Conjunction Weighted Average Method with Fuzzy Expert System for Weather Event Forecasting – A Monthly Outlook

Fuzzy logic as a limiting case of approximate reasoning is viewed in exact reasoning, consider everything in a matter of degree. A collection of elastic or equivalently interpreted to knowledge, a collection of variables in fuzzy constraint. Inference is process as a propagation of elastic constraints. Every logical system is fuzzified in fuzzy logic. Fuzzy logic is fascinating area of research, it trading off between significance and precision. It is convenient way to map space of input to a space of output. Fuzzy logic as so far as the laws of Mathematics refers to reality, they are not certain and so far, as they are certain as complexity rises, precise statements lose meaning and meaningful statements lose precision. Most meteorological infrastructure is surprisingly versatile. For example, the same radar system that can detect oncoming storms will also be useful for gathering general rainfall data for the farming sector. Being able to predict and forecast the weather also allows for data to be gathered to build up a more detailed picture of a nation’s climate, and trends within i

Monthly rainfall prediction based on artificial neural networks with backpropagation and radial basis function

Two models of Artificial Neural Network (ANN) algorithm have been developed for monthly rainfall prediction, namely the Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN). A total data of 238 months (1994-2013) was used as the input data, in which 190 data were used as training data and 48 data used as testing data. Rainfall data has been tested using architecture BPNN with various learning rates. In addition, the rainfall data has been tested using the RBFNN architecture with maximum number of neurons K = 200, and various error goals. Statistical analysis has been conducted to calculate R, MSE, MBE, and MAE to verify the result. The study showed that RBFNN architecture with error goal of 0.001 gives the best result with a value of MSE = 0.00072 and R = 0.98 for the learning process, and MSE = 0.00092 and R = 0.86 for the testing process. Thus, the RBFNN can be set as the best model for monthly rainfall prediction

A Comprehensive Survey of Data Mining Techniques on Time Series Data for Rainfall Prediction

Time series data available in huge amounts can be used in decision-making. Such time series data can be converted into information to be used for forecasting. Various techniques are available for prediction and forecasting on the basis of time series data. Presently, the use of data mining techniques for this purpose is increasing day by day. In the present study, a comprehensive survey of data mining approaches and statistical techniques for rainfall prediction on time series data was conducted. A detailed comparison of different relevant techniques was also conducted and some plausible solutions are suggested for efficient time series data mining techniques for future algorithms.

Statistics Model for Meteorological Forecasting Using Fuzzy Logic Model

Abstract The key atmospheric variables that impact crops are weather and rainfall. Extreme rainfall or drought at critical periods of a crop's development can have dramatic influences on productivity and yields. The analysis of effect of rainfall is needed to evaluate crop production in Northeastern Thailand. Two operations were performed on the Fuzzy Logic model; the fuzzification operation and defuzzification operation. The model predicted outputs were compared with the actual rainfall data. Simulation results reveal that predicted results are in good agreement with measured data. Prediction Error and Root Mean Square Error (RMSE) were calculated, and on the basis of the results obtained, it can be suggested that fuzzy methodology is efficiently capable of handling scattered data. Keywords Rainfall Prediction, Statistics Model, Meteorological Forecasting, Fuzzy Logic Model Background and Objectives Meteorological forecasting is one of the most essential and demanding operational tasks carried out by meteoric services all over the world (Guhathakurata, 2006). In the past, Thailand was severe drought and flooding. These problems are expected to increase steadily and damage to the economy, agriculture, and subsistence. To mitigate this, effective planning and management of water resources is necessary. Forecasting the rainfall at different time scales is important to both short-term and long-term planning in agricultural production for successful crop selection and crop rotation planning. An accurate and timely rainfall forecast is crucial for reservoir operation and flooding prevention because it can provide an extension of lead-time of the flow forecast, larger than the response time of the watershed, in particular for small and medium-sized mountainous basins. In the short term, this requires a good idea of the upcoming season. In the long term, it needs realistic projections of scenarios of future variability and chang

Rainfall Prediction in the Northeast Region of Thailand using Cooperative Neuro-Fuzzy Technique

Accurate rainfall forecasting is a crucial task for reservoir operation and flood prevention because it can provide an extension of lead-time for flow forecasting. This study proposes two rainfall time series prediction models, the Single Fuzzy Inference System and the Modular Fuzzy Inference System, which use the concept of cooperative neuro-fuzzy technique. This case study is located in the northeast region of Thailand and the proposed models are evaluated by four monthly rainfall time series data. The experimental results showed that the proposed models could be a good alternative method to provide both accurate results and human-understandable prediction mechanism. Furthermore, this study found that when the number of training data was small, the proposed model provided better prediction accuracy than artificial neural network

Interpretable fuzzy systems for monthly rainfall spatial interpolation and time series prediction

This thesis proposes methodologies to analyze and establish interpretable fuzzy systems for monthly rainfall spatial interpolation and time series prediction. A fuzzy system has been selected due to its capability of handling the uncertainty in the data and due to its interpretability characteristic. In the first part, this thesis proposes a methodology to analyze and establish interpretable fuzzy models for monthly rainfall spatial interpolation using global and local methods. In the global method, the proposed methodology begins with clustering analysis to de-termine the appropriate number of clusters, and fuzzy modeling and a genetic algorithm are then used to establish the fuzzy interpretation model. In the local method, the modu-lar technique has been applied to improve the accuracy of the global models while the interpretability capability of the model is maintained. In the second part, this thesis proposes a methodology to establish single and modular interpretable fuzzy models for monthly rainfall time series predictions. In the single model, the cooperative neuro-fuzzy technique and a genetic algorithm have been used. In the modular model, the modular technique has been applied to simplify the complexi-ty of the single model. The whole system is decomposed into twelve sub-modules ac-cording to the calendar months. The proposed modular model consists of two function-ally consecutive layers, the prediction layer and the aggregation layer. In the aggregation layer, Bayesian reasoning has been applied. The case study used in this thesis is located in the northeast region of Thailand. The proposed models were compared with commonly-used conventional and intelligent methods in the hydrological discipline. The experimental results showed that, in the quantitative aspect, the proposed models can provide good prediction accuracy and, in the qualitative aspect, the proposed models can also meet the criteria used for model in-terpretability assessment

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Rainfall prediction model using soft computing technique

Rainfall prediction in this paper is a spatial interpolation problem that makes use of the daily rainfall information to predict volume of rainfall at unknown locations within area covered by existing observations. This paper proposed the use of self-organising map (SOM), backpropagation neural networks (BPNN) and fuzzy rule systems to perform rainfall spatial interpolation based on local method. The SOM is first used to separate the whole data space into some local surface automatically without any knowledge from the analyst. In each sub-surface, the complexity of the whole data space is reduced to something more homogeneous. After classification, BPNNs are then use to learn the generalization characteristics from the data within each cluster. Fuzzy rules for each cluster are then extracted. The fuzzy rule base is then used for rainfall prediction. This method is used to compare with an established method, which uses radial basis function networks and orographic effect. Results show that this method could provide similar results from the established method. However, this method has the advantage of allowing analyst to understand and interact with the model using fuzzy rules