Modelling Rainfall Prediction Using Data Mining Method - A Bayesian Approach

Weather forecasting has been one of the most technically difficult problems around the globe. Weather data is meteorological data. It can be used for weather prediction. Weather data has 36 attributes but only 7 attributes are most important to rainfall prediction. Data is pre-processed to use it in this Bayesian approach. It is the data mining prediction model for rainfall prediction. The model is trained using the training data set and has been tested for accuracy on test data. The meteorological centres use high computing and supercomputing power to run weather prediction model. To address the problem of compute intensive rainfall prediction model, this paper studies data intensive model using data mining technique. This model works with efficient accuracy and uses moderate amount of compute resources for rainfall prediction. Bayesian approach is used for rainfall prediction. It works well with good accuracy

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

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.

Forecasting of Medium-term Rainfall Using Artificial Neural Networks: Case Studies from Eastern Australia

The advent of machine learning, of which artificial neural networks (ANN) are a component, has provided an opportunity for improved rainfall forecasts, which is of value for water infrastructure management, agriculture, mining and other industries. In this chapter, ANNs are shown to provide more skillful monthly rainfall forecasts for locations in south-eastern Queensland, Australia, for lead-times of 3–12 months. The skill of the forecasts from the ANNs is highest when the models are individually optimized for each month, and when longer-duration series are used as input. The ANN technique has application where there is temperature and rainfall data extending back at least 50 years. Such datasets exist for much of Europe and North America, though a review of the available literature indicates most research into the application of ANN has focused on China, India and Australia

Conceptual Model Uncertainty in the Management of the Chi River Basin, Thailand

With increasing demand and pressures on groundwater resources, accurate and reliable groundwater prediction models are essential for sustainable groundwater management. Groundwater models are merely approximations of reality, and we are unable to either fully characterize or mathematically describe the true complexity of the hydrologic system; therefore, inherent in all models are varying degree of uncertainty. A robust management policy should consider uncertainties in both the imprecise nature of conceptual/numerical models and their parameters. This study addresses the critical question of whether the use of multiple conceptual models to explicitly account for conceptual model uncertainty improves the ability of the models to assist in management decisions. Twelve unique conceptual models, characterized by three alternative geological interpretations, two recharge estimations, and two boundary condition implementations, were formulated to estimate sustainable extraction rates from Thailand’s Thaphra Area, where increasing groundwater withdrawals may result in water level declination and saline water upconing. The models were developed with MODFLOW and calibrated using PEST with the same set of observed hydraulic head data. All of the models were found to reasonably produce predictions of the available heads data. To select the best among the alternative models, multiple criteria have been defined and applied to evaluate the quality of individual models. It was found that models perform differently with respect to different evaluation criteria, and that it is unlikely that a single inter-model comparison criterion will ever be sufficient for general use. The chosen alternative models were applied both individually and jointly to quantify uncertainty in the groundwater management context. Different model-averaging methods were assessed in terms of their ability to assist in quantifying uncertainty in sustainable yield estimation. The twelve groundwater simulation models were additionally linked with optimization techniques to determine appropriate groundwater abstraction rates in the TPA Phu Thok aquifer. The management models aim to obtain maximal yields while protecting water level decline. Despite similar performances among the calibrated models, total sustainable yield estimates vary substantially depending on the conceptual model used and range widely, by a factor of 0.6 in total, and by as much as a factor of 4 in each management area. The comparison results demonstrate that simple averaging achieves a better performance than formal and sophisticated averaging methods such as Maximum Likelihood Bayesian Model Averaging, and produce a similar performance to GLUE and combined-multiple criteria averaging methods for both validation testing and management applications, but is much simpler to implement and use, and computationally much less demanding. The joint assessment of parameter and conceptual model uncertainty was performed by generating the multiple realizations of random parameters from the feasible space for each calibrated model using a simple Monte Carlo approach. The multi-model averaging methods produce a higher percentage of predictive coverage than do any individual models. Using model-averaging predictions, lower optimal rates were obtained to minimize head constraint violations, which do not ensue if a single best model is used with parameter uncertainty analysis. Although accounting for all sources of uncertainty is very important in predicting environmental and management problems, the available techniques used in the literature may be too computationally demanding and, in some cases, unnecessary complex, particularly in data-poor systems. The methods presented here to account for the main sources of uncertainty provide the required practical and comprehensive uncertainty analysis and can be applied to other case studies to provide reliable and accurate predictions for groundwater management applications

An Exploration of Neural Network Modelling Options for the Upper River Ping, Thailand

This thesis reports results from a systematic experimental approach to evaluating aspects of the neural network modelling process to forecast river stage for a large, 23,600 km2 catchment in northern Thailand. The research is prompted by the absence of evidenced recommendations as to which of the array of input processes, validations and modelling procedures might be selected by a neural network forecaster. The flood issue for forecasters at Chiang Mai derives from the monsoon rainfall, which leads to serious out-of-bank flooding two to four times a year. Data for stage and rainfall is limited as the instrumentation is sparse and the historical flood record is limited in length. Neural network forecasting models are potentially very powerful forecasters where the data are limited. The challenge of this catchment is to provide adequate forecasts from data for relatively few storm events using three stage gauges and one rain gauge. Previous studies have reported forecasts with lead times of up to 18 hours. Thus, one research driver is to extend this lead time to give more warning. Eight input determination methods were systematically evaluated through thousands of model runs. The most successful method was found to be correlation and stepwise regression although the pattern was not consistent across all model runs. Cloud radar imagery was available for a few storm events. Rainfall data from a network was not available so it was decided to explore the value of the raw cloud reflectivity data as a catchment-wide surrogate for rainfall, to enhance the data record and potentially improve the forecast. The limited number of events makes drawing conclusions difficult, but for one event the forecast lead time was extended to 24-30 hours. The modelling also indicates that for this catchment where the monsoon may come from the south west or the north east, the direction of storm travel is important, indicating that developing two neural network models may be more appropriate. Internal model training and parameterisation tests suggest that future models should use Bayesian Regularization, and average across 50 runs. The number of hidden nodes should be less than the number input variables although for more complex problems, this was not necessarily the case. Ranges of normalisation made little difference. However, the minimum and maximum values used for normalisation appear to more important. The strength of the conclusions to be drawn from this research was recognised from the start as being limited by the data, but the results suggest that neural networks are both helpful modelling processes and can provide valuable forecasts in catchments with extreme rainfall and limited hydrological data. The systematic investigation of the alternative input determination methods, algorithms and internal parameters has enabled guidance to be given on appropriate model structures