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Research Article
 

Seasonal Rainfall Forecasting Using Artificial Neural Network



G.A. Fallah-Ghalhary, M. Mousavi-Baygi and M. Habibi-Nokhandan
 
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ABSTRACT

The rainfall of Khorasan Province, the Northeastern part of Iran, was evaluated from Dec. to May that is included 80% total of annual rainfall in the area under study using artificial neural network. The data of 37 rainfall stations were selected and analyzed over a period of 33 years (1970-2002). The Digital Elevation Model (DEM) was then used to calculate the average rainfall in the area of interest. The relation between variation of synoptic patterns including Sea Surface Temperature (SST), Sea Level Pressure (SLP), the difference of sea level pressure, the difference between sea surface temperature and 1000 hPa surface level, relative humidity at 300 hPa level, geopotential height at 500 hPa level and air temperature at 850 hPa level with mean rainfall of the region were considered. Then the artificial neural network model was trained for 1970-2002 period and rainfall for period of 1993-2002 was predicted. The results showed that artificial neural network method was very successful in predicting rainfall and in more than 70% of years could predict rainfall within acceptable precision. The root mean square error of the model was found to be 41 mm which is considered negligible at yearly level and it is expected that by increasing the number of years of statistical data the precision of the model would increase.

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  How to cite this article:

G.A. Fallah-Ghalhary, M. Mousavi-Baygi and M. Habibi-Nokhandan, 2009. Seasonal Rainfall Forecasting Using Artificial Neural Network. Journal of Applied Sciences, 9: 1098-1105.

DOI: 10.3923/jas.2009.1098.1105

URL: https://scialert.net/abstract/?doi=jas.2009.1098.1105
 

INTRODUCTION

Forecasting of the seasonal rainfall is very important to semi-arid area like Khorasan Province in Northeast of Iran. Artificial neural network is an innovative approach to construct computationally intelligent systems that are supposed to possess humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments and explain how they make decisions. Considering the significance of rainfall in many decision making processes such as water resource management and agriculture, the present study aims to find out the relationship between large-scale climatic signals and regional rainfall using artificial neural network.

McCullagh et al. (1995) investigated the use of an Artificial Neural Network (ANN) to estimate the 6 h rainfall over the South-east coast of Tasmania. The results confirm that ANNs have the potential for successful application to the problem of rainfall estimation. Karamouz et al. (2004) have used from ANN, fuzzy logic and time series for seasonal rainfall forecasting in the western regions of Iran. In this research, The ANN model displayed a better performance compared to the other models. Nazemosadat and Cordery (2000) used Sea Surface Temperature (SST) for seasonal rainfall variability in the winter (Jan. to Mar.) in the southern regions of Iran. Results showed that rainfall is inversely proportion with Sea Surface Temperature (SST) of the Persian Gulf. Khoshakhlagh (1998) used from correlation between ENSO and rainfall in Iran. Results showed that the Iranian rainfall predictors are strongly related with ENSO.

Suwardi et al. (2006) have used of a neuro-fuzzy system for modeling wet season tropical rainfall. The models resulted low values of the RMSE indicated that the prediction models are reliable in representing the recent inter-annual variation of the wet season tropical rainfall.

Lee et al. (1998) used RBF (Radial Basis Function) networks and linear regression based on the locational information only for the daily rainfall prediction at 367 locations in Switzerland. Comparison with the observed data revealed that RBF networks produced good predictions while the linear models poor predictions. Maria et al. (2005) used ANN and linear regression for rainfall forecasting in Sao Paulo State, Brazil. The results show that ANN forecasts were superior to the ones obtained by the linear regression model thus revealing a great potential for an operational suite. Cavazos (2000) used self organization map and ANN for daily rainfall forecasting in Bucharest. Liu and Lee (1999) also used from ANN for short-term rainfall forecasting in Hong Kong region. This neural-based rainfall forecasting system is useful and parallel to traditional forecasts from the Hong Kong observatory. Wong et al. (2003) constructed fuzzy rule bases with the aid of SOM (Self-Organization Map) and back propagation neural networks and then with the help of the rule base developed predictive model for rainfall over Switzerland using spatial interpolation. Matayo et al. (2000) used Empirical Orthogonal Function (EOF) and simple correlation analysis for seasonal rainfall anomalies over East Africa. Pozo-Vazquez et al. (2001) the association among ENSO (El Nino-Southern Oscillation), the Northern Hemisphere Sea Level Pressure (SLP) and temperatures in Europe has been analyzed during the period 1873-1995. Surajit and Manojit (2007) used artificial neural network as a Soft Computing technique to anticipate the average monsoon rainfall over India and results have been compared with those obtained through conventional techniques.

MATERIALS AND METHODS

Study area: The area of this study is Khorasan Province in North east of Iran in Fig. 1. Total precipitation from December to May over a period of 33 yeas (1970-2002) was selected as data of our interest in this research.

Data of 37 stations including four Synoptic, five climatology and 28 rain gauges (all belong to Iranian Meteorological organization) were selected for each year.

Calculating of local average rainfall: Digitizes Elevation model is used to get the amount of local average rainfall. The following steps were taken to obtain the time series of average regional rainfall:

Making input files for the Arc GIS software
Obtaining the relation between rainfall and elevation using regression model
Obtaining digitized map of area under study
Analyzing and drawing annual spatial changes of rainfall in the region
Obtaining the values of annual average rainfall in the region under study
Making time series of rainfall in the region under study

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Fig. 1: Map of area of study with selected stations

Table 1: Name and properties of selected stations in the area under study
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Data collection: The data used in this study are:

Thirty seven rainfall station data for the seasonal rainfall (Dec.-May) were obtained from Iranian Meteorological organization. All of these stations are in the Eastern north region of Iran. Properties of these stations have shown in Table 1
Large-scale ocean and atmospheric circulation variables such as Sea Surface Temperature (SST), Sea Level Pressure (SLP), the difference sea level pressure, the difference sea surface temperature between surface and 1000 hPa level, relative humid at 300 hPa level, geopotential height at 500 hPa level, air temperature at 850 hPa level during months (June-Nov.). These data were obtained from NCEP/NCAR Re-analysis data. These data sets span the period of 1948-current, covering the globe on a 2.5x2.5 grid and available at http://www.cdc.noaa.gov National Oceanic and Atmospheric Administration (NOAA) website
Standard ENSO indices: NINO3, NINO1+2, Southern Oscillation Index (SOI) Available at http://www.cdc.noaa.gov
Indian Ocean Dipole (IOD) index (Saji et al., 1999). This is an index based on SST anomaly difference between the Eastern and Western tropical Indian Ocean. The index, its impact on the adjoining continental rainfall, interactions with El Nino Southern Oscillation (ENSO) and teleconnections can all be obtained from the IOD home page http://www.jamstec.go.jp/frsgc/research/d1/iod/

Identification of predictors: The aim of identification of predictors is to identify predictors for Khorasan Province seasonal rainfall, which can then be used in forecast models. The two main requirements for any useful predictors are good relationship with the seasonal rainfall and reasonable lead-time (i.e., months to season). The earlier research indicated that seasonal rainfall in the region is strongly correlated with predictors. So, the first step is to look for relationship with standardized predictors during the season (June-Nov.) and follow up with correlation’s between the rainfall and large-scale ocean-atmospheric variables (SST, SLP and so on). This approach of correlation with large-scale ocean-atmospheric circulation variables used to identify predictors for seasonal rainfall forecasting in the Northern East of Iran.

Correlation with large-scale variables: The predictors’ large-scale aspects and also the seasonal rainfall correlation with predictors such as SST, SLP and etc. were checked during pre-season rainfall (June-Nov). In this research, the correlations that are significant at 95% confidence level have been selected. Figure 2 show properties of selected points which have used for relation between rainfall and remote linkage controlling.

Predictor selection: Based on the correlations with indices and the correlation with large-scale variables, predictors with high correlations to the seasonal rainfall were identified. With this criterion, the selected predictors parameters are:

Standardized pressure of Aden gulf (x1), South of Persian Gulf (x2), North of Red Sea (x3), South of Red Sea (x4), The difference of pressure standardized between Adriatic Sea and south of Persian Gulf (x5). Aral Lake and North of Caspian Sea (x6), South of Persian Gulf and Arab Sea (x7), Oman Sea and South of Persian Gulf (x8), South of Persian Gulf and South of Red sea (x9), The standardized sea surface temperature of Siberian network (x10), The difference of temperature standardized between sea surface and the 1000 mb level of the Island network (x11).

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Fig. 2: Name and coordinates that have used for relation between rainfall and remote linkage controlling

The factor analysis of the relative humidity in the index area of factor 1 in a 5x5 degree networks (x12). These regions have shown in Fig. 3.

Time series of rainfall and selected predictors that are used for relation between rainfall and Remote linkage controlling that mentioned above have shown in Table 2.

THE STRUCTURE OF ARTIFICIAL NEURAL NETWORKS

Artificial neural networks were first introduced in 1943 by McCulloch et al. (1995). Later, with the development of back propagation algorithm for feed forward networks the application of neural network entered a new stage (Mahdizadeh, 2004).

Like natural neural networks, artificial neural networks are made up of parts called neural cells. As in natural neural networks where some cells are responsible for receiving the external stimulus, some for processing and some for the transfer of response to the intended part, in artificial neural networks, too, some cells receive the data of the problem, some process the data and some provide the solution to the problem. Thus, every neural network is made up of the input layer, the hidden layer and the output layer, with the three layers connected by means of connectors of different weights. In all neural networks, there is one input layer, one output layer and several hidden layers (Mahdizadeh, 2004). Figure 4 shows the structure of one kind of such networks (Mohammadi and Misaghi, 2003).

Figure 5 shows the model of a multi-input neuron (Christodoulou and Georgiopoulos, 2000). The three elements of a multi-input neuron are the following:

A) The set of synapses each specified with a certain weight. As it is shown in the Fig. 5, the neuron k with the output xk is connected to the intended neuron j through a proper weight connector called wjk. The effect of the neuron k on the neuron j is calculated through xk.wjk. If the neuron k is active and wjk is positive (excitatory synapse), the neuron k will have a positive effect on the neuron j. On the other hand, if the neuron k is active, but wjk is negative (inhibitory synapse), the neuron k will have a negative effect on the neuron j. Special attention should be paid to the written form and the subtitle of the weight of the synapse wjk. The first subtitle belongs to the target neuron and the second to the source neuron of the intended synapse.

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Fig. 3: The detected areas of relative humidity at 300 mb level in networks of 5x5 degree

Table 2: Time series of rainfall and selected predictors
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Fig. 4: The overall structure of feed forward monolayer neural networks

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Fig. 5: A model of a multi-input neuron

B) A capacitor for collecting the incoming signals which are weighted by the synapses of the neuron. The accumulating effect of all neurons which are connected to the intended neuron (Neuron j) is calculated by adding up all the effects of individual neurons on the neuron j.
C) A function of activity is used to limit the output range of the neuron. Activity function is considered as a constraining function in which the eligible changes of the range of output signals is restricted to some finite values. The net input and the output y are calculated by Eq. 1 and 2.

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(1)

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(2)

In the Equation x1, x2,…, xk represent the incoming signals, wj1, wj2,…, wjk stand for synaptic weights accumulating in a neuron, net is the accumulated effect of all the neurons connected to the neuron j and the internal threshold of the neuron j. g is activity function and yj is the output signal of the neuron.

To assess the accuracy of the model, the index of Root Mean Square Error (RMSE) has used which is calculated by the following formula:

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(3)

In the above Equation, RMSE is Root Mean Square Error, oi and ei are the observed and predicted value of the variable, respectively in the point i and n number of observations.

Research methodology: The methodology described is used as a diagnostic tool to derive anomalous atmospheric patterns characteristic of rainfall events and to derive seasonal rainfall at the area and local scales. The methodology consists of three main steps: (1) classification of the atmospheric controls into different climate signals (i.e., weather types), (2) derivation of large-scale climate anomalies associated with rainfall events and (3) derivation of empirical transfer functions between the atmospheric controls and seasonal rainfall at the area and local scales.

The statistical methods are used to discover patterns; in this case, the correlation between rainfall and climate signals is supposed to find significant features that characterize the seasonal atmospheric circulation and the atmospheric control fields (e.g., humidity) during June to November over the study area.

The purpose of this study is to improve our understanding of the physical and remote linkages associated with rainfall events over the Iran region. This is accomplished by exploring the climate anomalies characteristic of seasonal rainfall events. As explained earlier, the factor analysis classes are used to composite upper troposphere moisture, temperature and mid troposphere geopotential height patterns. These mean fields and their anomalies are chosen to explore the physical characteristics of the atmosphere during seasonal rainfall events in Khorasan Province in Iran during the 1970-2002 period.

Experimentation setup for training and performance evaluation: The data was obtained for the period from 1970 to 2002. The rainfall data was standardized and divided from 1970-1992 as training set and the data from 1993-2002 as test set. The operation was started with a network having 12 input nodes. Further experimentation showed that it was not necessary to include information corresponding to the whole year, but 6 month information centered over the predicted month of the 33 years in the time series would give good generalization properties because in the area under study, 80% total of the rainfall fall from Dec. to May and predictors of the pre-season (from June to Nov.) is suitable to predict amount of the rainfall from Dec. to May.

Thus, based on the information from the earlier years, the network would predict the amount of rain to be expected in each 6 month of the each year. The training was terminated after 1000 epochs. Experiments were carried out on a machine and the model was executed using Neuro Solution software. In this research, tangent hyperbolic axon transfer function was used in the hidden layer and linear tangent hyperbolic axon transfer function in the output layer. Test data was presented to the network and the output from the network was compared with the actual data in the time series. Following are the details of network training.

ANN training: For neural networks using momentum algorithm, 1 input layer, 1 hidden layers and an output layer were used. Input layer consists of 12 neurons corresponding to the input variables and the hidden layer consists of 10 neurons and the output layer consists of 1 neuron (rainfall) [12-10- 1].

RESULTS AND DISCUSSION

After conducting various tests to test the network and the number of neurons of the hidden layer and different functions of activity in the hidden and output layers, as mentioned above, the final model with one input layer, one hidden layer and one output layer (rainfall from Dec. to May), had the least error, so in this research, used it as the main model.

Table 3 shows the training results for ANN model. As an evident from Table 3, Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Mean Absolute Error (MAE), Minimum Absolute Error (Min. Abs Error), Maximum Absolute Error (Max. Abs. Error) was obtained 1755.43, 0.9642, 36.21, 6.51, 73.009 and 0.3, respectively. Table 4 shows the comparative performance of between the actual rainfall and the predicted rainfall by using ANN. Figure 6 also shows actual data versus predicted data. Table 5 shows the characteristic of ANN structure. Root mean square error for the model was obtained 41 mm.

The investigation of the model results showed that the difference between actual rainfall (mm) and predicted rainfall (mm) is acceptable and the model can predict the amount of the rainfall in the most years. The root mean square error for the model was obtained 41.5 mm. As a result, the entered variables in the model can successfully explain the rainfall distribution and dispersal pattern of the seasonal rainfall in the study area.

Table 3: Training results for ANN
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Table 4: Seasonal rainfall prediction (6 months) using ANN
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Table 5: Properties of ANN network
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Fig. 6: Comparative of observed rainfall and predicted rainfall using ANN model

This object has the important role in the planning and agricultural water management.

The results comparative of this research and other researches such as Karamouz et al. (2004) and Maria et al. (2005) showed that ANN techniques are efficiencies in the rainfall prediction and they can successfully predict amount of the rainfall. Then, the results of this research support the other researches in the study area and with considering of these predictions; we can planning future politicals for the maximum operation.

CONCLUSIONS

In this study, it has been attempted to evaluate amount of the rainfall (six month ahead) based on Artificial Neural Network (ANN). As the RMSE values on test data are comparatively less, the prediction model was reliable. There have been few deviations of the predicted rainfall value from the actual. As climate and rainfall predication involves tremendous amount of imprecision and uncertainty. The proposed prediction model based on soft computing on the other hand is easy to implement and produces desirable mapping function by training on the given data set. A network requires information only on the input variables for generating forecasts. In these experiments, only 23 years training data were used to evaluate the learning capability. Network performance could have been further improved by providing more training data. Moreover, the considered connectionist models are very robust, capable of handling the noisy and approximate data that are typical in weather data and therefore should be more reliable in worst situations. Choosing suitable parameters for the soft computing models is more or less a trial and error approach. Optimal results will depend on the selection of parameters. Selection of optimal parameters may be formulated as an evolutionary search (Fogel, 1999) to make ANN models fully adaptable and optimal according to the requirement.

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