Flood prediction of Sungai Bedup, Serian, Sarawak, Malaysia using deep learning

Roselind, Tei (2018) Flood prediction of Sungai Bedup, Serian, Sarawak, Malaysia using deep learning. [Final Year Project Report] (Unpublished)

[img] PDF
Flood prediction of Sungai Bedup, Serian, Sarawak, Malaysia using deep learning (24 pgs).pdf

Download (2MB)
[img] PDF (Please get the password from Digital Collection Development Unit, ext: 3913 / 3914)
Flood prediction of Sungai Bedup, Serian, Sarawak, Malaysia using deep learning (fulltext).pdf
Restricted to Registered users only

Download (20MB)

Abstract

This study aims to evaluate the performance of the Long Short Term Memory (LSTM) model for flood forecasting. Seven water level data sets provided by the Department of Irrigation and Drainage (DID) for Sungai Bedup, Serian, Kuching, Sarawak, Malaysia are used for evaluating the performances of this algorithm. Distinctive network was trained and tested using daily data obtained from the DID Department in Kuching with the year range from 2014 to 2017. The performances of the algorithm were evaluated based on (Training Error, Testing Error, Loss, Accuracy, Validate Loss and Validate Accuracy, respectively) and compared with the Backpropagation neural network (BP). Among the seven data sets, Sungai Bedup showed a small testing rate which is (0.08), followed by Bukit Matuh (0.11), Sungai Teb (0.14), Sungai Merang (0.15), Sungai Meringgu (0.12), Semuja Nonok (0.14) and lastly is Sungai Busit (0.13). The performance of the developed model is evaluated by comparing them with BP model. Results from this study evidently proved that LSTM models is reliable to forecasting flood with the lowest testing error rate which is (0.08) and highest validate accuracy (92.61% ) compared to Bp with the testing error rate (0.711) and validate accuracy (85.00%). Discussion is provided to prove the effectiveness of the model in forecasting flood problems.

Item Type: Final Year Project Report
Additional Information: Project report (BSc) -- Universiti Malaysia Sarawak, 2018.
Uncontrolled Keywords: Backpropagation (BP), Long Short Term Memory (LSTM), unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, undergraduate, research, Universiti Malaysia Sarawak
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Unai
Date Deposited: 24 Feb 2020 02:44
Last Modified: 24 Feb 2020 03:10
URI: http://ir.unimas.my/id/eprint/29088

Actions (For repository members only: login required)

View Item View Item