Log in
Language:

MERAL Myanmar Education Research and Learning Portal

  • Top
  • Universities
  • Ranking
To
lat lon distance
To

Field does not validate



Index Link

Index Tree

Please input email address.

WEKO

One fine body…

WEKO

One fine body…

Item

{"_buckets": {"deposit": "5f17cb6c-1c4c-47d5-8558-0b4506e9cc1a"}, "_deposit": {"id": "2536", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "2536"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/2536", "sets": ["user-uy"]}, "communities": ["ccm", "ccp", "kyauksetu", "ltc", "maas", "miit", "mlmu", "mmu", "mtlu", "mtu", "mub", "mude", "mufl", "pathein", "scu", "suoe", "tcu", "tgu", "tuh", "tum", "ucsm", "ucsmtla", "ucsmub", "ucspathein", "ucstaungoo", "ucsy", "udmm", "udmy", "uit", "um", "um1", "um2", "umkn", "umm", "uphy", "urj", "uvs", "uy", "yau", "ydbu", "ytu", "yude", "yueco", "yufl", "yuoe"], "control_number": "2536", "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "WIRELESS FLOOD MONITORING USING INTEGRATED HYDROLOGICAL SENSORS AND FLOOD PREDICTION VIAARTIFICIAL NEURAL NETWORK", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Flooding is a natural phenomenon that is very difficult to model into an equation because of its nonlinear characteristics. As a result, early warning flood prediction systems are seldom developed and often rely on meteorological satellites and hydrological maps. However, in the advent of technology, randomness and nonlinearity can now be modelled using artificial neural network. The goal of this study is to develop a wireless flood monitoring and prediction system using artificial neural network, specifically the Nonlinear Autoregressive Network with External Inputs (NARX) neural network that can be used in a small community as flood early warning system. The flood monitoring system was developed by integration of different hydrological sensors such as rain gauge, float sensor, flow meter, soil resistivity meter, air humidity and temperature sensors. The wireless communication was achieved by the use of Zigbee modules. Training of ANN was done via the backpropagation algorithm and an MSE of 0.0032 was achieved using seven epochs having the fourth epoch having the best validation. During the field testing, an average prediction rate accuracy of 98.65% was achieved. A two-sample t-test was done to see if the actual field test is different from the predicted values and the result was there is no significant difference between the two that validates the accuracy of the prediction"}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "flood prediction system"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-05-05"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Wireless flood monitoring using integrated Hydrological sensors and flood prediction via artificial Neural Network.pdf", "filesize": [{"value": "1251 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 1251000.0, "url": {"url": "https://meral.edu.mm/record/2536/files/Wireless flood monitoring using integrated Hydrological sensors and flood prediction via artificial Neural Network.pdf"}, "version_id": "b5244d02-c073-47ea-a334-cdf8ba400158"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_journal_title": "8th AUN/SEED-Net Regional Conference on Electrical and Electronics Engineering"}]}, "item_1583103147082": {"attribute_name": "Conference papaers", "attribute_value_mlt": [{}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_supervisor(s)": []}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Amado, Timothy M."}, {"subitem_authors_fullname": "Cruz, Febus Reidj G."}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Other"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Other"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2015"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "https://uyr.uy.edu.mm/handle/123456789/369"}, "item_title": "WIRELESS FLOOD MONITORING USING INTEGRATED HYDROLOGICAL SENSORS AND FLOOD PREDICTION VIAARTIFICIAL NEURAL NETWORK", "item_type_id": "21", "owner": "1", "path": ["1582967549708"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000002536", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-03-05"}, "publish_date": "2020-03-05", "publish_status": "0", "recid": "2536", "relation": {}, "relation_version_is_last": true, "title": ["WIRELESS FLOOD MONITORING USING INTEGRATED HYDROLOGICAL SENSORS AND FLOOD PREDICTION VIAARTIFICIAL NEURAL NETWORK"], "weko_shared_id": -1}
  1. University of Yangon
  2. Department of Physics

WIRELESS FLOOD MONITORING USING INTEGRATED HYDROLOGICAL SENSORS AND FLOOD PREDICTION VIAARTIFICIAL NEURAL NETWORK

http://hdl.handle.net/20.500.12678/0000002536
http://hdl.handle.net/20.500.12678/0000002536
fca0a2ae-8073-4d62-9ee8-0998e759706a
5f17cb6c-1c4c-47d5-8558-0b4506e9cc1a
None
Preview
Name / File License Actions
Wireless Wireless flood monitoring using integrated Hydrological sensors and flood prediction via artificial Neural Network.pdf (1251 Kb)
Publication type
Other
Upload type
Other
Title
Title WIRELESS FLOOD MONITORING USING INTEGRATED HYDROLOGICAL SENSORS AND FLOOD PREDICTION VIAARTIFICIAL NEURAL NETWORK
Language en
Publication date 2015
Authors
Amado, Timothy M.
Cruz, Febus Reidj G.
Description
Flooding is a natural phenomenon that is very difficult to model into an equation because of its nonlinear characteristics. As a result, early warning flood prediction systems are seldom developed and often rely on meteorological satellites and hydrological maps. However, in the advent of technology, randomness and nonlinearity can now be modelled using artificial neural network. The goal of this study is to develop a wireless flood monitoring and prediction system using artificial neural network, specifically the Nonlinear Autoregressive Network with External Inputs (NARX) neural network that can be used in a small community as flood early warning system. The flood monitoring system was developed by integration of different hydrological sensors such as rain gauge, float sensor, flow meter, soil resistivity meter, air humidity and temperature sensors. The wireless communication was achieved by the use of Zigbee modules. Training of ANN was done via the backpropagation algorithm and an MSE of 0.0032 was achieved using seven epochs having the fourth epoch having the best validation. During the field testing, an average prediction rate accuracy of 98.65% was achieved. A two-sample t-test was done to see if the actual field test is different from the predicted values and the result was there is no significant difference between the two that validates the accuracy of the prediction
Keywords
flood prediction system
Identifier https://uyr.uy.edu.mm/handle/123456789/369
Journal articles
8th AUN/SEED-Net Regional Conference on Electrical and Electronics Engineering
Conference papaers
Books/reports/chapters
Thesis/dissertations
Back
0
0
views
downloads
See details
Views Downloads

Versions

Ver.1 2020-03-08 23:41:17.703049
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Export

OAI-PMH
  • OAI-PMH DublinCore
Other Formats
  • JSON

Confirm


Back to MERAL


Back to MERAL