff model proves to produce qualitative forecasts (Lekkaset al., 2005); and the rainfall level and actual stream flow (water level) can be actually used asparameters for the development of the model (Elsafi, 2014). This study focuses on the application ofANN as a rainfall-runoff model in Pampanga River Basin with rainfall level and streamflow level(water level) as its parameters.
OBJECTIVE
The objective of this research is to forecast the flooding of the Pampanga River Basin. This will help
develop a warning signal that will inform the residents living near the area ahead when to evacuate. In
this way, a network will be trained to determine the model that will give the reasonable relationshipbetween the water levels and rainfall in Pampanga River Basin specifically on how the rainfall levelcan forecast stream flow (water level) downstream.
METHODOLOGY
Study Site

Fig. 1 Hydrological map of Pampanga River Basin, PhilippinesⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

The Pampanga River Basin (Fig. 1), the 4th largest basin in the Philippines, has an aggregate area of
10,434 km2. About ninety five percent (95%) of the basin transcends the bounds of four provinces,namely Nueva Ecija, Tarlac, Pampanga and Bulacan. The remaining five percent is part of provinces
including Aurora, Zambales, Rizal, Quezon, Pangasinan, Bataan and Nueva Vizcaya.
Data Preparation
Rainfall and water level data of the Pampanga River Basin for the years 2014 and 2015 were gatheredfrom the Hydrometeorological Division (HMD) in Philippine Atmospheric Geophysical andAstronomical Services Administration (PAGASA). An hourly data from January 2014 to October 2015of the water level and rainfall reading was provided by the office. Since this study focuses on eventbased modelling, cumulative 24-hour rainfall level of the wet season of Philippines including June2014 – Dec 2014 and from June 2015 – October 2015 were considered.
In the data given, there were seventeen rainfall reading stations, but only thirteen stations wereconsidered since the data from other stations have problems in it including incomplete or erroneous
data. The rainfall data from these stations will serve as input data for the model. Nevertheless, for thewater level reading stations, there are ten of them but only nine stations were considered. These ninestations will represent the nine sets of models which will serve as basis for the clustering of the data set.
Data from these nine stations will represent the output/target data for the model. The input data is, then,normalized using the normalization formula intrinsic in the use of the MATLAB Neural Network
Toolbox given in Eq. (1).

p' 2p  min( p)1max(p)  min( p)(1)

In Eq. (1), p represents the original data before normalization, p' represents the normalized dataand; min(p) and max(p) represents the minimum and the maximum data in the set, respectively. The
data was split into two computational components: training set and validation set which will be used as
calibration for the model developed. Using MATLAB Neural Network Toolbox, the input data can berandomly classified into seventy percent (70%) for the training set and the thirty percent (30%) for thevalidation set.
Model Development and Implementation

Fig. 2 Network visualization of the architecture of ANNⒸ ISERD35IJERD – International Journal of Environmental and Rural Development (2017) 8-1

ANN’s are designed to mimic the behavior of a neuron inside a human brain while it sends information(Sarkar and Kumar, 2012; Gershenson, 2003; Zhang and Govindaru, 2000; Chakraborty et al., 1992). It
requires inputs (like neuron synapses) which are multiplied by numerical weights (strength ofrespective signals), and mathematical functions that determine the activation of the neuron. In the end,a function computes for the total output of an artificial neuron. In this study, a Feedforward
Backpropagation ANN Model was used with fixed network structure consisting of two hidden layers
and ten nodes each as shown in Fig. 2.
A hyperbolic tangent sigmoid (tansig) function is used in the problem for all the transfer (oractivation) functions in the model. The tansig function is given by Eq. (2).

tansig(n)211  e 2 n(2)Before the training process starts, several parameters were set. These values will be used in theprocess and is important to achieve the best possible model using the Mean Square Error (MSE)
criterion. Parameters (Table 1) are set accordingly before training the model.
Table 1 Values of the model parameters before the model training
Parameters
Maximum number of epochs to train
Maximum validation failures
Initial learning rate
Learning rate increase factor
Learning rate decrease factor
Maximum performance increase
Momentum
Minimum performance gradient
Epochs between displays

Values assigned
1000.00
1000.00
0.01
1.05
0.70
1.04
0.90
10-10
25.00

Values shown were the pre-set of MATLAB Neural Network Toolbox.

RESULTS AND DISCUSSION
Network Model Assessment
Forty-five (45) models using the set architecture of ten neurons in the first layer and a single node in
the last layer for the output were developed after the training process. For each area cluster, there are
five models that were developed – each forecasting from three hours water level advance up to sevenhours water level advance. In general, most of the models showed good fit in its regression analysis interms of their forecasting capability and validity. Using the Mean Square Error (MSE) criterion, it
measured the average squares of errors or deviation from the predicted water level data to the actual
recorded water level data and the Pearson Correlation (R) that defines the strength of linear fit for theANN trained.
The other four to seven-hour models follow the same behavior as the initial three-hour model foreach area cluster. Overall the average square of deviation of all these models ranges from 0.0007 to0.7624 which is relatively low compared to the actual predicted data. Most of the models showedsignificantly strong linear relationship (R between values 0.8 to 1) for the predicted data except for theset of Sasmuan area cluster with weak linear relationship (R between values 0.4 to 0.6).

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IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Table 2 MSE and R of the three-hour model for each of the nine (9) area clusters
Clustered Station
ArayatCandaba
MexicoPenaranda
San Isidro
Sapang Buho
Sasmuan
SulipanZaragoza

Mean Square Error (MSE)
0.58320.00600.00190.00900.02950.04730.02460.20000.0135Pearson Correlation (R)0.99160.9991
0.99220.99620.99790.99260.54480.98890.9948

Data Plots
The actual plot of area model with the highest value of R (three-hour model in Candaba station) showsa clear visual captivity of behavior of the actual recorded water level versus the predicted data in
Candaba station of water level as shown in Fig. 3.

Fig. 3 Plot of target data vs predicted data
for three-hour ahead forecastingin Candaba station

Fig. 4 Plot of target data vs predicted data
for five-hour ahead forecastingin Sasmuan station

On the contrary, the model with the lowest value of R (five-hour model in Sasmuan station)
showed weak captivity of behavior of the actual water level data (Fig.4). The model cannot linearly
capture the trend of the actual data and the actual data shows periodic up and down of plot. This may
be attributed to the nonlinear relationship of the rainfall and water level in the area and to the otherphysical phenomena or factors not considered in this study.
CONCLUSION
The application of Artificial Neural Networks (ANN) to the hydrology in Philippine setting has been
explored by creating forty-five (45) forecasting models under nine (9) area clusters in Pampanga riverbasin. The training of the model resulted in good-fitted models except for the case of the Sasmuanstation. Inaccuracy of the model in Sasmuan station may be attributed to the other factors notconsidered in this study and hence can be further investigated and studied so that the model can beⒸ ISERD37

IJERD – International Journal of Environmental and Rural Development (2017) 8-1

recalibrated. Furthermore, the use of ANN is not governed by theoretical rules nor any rule-of-thumbsuggestions that will result in a global optimal model. This side of the ANN can be further explored inits application to hydrology. In addition to this, ANN is a data-dependent method and reasonableamount of data must be used in order to build a much reliable network model.
ACKNOWLEDGEMENTS
The data provided by the Hydrometeorological Division (HMD) of PAGASA thru Engr. Dominic
Joshua Lopez is hereby gratefully acknowledged.
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Elsafi, S. 2014. Artificial neural networks (ANN’s) for flood forecasting at Dongola Station in the River Nile,Sudan. Alexandria Engineering Journal, 53, 655-662.
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