Neuro-fuzzy(NF)-based adaptive flood warning system for Bangladesh. Md. Ebrahim Hossain, Taskin Noor Turna, Soheli, S. J., & Kaiser, M. S. In 2014 International Conference on Informatics, Electronics Vision (ICIEV), pages 1–5, May, 2014. doi abstract bibtex We have proposed neuro-fuzzy (NF) based flood warning system in this paper. The input data have been collected using wireless sensor network (WSN). The proposed model has collected input parameters, such as rainfall, river water level and river water flow, from a specific site and send the data to decentralized node. Based on the inputs, Adaptive Neuro-Fuzzy Inference System (ANFIS) model has determined the flood possibility index. All the decentralized nodes forward the pre-processed information to the centralized node for further processing. Depending on the last ten years data, The centralized node has compared the flood possibility index with the last ten years data stored in a database and then calculated flood persistence index by linguistic term Red, Yellow or Green on a GIS map. This model may also serve as a tool for real time flood monitoring and flood warning.
@inproceedings{md_ebrahim_hossain_neuro-fuzzynf-based_2014,
title = {Neuro-fuzzy({NF})-based adaptive flood warning system for {Bangladesh}},
doi = {10.1109/ICIEV.2014.6850711},
abstract = {We have proposed neuro-fuzzy (NF) based flood warning system in this paper. The input data have been collected using wireless sensor network (WSN). The proposed model has collected input parameters, such as rainfall, river water level and river water flow, from a specific site and send the data to decentralized node. Based on the inputs, Adaptive Neuro-Fuzzy Inference System (ANFIS) model has determined the flood possibility index. All the decentralized nodes forward the pre-processed information to the centralized node for further processing. Depending on the last ten years data, The centralized node has compared the flood possibility index with the last ten years data stored in a database and then calculated flood persistence index by linguistic term Red, Yellow or Green on a GIS map. This model may also serve as a tool for real time flood monitoring and flood warning.},
booktitle = {2014 {International} {Conference} on {Informatics}, {Electronics} {Vision} ({ICIEV})},
author = {{Md. Ebrahim Hossain} and {Taskin Noor Turna} and Soheli, S. J. and Kaiser, M. S.},
month = may,
year = {2014},
keywords = {ANFIS, Alarm systems, Bangladesh, Flood Persistency, Flood Possibility, Flood Warning System, GIS map, Indexes, Neuro Fuzzy, Resistors, Rivers, Sensors, Wireless Sensor Network, Wireless sensor networks, adaptive neuro-fuzzy inference system model, alarm systems, centralized node, computerised monitoring, decentralized node, environmental monitoring (geophysics), flood possibility index determination, floods, fuzzy neural nets, fuzzy reasoning, geographic information systems, geophysics computing, input data collection, neuro-fuzzy-based adaptive flood warning system, rainfall, real time flood monitoring, river water flow, river water level, wireless sensor network, wireless sensor networks},
pages = {1--5},
}
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