Lake Chini Water Level Prediction Model using Classification Techniques

Lim Zee Hin, Zalinda Othman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationComputational Science and Technology - 6th ICCST 2019
EditorsRayner Alfred, Yuto Lim, Haviluddin Haviluddin, Chin Kim On
PublisherSpringer Verlag
Pages215-226
Number of pages12
ISBN (Print)9789811500572
DOIs
Publication statusPublished - 1 Jan 2020
Event6th International Conference on Computational Science and Technology, ICCST 2019 - Kota Kinabalu, Malaysia
Duration: 29 Aug 201930 Aug 2019

Publication series

NameLecture Notes in Electrical Engineering
Volume603
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference6th International Conference on Computational Science and Technology, ICCST 2019
CountryMalaysia
CityKota Kinabalu
Period29/8/1930/8/19

Fingerprint

Water levels
Data mining
Lakes
Time series
Aquatic organisms
Drought
Decision trees
Support vector machines
Rain
Data structures
Neural networks
Processing

Keywords

  • Classification
  • Lake Chini
  • Piecewise Aggregate Approximation
  • Symbolic Aggregate Approximation
  • Time Series
  • Water Level

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Hin, L. Z., & Othman, Z. (2020). Lake Chini Water Level Prediction Model using Classification Techniques. In R. Alfred, Y. Lim, H. Haviluddin, & C. K. On (Eds.), Computational Science and Technology - 6th ICCST 2019 (pp. 215-226). (Lecture Notes in Electrical Engineering; Vol. 603). Springer Verlag. https://doi.org/10.1007/978-981-15-0058-9_21

Lake Chini Water Level Prediction Model using Classification Techniques. / Hin, Lim Zee; Othman, Zalinda.

Computational Science and Technology - 6th ICCST 2019. ed. / Rayner Alfred; Yuto Lim; Haviluddin Haviluddin; Chin Kim On. Springer Verlag, 2020. p. 215-226 (Lecture Notes in Electrical Engineering; Vol. 603).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hin, LZ & Othman, Z 2020, Lake Chini Water Level Prediction Model using Classification Techniques. in R Alfred, Y Lim, H Haviluddin & CK On (eds), Computational Science and Technology - 6th ICCST 2019. Lecture Notes in Electrical Engineering, vol. 603, Springer Verlag, pp. 215-226, 6th International Conference on Computational Science and Technology, ICCST 2019, Kota Kinabalu, Malaysia, 29/8/19. https://doi.org/10.1007/978-981-15-0058-9_21
Hin LZ, Othman Z. Lake Chini Water Level Prediction Model using Classification Techniques. In Alfred R, Lim Y, Haviluddin H, On CK, editors, Computational Science and Technology - 6th ICCST 2019. Springer Verlag. 2020. p. 215-226. (Lecture Notes in Electrical Engineering). https://doi.org/10.1007/978-981-15-0058-9_21
Hin, Lim Zee ; Othman, Zalinda. / Lake Chini Water Level Prediction Model using Classification Techniques. Computational Science and Technology - 6th ICCST 2019. editor / Rayner Alfred ; Yuto Lim ; Haviluddin Haviluddin ; Chin Kim On. Springer Verlag, 2020. pp. 215-226 (Lecture Notes in Electrical Engineering).
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