Journal of Scientific & Industrial Research Vol. 73, October 2014, pp. 643-647 Rainfall Prediction with TLBO Optimized ANN a a B Kavitha Rani *, K Srinivas and A Govardhanb a Jyothishmathi Institute of Technology & Science, Karimnagar, Andhra Pradesh, India b Department of CSE & Director, School of IT JNTUH University, Hyderabad Received 9 January 2014; revised 3 June 2014; accepted 1 August 2014 Rainfall prediction is very crucial for India as its economy is based on mainly agriculture. The parameters that are required to predict the rainfall are very complex in nature and also contain lots of uncertainties. Although various approaches have been earlier suggested for prediction, the soft computing is found to be very effective in developing models which emulates human being and derives expertise like human being to adapt to the situations and learn from the experiences. In this study, rainfall prediction for Andhra Pradesh (AP) state is carried out with Artificial Neural Network (ANN). A new heuristic approach Teaching Learning Based optimization (TLBO) is used to train the weights of the ANN developed for rainfall prediction. A comparison is done with classical back Propagation learning approach and mTLBO (a variant of classical TLBO). The data of monthly rainfall (mm) in Coastal Andhra is collected from Indian Institute of Tropical Meteorology (IITM), Pune, India. The data set consists of 1692 monthly observations during years 1871 to 2011. The simulated results reveal the effectiveness of ANN-mTLBO over ANN-BP on investigated datasets. The findings of our work will be very useful in assessing the possible drought situations in AP from the rainfall predictions. Keywords: Rainfall Predictions, ANN, TLBO, Back Propagation Introduction In India the entire agriculture depends upon rain. The economy of India is mainly centered on the productivity from the agricultural outputs. It is thus a major concern to identify any trends for rainfall to deviate from its periodicity, which would disrupt the economy of the country. Even a short term prediction of rainfall is highly difficult due to the fact that parameters involved in predicting rainfall are very complex and uncertain. Rainfall-runoff3 processes are non-linear complex systems involving several contributing factors such as rainfall depth, rainfall distribution, land use, soil type, soil moisture content, etc. Due to process and model complexity, these models are often fitted without serious consideration of parameter values, resulting in poor performance during verification1.Another problem with both conceptual and physically-based models is that empirical regularities or periodicities are not always evident and can often be masked by noise 2. —————— * Author for Correspondence Email: [email protected] Artificial neural networks In this work ANN based rainfall prediction model is proposed with a recently developed heuristic algorithm known as teaching-Learning based optimization (TLBO)8,9. The weights of the ANN developed in the work is trained with TLBO technique. TLBO is a population based approach which starts with many candidate solutions and eventually achieves the desired optimum target with iterations. Unlike back propagation algorithm TLBO does not get trapped in local optima. In this work an exhaustive simulations are carried out with TLBOANN and BP-ANN for rainfall detection4-7 of AP state, mainly coastal Andhra Pradesh. ANNs are mathematical models with a highly connected structure inspired by the structure of the brain and nervous systems. ANN processes operate in parallel, which differentiates them from conventional computational methods. ANNs consist of multiple layers - an input layer, an output layer and one or more hidden layers as shown in Figure 1. Each layer consists of a number of nodes or neurons which are inter-connected by sets of correlation weights. The input nodes receive input information that is 644 J SCI IND RES VOL 73 OCTOBER 2014 Teaching-Learning Based Optimization This optimization method is based on the effect of the influence of a teacher on the output of learners in a class. Like other nature-inspired algorithms, TLBO is also a population based method that uses a population of solutions to proceed to the global solution. A group of learners are considered as the population. In TLBO, different subjects offered to learners are considered as different design variables for the TLBO. The learning results of a learner is analogous to the ‘fitness’, as in other population-based optimization techniques. The teacher is considered as the best solution obtained so far. There are two parts in TLBO: ‘Teacher Phase’ and ‘Learner Phase’. The ‘Teacher Phase’ means learning from the teacher and the ‘Learner Phase’ means learning through the interaction between learners. Teacher phase Figure 1—The structure of ANN processed through a non-linear transfer function to produce outputs to nodes in the next layer. These processes are carried out in a forward manner hence the term multi-layer feed-forward model is used. The learning or training process uses a supervised learning algorithm that compares the model output to the target output and then adjusts the weight of the connections in a backward manner. The process can be summarized in mathematical form as follows. … (1) where Xo and Woj are the bias (Xo = 1) and its bias weight, respectively. N represents the number of input nodes. Each hidden node input (netj) is then transformed through the non-linear transfer function to produce a hidden node output, Yj. The most common form of the transfer function is a sigmoid function and is expressed as follows: … (2) Similarly, the output values between the hidden layer and the output layer are defined by … (3) where M = the number of hidden nodes; Wjk = the connection weight from the j-th hidden node to the k-th output node; and Zk = the value of the k-th output node. In our society the best learner is mimicked as a teacher. The teacher tries to disseminate knowledge among learners, which will in turn increase the knowledge level of the whole class and help learners to get good marks or grades. So a teacher increases the mean learning value of the class according to his or her capability i.e. say the will try to move mean towards their teacher own level according to his or her capability, thereby increasing the learners’ level to a new mean . will put maximum effort into teaching his Teacher or her students, but students will gain knowledge according to the quality of teaching delivered by a teacher and the quality of students present in the class. The quality of the students is judged from the mean value of the population. Teacher puts effort in so as to increase the quality of the students from to , at which stage the students require a new teacher, of superior quality than themselves, i.e. in this case the new teacher is . Let be the mean and be the teacher at any iteration . will try to move mean towards its own level, so now the new mean will be .The solution is updated designated as according to the difference between the existing and the new mean given … (4) where is a teaching factor that decides the value of mean to be changed, and is a random number in the range [0, 1]. The value of can be either 1 or 2, KAVITA RANI et al.: RAINFALL PREDICTION WITH TLBO OPTIMIZED ANN which is again a heuristic step and decided randomly with equal probability as … (5) This difference modifies the existing solution according to the following expression … (6) Learner phase Learners increase their knowledge by two different means: one through input from the teacher and the other through interaction between themselves. A learner interacts randomly with other learners with the help of group discussions, presentations, formal communications, etc. A learner learns something new if the other learner has more knowledge than him or her. Learner modification is expressed as Randomly select two learners Xi and Xj; where ij … (7) Accept Xnewif it gives a better function value Numerous modifications of Teaching Learning Based Optimization (TLBO) algorithm have been done for improvement of algorithm. In our work we have used a variant of TLBO known as mTLBO8 for our simulation purpose. A brief description is given below for the same.In this modification of TLBO only the Learner phase of basic TLBO is modified. The Teacher phase remains same as in TLBO. Through the exhaustive analysis of TLBO concept it is clearly evident that more the learner is learned better the solution. In a traditional teaching-learning environment the learners output is dependent on the interaction between learners and the class room delivery by teachers. To further enhance the learning capability of students an extra training through the tutorial helps. A learner interacts randomly with other learners with the help of group discussions, presentations, formal communications, etc. and at the 645 same time he or she can discuss more closely with the teacher who is more knowledgeable person in a tutorial class. A learner always learns something new form the teacher and if the other learner has more knowledge than him or her then only he or she gets more knowledge. Hence, an extra term is added in the learner phase equation to modify TLBO. Now the New Learner modification is expressed as in place of its corresponding equation of basic TLBO … (8) The third term in the above equation (8) represents the close interaction between a teacher and learner analogous to tutorial concept. Experimental Set Up and Simulations 1 The model building process consists of four sequential steps: 2 Selection of the input and the output data for the supervised BP/TLBO learning. 3 Normalization of the input and the output data. 4 Training of the normalized data using BP/TLBO learning. 5 Testing the goodness of fit of the model. 6 Comparing the predicted output with the desired output Dataset Description The data of monthly rainfall (mm) in Coastal Andhra is collected from Indian Institute of Tropical Meteorology (IITM), Pune, India. The data set consists of 1692 monthly observations during years 1871 to 2011. In this study, to rescale the variables adjusted normalized technique is used. These adjusted values fall in range from -1 to +1. The used data is split into three different samples such as training sample, testing sample and hold out sample. Structure of the ANN The model proposed uses three layer feed forward neural network with input layer, one hidden and one output layer. To develop a very simple ANN a single hidden layer is chosen. The number of input neurons needed by model is two, each representing the values of lag12 (monthly rainfall of previous year) and Month (where month takes the values 1 to 12 for January to December respectively). The training 646 J SCI IND RES VOL 73 OCTOBER 2014 dataset is grouped year and month wise. The input data set is a matrix with two columns and rows equal to the size of the training dataset. The predicted rainfall of a month is a function of the corresponding month of previous years available in the training dataset. For example predicted rainfall of 2011 January is the function of all previous years January month rainfalls. The model uses only one output unit which indicates the forecast of monthly rainfall. Number of neurons in hidden layer is initially started with one neuron and based on the RMSE of the model the number of neurons are grown. After several simulations we find that the model provided fairly good results with seven neurons in the hidden layer. This demonstrates the use of Forward selection method to determine rainfall predictions. The model performance is shown in table 1 by calculating Root-Mean-Square Error (RMSE) which is used to measure how close forecasts or predictions are to the eventual outcomes given in the data.The Figure 2 below demonstrates the predicted values with the target values of both ANN-BP and ANN-mTLBO. It can be clearly observed that the ANN-mTLBO outperforms ANN-BP in terms of predicted accuracy. The RMSE shown in table 1 is the validation of the better predicted accuracy of ANN-mTLBO. The entire simulation was carried out with standard BP algorithm and the parameters of mTLBO were chosen from8. Since the TLBO is a population based Table 1—Model performance Data set In-Sample set Out-of-Sample set In sample Set Out of sample set Models RMSE ANN-BP ANN-BP ANN-mTLBO ANN-mTLBO 0.47 0.53 0.23 0.33 algorithm several simulations are taken to average the RMSE presented in the work. It is evident from the results analysis that mTLBO is not trapped in local optimum solution unlike BP algorithm. It may be reiterated here that BP algorithm has the deficiency to be get trapped to local optima solution as it is gradient based. Whereas the population based approach of mTLBO provides the algorithm to seek the better solutions region after several iteration and provides optimized weights and bias values for the chosen ANN model. Conclusion and future improvements The objective of this work is to predict rainfall in the AP state by using a suitable ANN model. As ANN is better non-liner function approximate this work has once aging emphasized the well researched ANN model. In this work a suitable model for rainfall detection in Coastal Andhra is developed using both back propagation and TLBO algorithm. A very minimal ANN model selected with forward selection mechanism consisting of one hidden layer with seven neurons are trained separately using BP and a variant of TLBO known as mTLBO. The results obtained from simulations reveal the accuracy of ANN-mTLBO over ANN-BP. The performances are compared with RMSE values and the graph of predicted and targeted outputs is shown as comparison for both approaches. The findings of the models developed can be very helpful to identify the drought situations in the regions of Andhra Pradesh and suitable steps can be initialed by the government organizations to mitigate the disadvantages. The study can be further be enhanced taking several other parameter as inputs for rainfall predictions. 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