@article{MAKHILLJEAS2020151219330,
    title = {Classification of Remote Sensor Data for Flood Disaster Forecasting using Data Mining
Hybrid Techniques: A Proposed Model},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {15},
    number = {12},
    pages = {2542-2548},
    year = {2020},
    issn = {1816-949x},
    doi = {jeasci.2020.2542.2548},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2020.2542.2548},
    author = {Hasmeda Erna,Nurjannatul,Noor Afiza,Muslihah,Mohammad and},
    keywords = {Dimensionality reduction,remote sensor data,classification,government,SVM},
    abstract = {Based on the National Security Council (NSC)
Directive No. 20 that concern in coordinating responsible
agencies and committee, the Malaysian government has
established a disaster management coordination and
preparedness agency. Among the natural disasters that
occurred in Malaysia, floods are the most destructive.
Thus, research to develop the flood forecasting model
tailored to Malaysia requirements is crucially needed.
Nowadays, neural network, SVM and decision tree have
been used extensively as the data mining models. Support
Vector Machine (SVM) is greatly popular, robust and
efficient in flood modeling and prediction. SVM has been
also extended as a regression tool, known as Support
Vector Regression (SVR). However, the increasing
volume and varying format of collecting data from remote
sensing presents challenges on the efficiency of data
classification for forecasting. Data that are obtained are
high dimensional in nature and dimensionality reduction
needs to be improved by reducing random variable in
classification techniques. This research aims to propose
flood disaster forecasting using data mining classification
techniques by reducing random variable for efficient
result in flood forecasting. This research will
investigates/identify the data mining technique in disaster
that being research by the researchers and proposed a
conceptual model to analyze flood data. SVR will be
employed to select nearby sensors and develops a linear
model for a target sensor. Neural network will be used to
extract patterns and detect trends that are too complex to
be noticed by either humans or other computer
techniques. The proposed model will be tested using data
from Malaysian disaster management agencies. The result
of this study shall create a new model that is expected to
improve flood disaster forecasting and contribute to
enhancement of early warning system and decision
making during a disaster in Malaysia.}
    }