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Lake Chini Water Level Prediction Model using Classification Techniques

机译：湖北省水位预测模型使用分类技术

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Monsoon seasons in Malaysia bring uneven distribution of rainfall and eventually affect the water level at Lake Chini as flood and drought disturb the population and distribution of aquatic organisms at the lake. This study is conducted to produce Lake Chini water level prediction model by comparing several algorithms using data mining approach via classification techniques. Data from seven observation stations between 2011 and 2014 are collected from Pusat Penyelidikan Tasik Chini, Universiti Kebangsaan Malaysia and data from Melai station in particular is used for this purpose. The collected time series data is complex and high in dimensionality thus leading to low efficiency in data mining process. The analysis comprises of four phases that include data collection, data pre-processing, data mining and model development and interpretation and evaluation of patterns. To overcome high dimensional time series, dimensionality reduction approach such as Piecewise Aggregate Approximation (PAA) and Symbolic Aggregate approximation (SAX) are applied while three classification techniques namely Decision Tree, Artificial Neural Network and Support Vector Machine are used to classify the data. Performance measures for each of the algorithms are evaluated and compared to select the most suitable model for the prediction.
机译：在马来西亚带来的雨量分布不均，最终季风季节影响，在珍妮湖的水位洪水和干旱干扰的人口，并在湖泊水生生物的分布。这项研究是由进行比较，通过分类技术，利用数据挖掘方法几种算法产生珍尼湖的水位预测模型。从2011年至2014年之间的七个观测站数据被从Pusat Penyelidikan的Tasik奇尼，马来西亚国民大学和从特别Melai站数据收集被用于此目的。所收集的时间序列数据是复杂的并在维数从而导致在数据挖掘过程效率低高。分析包括四个阶段，其中包括数据采集，数据预处理，数据挖掘和模型开发和解释，并且图形评估。为了克服高维时间序列，降维的方法如分段骨料逼近（PAA）和符号骨料近似（SAX）被施加，而3的分类技术即决策树，神经网络和支持向量机被用来对数据进行分类。为每个算法的性能措施评估和比较，以选择所述预测的最合适的模型。

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