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Wireless sensor network for AI-based flood disaster detection

  • S.I. : Design and Management of Humanitarian Supply Chains
  • Published:
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Abstract

In recent decades, floods have led to massive destruction of human life and material. Time is of the essence for evacuation, which in turn is determined by early warning systems. This study proposes a wireless sensor network decision model for the detection of flood disasters by observing changes in weather conditions compared to historical information at a given location. To this end, we collected data such as air pressure, wind speed, water level, temperature and humidity (DH11), and precipitation (0/1) from sensors located at several points in the area under consideration and obtained sea level air pressure and rainfall from the Google API. The collected data was then transmitted via a LoRaWAN network implemented in Raspberry-Pi and Arduino. The developed support vector machine (SVM) model includes a number of coordinators responsible for a number of sectors (locations). The SVM model sends the binary decisions (flood or no flood) with an accuracy of 98% to a cloud server connected to monitoring rooms, where a decision can be made regarding the response to a possible flood disaster.

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Fig. 1

(Source: https://www.arduino.cc/en/uploads/Main/ArduinoUno_R3_Front.jpg)

Fig. 2

(Source: https://www.arduino.cc/en/uploads/Main/ArduinoUno_R3_Front.jpg)

Fig. 3

(Source: https://www.alliedelec.com/m/d/4252b1ecd92888dbb9d8a39b536e7bf2.pdf)

Fig. 4

(Source: https://wiki.dragino.com/index.php?title=File:LoRa_Shield_Pin_Mapping.png)

Fig. 5

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Notes

  1. Other breakdowns can be made based on region and geographical structure, but this would be too granular.

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Author information

Authors and Affiliations

  1. Data Analytic Center (DANA), Fraunhofer Institute for Open Communication Systems (FOKUS), Kaiserin-Augusta-Allee 31, 10589, Berlin, Germany

    Jamal Al Qundus & Adrian Paschke

  2. EVA Electronics Co., Al Muthanna St, Hawally, Kuwait

    Kosai Dabbour

  3. Department of Information Systems, Supply Chain and Decision Making, NEOMA Business School, 59 Rue Pierre Taittinger, 51100, Reims, France

    Shivam Gupta

  4. IAE Montpellier, Montpellier Research in Management, University of Montpellier, Place Eugène Bataillon, 34000, Montpellier, France

    Régis Meissonier

Authors
  1. Jamal Al Qundus
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  2. Kosai Dabbour
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  3. Shivam Gupta
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  4. Régis Meissonier
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  5. Adrian Paschke
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Correspondence to Régis Meissonier.

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Appendices

Appendix 1

Main logic process: In the main loop function, we have called 6 customized functions:

  • getHumidityValue(): this function uses the DHT.h library to extract and give the current Humidity value in the range of (0–100%).

  • getTemperatureValue(): this function also uses the DHT.h library to extract and give the current temperature value.

  • getWindSpeedValue(): This function reads the output on analog pin A1 where the wind speed sensor is connected and returns the output voltage scaled in a range of (0–1023).

  • getWaterLevelValue(): this function reads the output on analog pin A0 where the water level sensor is connected and returns the output voltage scaled in a range of (0–1023).

  • createDataPcket(): this function creates a struct where all of the readings are stored inside the struct along with the station ID and returns this struct in the sending process.

  • sendDataPacketUsingLoRa(): this function initializes the data packet and assigns the struct payload to the LoRa packet and sends it over the RF channel modulated using the ASK modulation function. We have to consider that the sending procedure using any LoRa module requires exactly 3 s to finalize the sending of the entire packet over the modulated channel, so the next reading will happen at least 3 s afterwards in order to avoid any data overlapping or interference during the communication process with the receiver. One more point that must also be considered during LoRa communication is that the data is sent as a payload packet and not as a byte array because of the LoRa communication protocol. This feature is considered as a positive point because on the receiver side, in case of multiple senders, the probability of having multiple mixed and interfering serial data is very high if the data is sent serially, but with the payload/API mode, the receiver will accept each data packet individually and this data packet can be parsed easily. The ID of the sender can also be extracted to determine what packet belongs to what sender.

Appendix 2

Main logic process: In the main loop function, we have called 4 customized functions:

  • receiveDataPacket(): this function is a kind of interruption process that runs while all other functions in Arduino are processing and outputting. The main process here is to listen to the RF port to see if any data packet has arrived and if a data packet has arrived, then a payload packet is defined and prepared to receive the incoming payload from the sender. The packet will be demodulated and stored in the receiver payload automatically.

  • parseReceivedDataPacket(): this function will parse the payload packet and mainly extract the sender ID with all incoming readings and store them directly in a pre-defined struct to prepare for serial communication with the RPI device.

  • createSerialPacket(): once the data packet is parsed and stored in the custom struct, then another data packet with a serial type (Bytes Converted) will be implemented. This means that the incoming sender ID, humidity value, temperature value, wind speed value, and water level value will be converted to a byte array and stored in a buffer to be sent to the RPI.

  • sendBytesArrayToRPI(): this function simply goes through the byte array byte by byte and writes it directly on the serial port where the connection between Arduino and RPI happens.

On the receiver terminal (Raspberry Pi side), data collecting and receiving processes build on the following phases:

  • Initializing the Serial Port: on Raspberry Pi, serial data can be received in 2 different modes: the first is to use UART (Rx, Tx) Pins on the GPIO PINOUT, and the second is to use the USB port for serial communication. We chose the second way. The data comes from Arduino over the USB port in a serial byte array mode, so this requires initializing the RPI serial port using the following Python-developed functions:

  • setSerialBaudRate(): this function sets communication speed with Arduino to 9600 bit/s as defined by the Arduino receiver.

  • setParityBitCheck(): this function applies the parity check OS function over any serial port to check for any bit error during serial communication.

  • setMaxBufferSize(): this function specifies the maximum size of the received byte array at 1 byte per CPU tick.

Receiving the data: once the serial port communication has been initialized and prepared for receiving data, this process will accept the incoming bytes and store them in a list structure in Python. Once the data packet array is completely received, then the list will be converted back to string data and then to numerical data using the following functions:

  • receiveSerialBuffer(): this function stores all incoming bytes in list structure until the data packet is completely received.

  • convertBufferToString(): this function converts the buffer array to string and passes it to the numerical conversion function.

  • convertStringDataToNumericalValues(): this function converts the string data to double temperature value, double humidity value, integer wind speed value, integer water level value, and finally integer station ID.

  • storeDataInCSVFile(): all data is stored and saved in a CSV file for future classification procedures and processes.

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Al Qundus, J., Dabbour, K., Gupta, S. et al. Wireless sensor network for AI-based flood disaster detection. Ann Oper Res 319, 697–719 (2022). https://doi.org/10.1007/s10479-020-03754-x

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