6 th International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA 2012)
IP Based Weather Monitoring and Forecasting using ANN Norsang Lama, Samrat Subedi, Bhupesh Kumar Mishra, Rameshwar Rijal Kantipur Engineering College, P.O.Box 8849, Kathmandu, Nepal
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Abstract: IP Based Weather Monitoring and Forecasting using Artificial Neural Network (IP-WMFANN) is a model that monitors environmental condi tions of a smaller geographical region and predicts the weather parameters. In this, a weather forecasting model is proposed using Arti ficial Neural Network (ANN). The prediction is done using the features extracted from weather data over di fferent peri ods as well as from the weather parameter ti me-series itself. The back-propagation algori thm is used for training to Multi Layer Perceptron network or predicating the weather parameter with high accuracy. This model ai ms to provi de the information about the weather parameters which are critical for fiel ds like industry, agriculture, tourism, trans portati on, etc. accomplished in three uni ts: remotely monitoring, storing in database and forecasting using ANN.
Keywords: Artificial Neural Network (ANN), Weather Forecasting, Back Propagati on Algorithm Introduction Weather is the state of the atmosphere at a given time and place with respect to heat or cold, wetness or dryness, calm or stormy, clearness or cloudiness. The informat ion about the weather parameters such as temperature (min imu m and maximu m), rain fall, relative hu mid ity, etc. are major determining factors in sectors like agricu lture as well as in many industries. Today, not only gaining the complete informat ion about the weather conditions in current time becomes major task but also predicting in future time too. Observation of atmospheric pressure, temperature, wind speed, wind direction, hu mid ity, and precipitation are made near earth’s surface by trained observers, automatic weather stations (1). A great quantity of data coming fro m observation over a longer period at local stations can be used to foresee weather condition in future time. Then it co me weather forecasting which predict the state of the atmosphere for a future time and a given location. As weather is continuous, data-intensive, mu ltid imensional, dynamic and chaotic, the problem of generating predictions of meteoro logical events becomes mo re co mplex and the forecasts become less accurate as the range of the forecast increases [1, 2, 3]. There are various techniques involved in weather forecasting, fro m relatively simp le observation of the
sky to highly complex co mputerized mathematical models. Soft co mputing model always composed of Fuzzy logic, Neural Network, Genetic Algorith m (GA), etc [4]. Neural Network is highly suitable for forecasting the weather parameters [5,6] and even the hybrid models are created combin ing these components which are used in predict ion of time series data [4]. In this paper, an automatic wireless station is proposed which monitors the weather informat ion of particular area and in further weather forecasting model using Artificial Neural Net work (ANN). ANN has been most pro minent technique for solving highly nonlinear phenomena because of its adaptive nature. It does not require any prior knowledge of the system on consideration and can model the dynamical systems on real time basis. This important characteristic makes complex problems like weather forecasting, stock market prediction, and other prediction problems as the scope of application of ANN during the last few decades [1,2]. MultiLayer Perceptron (MLP) network trained using back propagation algorithm (BPN) with actual data of past ten years showed the minimu m fo recasting error [1,6]. Even a feature based neural network model was used to forecast weather parameters and made the prediction with high degree of accuracy. Statistical indicators were capable of extracting the trends,
which were considered as features [5]. Hybrid model such as ANN with Particle Swarm Optimization (PSO) technique, which optimizes the weight in neural network, was used to forecast the weather parameters [7]. The model predicted consistently at lower error rates with overall error rate in allowab le error range. Back propagation integrated with GA as Neuro- GA model and Fuzzy– Neuro model can have better performance too [3, 8]. Other hybrid model as fuzzy logic with case-based reasoning (CBR) can be combined for weather prediction. The methodology was used to acquire knowledge about what salient features of continuous-vector, unique temporal cased indicate significant similarity between cases [9]. Though ANNs and different soft computing techniques are mostly used in predicting different weather parameters all over the world there has been no specific research for predict ion of weather parameters in Nepal and meteorological forecasters did not put much precedence on application of this potent mathematical tool. The aim of this work is to build a co mplete system which gives the complete informat ion of weather and ANN- based model for forecasting in future time.
Literature Review Artificial Neural Network: An artificial neural network is an emulation of bio logical neural system based on the operation of biological neural networks . Neural Netwo rks are an informat ion processing technique based on the way biological nervous systems, such as the brain, process information. The fundamental concept of neural networks is the structure of the information processing system. Neural network is co mposed of a large number o f highly interconnected processing elements or neurons and uses the human-like technique of learn ing by example to resolve problems. An ANN is an adaptive, most often nonlinear system that learns to perform a function (an input/output map) fro m data. Adaptive means that the system parameters are changed during operation normally called the training phase and after the training phase the parameters are fixed and the system is used to solve the problem called the testing phase. It takes a different approach to problem solving than that of conventional computers.
Back propagation Algorithm: The backpropagation algorithm is easiest to understand if all the units in the network are linear .In order to train a neural network to perform so me task, we must adjust the weights of each unit in such a way that the erro r between the desired output and the actual output is reduced. This process requires that the neural network co mpute the error derivative of the erro r weights (EW). It must calculate how the error changes as each weight is increased or decreased slightly. The algorith m co mputes each EW by first computing the error activation (EA), the rate at which the error changes as the activity level of a unit is changed. After calculating all the EAs in the hidden layer, we can compute in like fashion the EAs for other layers, moving from layer to layer in a direction opposite to the way activities propagate through the network. Once the EA has been computed for a unit, it is straightforward to compute the EW for each incoming connection of the unit. The EW is the product of the EA and the activity through the incoming connection. It computes the total weighted input Xj, as: ∞
𝑋𝑗 =
𝑦𝑖 𝑏𝑊𝑖𝑗 𝑛 =1
Where yi is the activity level of the jth unit in the previous layer and Wij is the weight of the connection between the ith and the jth unit. Next, the unit calculates the activity yj using some function of the total weighted input. Typically we use the sigmoid function:
𝑦𝑗 =
1 1 + 𝑒−𝑥 𝑗
Once the activities of all output units have been determined, the network computes the error E, which is defined by the exp ression:
𝐸=
1 2
(𝑦𝑗 − 𝑑𝑗 )2 𝑖
Where yj is the activity level of the jth unit in the top layer and dj is the desired output of the jth unit
Methodology
error derivative of the weights (EW) and for that we chose the back propagation algorith m and delta rules for to establish ANN model. Implementati on of Neural Network Model
Fig: “Figure System Overview” Our system “IP-WMFANN” is designed to monitor the environmental conditions, calculate the necessary values, store them in database for future reference and prediction is made on the basis of it. The system operates in three stages: remotely monitoring, storing in database, and forecasting using ANN. They can be described in detail as follows:
We have developed weather monitoring system to collect day-to-day data. This model measures basic weather parameters, viz. temperature, rainfall and humid ity. At the end of the day, maximu m and minimu m temperature for the day is calculated and is stored in the database along with the rainfall and average relative humidity. We use collected sample data for 5 years (2007-2011)1. Fro m the whole data set, the input and desired output matrices are generated. Results Analysis The model predicts next five day ahead maximu m temperature, minimu m temperature, and relative humid ity. The simu lated output as shown in corresponding graph shows the observed prediction which was also compared against the actual data.
Remotely Monitoring: The remotely mon itoring section includes the wireless sensor node. The wireless sensor node is composed of the required sensors, controller unit and the radio. Its operation provides real and complete data for future prediction. In this stage the data are prepared for the specific day and transmitted to the base station at the end of the day. Fig: “Next five day prediction for maximum Temperature”
Storing in database: The base station side includes the hardware such as PC, receiver radio and interface between them. The received data is sent to PC to store in database for future use. Forecasting using ANN: Weather prediction uses the power of computers to make forecast. The forecast models as computer programs runs on computers and make the predictions of the weather parameters such as temperature, pressure, wind and rainfall. . We apply ANN for weather forecast prediction. In order to train a neural network to perform some task, we must adjust the weights of each unit in such a way that the error between the desired output and the actual output is reduced. This process requires that the neural network co mpute the
Fig: “Next five day prediction for relative humidity”
1:
Data from the Department of Hydrology and Meteorology,
Nepal of Khumaltar station, Lalitpur, for 5 years (2007-2011) and also collected sample data final month of 2011 was from our own system
Fig: “Next five day prediction for minimum temperature” Results Prediction Parameters
Varience
Root Mean Square Error
Maximu m Temperature
0.392 0.1543 0.1452 0.0675 0.6132 0.1893 0.0231 0.1205 0.1457 0.0456 1.6734 0.5436 0.46305 0.0324 0.0107
0.3403
Mimimu Temperature
Relative Hu madity
show that the network has a good performance and appropriate accuracy. Besides the other available prediction models and strategy ANN models have the potential to be useful in weather pred iction and can be applicable as an alternative to traditional meteorological approaches. There are other several parameters weather prediction model like rain fall, precipitation, rad iation which can also be included in the model for a co mpleted weather forecast modelling. For highly accurate prediction the reliab le and relevant data is required since the weather varies stochastically within the s mall range. Acknowledgement This project has been supported by Research, Develop ment and Consultancy Division of Kantipur Engineering College. We would also like to support Depart ment of Hydrology and Meteorology, Nepal of Khumaltar station, Lalitpur for providing relevant data to us. References:
0.1218
0.8138
[1] Mohsen Hayeti, and Zahra Mohebi. "Application of Artificial Neural Networks for Temperature Forecasting", World Academy of Science, Engineering and Technology, 2007, pp. 275-279. [2] S. S. De, and A. Debnath. "Artificial Neural Network Based Prediction of Maximu m and Minimu m Temperature in the summer Monsoon Months over India", CCSE, Applied Physics Research, 2009, Vo l. 1. 2.
Table: “observed resultof the system”
[3] Shaminder Singh, Pankaj Bhambri, and Jasmeen Gill. "Time Series based Temperature Prediction using Back Propagation with Genetic Algorith m Technique", International Journal of Computer Science Issues, 2011, Vol. 8. 3.
The predicted result was very close to the expected values. These three parameters prediction were the in the range of predefined error threshold values. We observed that sudden variances in the temperature range is also has been predicted by this system with vary minimu m pred iction erro r.
[4] Satyendra Nath Mandal, and J. Pal Choudhury, S. R. Bhadra Chauduri, Dilip De. "Soft Co mputing Approach in Prediction of a Time Series Data", Journal of Theoretical and Applied Information Technology, pp.1131-1141 .
Conclusions Wireless automatic station can monitor the atmospheric variab le and provides the data to ANN model using to predict in future time. The results
[5] Paras, Sanjay Mathur, Avinash Kumar, and Mahesh Chandra. "A Feature Based Neural Net work Model for Weather Forecasting",: World Cademy of Science, Engineering and Technology, 34, 2007, pp. 66-73.
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