IoT | La estación meteorológica TensorFlow predice la intensidad de la lluvia

⭐ Ingrese el ID y el rango de la hoja de cálculo para agregar hallazgos recientes de la estación meteorológica a la hoja de cálculo.

https://docs.google.com/spreadsheets/d/spreadsheetId/edit#gid=0

  // Ingrese su spreadsheetId:
 $ spreadsheetId =''; 
 // Ingrese el rango (la primera fila) bajo el cual se agregarán nuevos valores (8 filas) :
 $ range ='A1:H1'; 
 // Adjunte los hallazgos recientes de la estación meteorológica a la hoja de cálculo. 
 $ values ​​=[
 [$ variables_from_module ["wd" ], $ variables_from_module ["a_ws"], $ variables_from_module ["m_ws"], $ variables_from_module ["1_rf"], $ variables_from_module ["24_rf"], $ variables_from_module ["tem"], $ variables_from_module, $ variables_from_module ["b_pr"]] 
]; 
 $ body =new Google_Service_Sheets_ValueRange ([
 'valores' => $ valores 
]); 
 $ params =[
 'valueInputOption' => "RAW" 
]; 
 
 ... 
 
 $ result =$ service-> spreadsheets_values-> append ( $ spreadsheetId, $ range, $ body, $ params); 
 printf ("
 
% d celdas adjuntas.", $ result-> getUpdates () -> getUpdatedCells ());  

💻 account_verification_token.php

⭐ Después de autorizar la aplicación web mediante la AuthUrl creada , ingrese el código de verificación proporcionado para configurar un token de acceso.

  $ account_verification_token =""; // Ingrese el código de verificación después de la autorización.  

Paso 3.1:Configurar la aplicación web en Raspberry Pi (o cualquier servidor)

Después de crear la aplicación web PHP, decidí ejecutarla en mi Raspberry Pi, pero puedes ejecutar la aplicación en cualquier servidor siempre que sea un servidor PHP.

Si desea utilizar Raspberry Pi pero no sabe cómo configurar un servidor web LAMP en Raspberry Pi, puede revisar este tutorial.

⭐ En primer lugar, mueva la carpeta de la aplicación (remote_weather_station) al servidor apache (/ var / www / html) utilizando la terminal, ya que el servidor apache es una ubicación protegida.

sudo mv / home / pi / Downloads / remote_weather_station / var / www / html /

⭐ La aplicación web arroja un error cuando se abre por primera vez debido a que falta el código de verificación. Para obtener el código de verificación, vaya al enlace de autorización generado por la aplicación.

⭐ Luego, haga clic para Ir a la estación meteorológica remota (inseguro) .

⭐ Otorgue a la aplicación los permisos necesarios.

⭐ Ahora, copie el código de verificación y péguelo en account_verification_token.php .

⭐ Regrese a la aplicación web. Debería mostrar el mensaje de confirmación: ¡Exitoso! Actualizar la página .

⭐ Después de actualizar la página, la aplicación web usa el código de verificación para obtener un token de acceso y guarda el token de acceso en un archivo llamado token.json . Por lo tanto, no vuelve a lanzar un error.

⭐ Para probar si la aplicación web PHP está agregando datos nuevos a la hoja de cálculo proporcionada con precisión:

http:// localhost / remote_weather_station /? wd =12 &a_ws =8 &m_ws =11 &1_rf =14 &24_rf =84 &tem =24 &hum =32 &b_pr =57

Paso 4:leer y enviar datos meteorológicos con ESP8266 (WiFi)

Para recopilar datos meteorológicos en mi balcón, utilicé una placa de desarrollo NodeMCU ESP-12E (ESP8266) con la estación meteorológica.

Lo programé para enviar datos meteorológicos a la aplicación web PHP cada cinco minutos.

⭐ Incluya las bibliotecas necesarias y defina la configuración de WiFi.

  #include  
 #include  
 #include  
 #include  
 #include  
 
 // Defina su configuración de WiFi. 
 const char * ssid =""; 
 const char * password =" ";  

⭐ Defina la configuración de la estación meteorológica y los pines de conexión en serie:D6, D5.

  // Defina la configuración de la estación meteorológica:
 char databuffer [35]; 
 double temp; 
 int transferring =0; 
 
 // Defina los pines de conexión en serie - RX y TX. 
 SoftwareSerial Serial_1 (D6, D5); // (Rx, Tx)  

⭐ En getBuffer () función, obtenga datos de la estación meteorológica.

  void getBuffer () {
 int index; 
 for (index =0; index <35; index ++) {
 if (Serial_1.available ()) { 
 búfer de datos [índice] =Serial_1.read (); 
 if (búfer de datos [0]! ='c') {
 índice =-1; 
} 
 } 
 else {
 índice -; 
} 
} 
}  

⭐ Depura los datos meteorológicos y crea el enlace.

  String weather_data ="wd =" + String (WindDirection ()) + "&a_ws =" + String (WindSpeedAverage ()) + "&m_ws =" + String (WindSpeedMax ()) + "&1_rf =" + Cadena (RainfallOneHour ()) + "&24_rf =" + Cadena (RainfallOneDay ()) + "&tem =" + Cadena (Temperatura ()) + "&hum =" + Cadena (Humedad ()) + "&b_pr =" + Cadena ( BarPressure ()); 
 String server ="http://192.168.1.24/remote_weather_station/?"; 
 
 ... 
 
 int WindDirection () { return transCharToInt (databuffer, 1,3); } // Dirección del viento (grados) 
 
 float WindSpeedAverage () {temp =0.44704 * transCharToInt (databuffer, 5,7); temperatura de retorno; } // Velocidad del aire promedio (1 minuto) 
 
 float WindSpeedMax () {temp =0.44704 * transCharToInt (databuffer, 9,11); temperatura de retorno; } // Velocidad máxima del aire (5 minutos) 
 
 temperatura de flotación () {temp =(transCharToInt (databuffer, 13,15) - 32.00) * 5.00 / 9.00; temperatura de retorno; } // Temperatura ("C") 
 
 float RainfallOneHour () {temp =transCharToInt (databuffer, 17,19) * 25.40 * 0.01; temperatura de retorno; } // Lluvia (1 hora) 
 
 float RainfallOneDay () {temp =transCharToInt (databuffer, 21,23) * 25.40 * 0.01; temperatura de retorno; } // Lluvia (24 horas) 
 
 int Humidity () {return transCharToInt (databuffer, 25,26); } // Humedad (%) 
 
 float BarPressure () {temp =transCharToInt (databuffer, 28,32); temperatura de retorno / 10,00; } // Presión barométrica (hPA)  

⭐ Envíe paquetes de datos cada 5 minutos a Raspberry Pi (o cualquier servidor).

  transfiriendo ++; Serial.println ("Time =>" + String (transfiriendo) + "s /" + String (int (5 * 60)) + "s \ n \ n"); 
 if (transfiriendo ==5 * 60) {
 // Crea el objeto HTTP para realizar una solicitud al servidor. 
 HTTPClient http; 
 http.begin(server + weather_data);
 int httpCode =http.GET(); 
 String payload =http.getString(); 
 Serial.println("Data Send...\nHTTP Code => " + String(httpCode) + "\nServer Response => " + payload + "\n\n"); 
 http.end();
 transferring =0; 
 }
 // Wait 1 second...
 delay(1000); 

⭐ After uploading the code to the NodeMCU ESP-12E (ESP8266) development board, it displays weather data every second on the serial monitor and sends data packets every five minutes (300 seconds) to the PHP web application.

Connection is successful!

...

Weather Data => wd=0&a_ws=0.00&m_ws=0.00&1_rf=0.00&24_rf=0.00&tem=21.67&hum=29&b_pr=1016.70
Buffer => c000s000g000t071r000p000h29b10167*3
Time => 299s / 300s


Weather Data => wd=0&a_ws=0.00&m_ws=0.00&1_rf=0.00&24_rf=0.00&tem=21.67&hum=29&b_pr=1016.70
Buffer => c000s000g000t071r000p000h29b10167*3
Time => 300s / 300s

... 

⭐ Then, it shows the response from the server.

...

Data Send...
HTTP Code => 200
Server Response => Token Found!

8 cells appended.

... 

Step 4.1:Reading and sending weather data with SIM808 (GPRS)

To collect weather data in my backyard, I used a SIM808 shield for Arduino Uno if the distance between the weather station and my router is too far away.

Most of the code is the same and covered in the previous step aside from the parts below.

⭐ Include required libraries and define the sim808 object.

For SIM808 GPS/GPRS/GSM Shield | Download

#include 
#include 

// Define the sim808.
DFRobot_SIM808 sim808(&Serial); 

⭐ Initialize the SIM808 module and continue if it is working accurately.

//******** Initialize sim808 module *************
 while(!sim808.init()) {
 delay(1000);
 Serial.print("Sim808 init error\r\n");
 }
 delay(2000);
 // Continue if the SIM808 Module is working accurately.
 Serial.println("Sim808 init success");
 delay(5000); 

⭐ Send data packets every 5 minutes to the server by establishing a TCP connection to make a GET request.

transferring++; Serial.println("Time => " + String(transferring) + "s / " + String(int(5*60)) + "s\n\n");
 if(transferring ==5*60){
 //*********** Attempt DHCP *******************
 while(!sim808.join(F("cmnet"))) {
 Serial.println("Sim808 join network error!");
 delay(2000);
 }
 //************ Successful DHCP ****************
 delay(5000);
 Serial.println("Successful DHCP");
 //*********** Establish a TCP connection ************
 if(!sim808.connect(TCP,"192.168.1.24", 80)) { // Change it with your server.
 Serial.println("Connection Error");
 }else{
 Serial.println("Connection OK");
 }
 delay(2000);
 
 ... 

⭐ After creating the line string, convert it from string to char array to make an HTTP Get Request with the SIM808.

...
 
 String line ="GET /remote_weather_station/?" + weather_data_1 + weather_data_2 + weather_data_3 + " HTTP/1.0\r\n\r\n";
 Serial.println(line);
 char buffer[512];
 // Convert the line from string to char array to make an HTTP Get Request with the SIM808.
 char web_hook[110];
 String_to_Char(line, 110, web_hook);
 sim808.send(web_hook, sizeof(web_hook)-1);
 while (true) {
 int ret =sim808.recv(buffer, sizeof(buffer)-1);
 if (ret <=0){
 Serial.println("Fetch Over...");
 break; 
 }
 
 ... 

⭐ After uploading the code to the SIM808 shield, it displays weather data every second on the serial monitor and sends data packets every five minutes (300 seconds) to the PHP web application.

Step 4.2:Creating the weather data set for more than three months

After finishing coding, I started to collate weather data on Google Sheets every five minutes for more than three months to build a neural network model and make predictions on the rainfall intensity.

Collected Weather Data:

• Wind Direction (deg)
• Average Wind Speed (m/s)
• Max Wind Speed (m/s)
• One-Hour Rainfall (mm)
• 24-Hours Rainfall (mm)
• Temperature (°C)
• Humidity (%)
• Barometric Pressure (hPa)

Then, I downloaded the spreadsheet as Remote Weather Station.csv , consisting of 32219 rows as my preliminary local weather data set. I am still collating weather data to improve my data set and model :)

Step 5:Building an Artificial Neural Network (ANN) with TensorFlow

When I completed collating my preliminary local weather data set, I started to work on my artificial neural network (ANN) model to make predictions on the rainfall intensity.

I decided to create my neural network model with TensorFlow in Python. So, first of all, I followed the steps below to grasp a better understanding of the weather data:

• Data Visualization
• Data Scaling (Normalizing)
• Data Preprocessing
• Data Splitting

After applying these steps, I decided to use my neural network model to classify different rainfall intensity classes theoretically assigned as labels (outputs) for each input (row). I created my classes according to the rate of precipitation, which depends on the considered time.

The following categories are used to classify rainfall intensity by the rate of precipitation (rainfall):

• Light rain — when the precipitation rate is <2.5 mm per hour
• Moderate rain — when the precipitation rate is between 2.5 mm and 7.6 mm per hour
• Heavy rain — when the precipitation rate is between 7.6 mm and 50 mm per hour
• Violent rain — when the precipitation rate is> 50 mm per hour

According to the precipitation (rainfall) rates, I preprocessed the weather data to assign one of these five classes for each input as its label:

• 0 (None)
• 1 (Light Rain)
• 2 (Moderate Rain)
• 3 (Heavy Rain)
• 4 (Violent Rain)

After scaling (normalizing) and preprocessing the weather data, I elicited seven input variables and one label for each reading, classified with the five mentioned classes. Then, I built an artificial neural network model with TensorFlow to obtain the best possible results and predictions with my preliminary data set.

Layers:

• 7 [Input]
• 16 [Hidden]
• 32 [Hidden]
• 64 [Hidden]
• 128 [Hidden]
• 5 [Output]

To execute all steps above, I created a class named Weather_Station in Python after including the required libraries:

import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd 

Subsequently, I will discuss coding in Python for each step I mentioned above.

Also, you can download IoT_weather_station_neural_network.py to inspect coding.

Step 5.1:Visualizing and scaling (normalizing) the weather data

Before diving in to build a model, it is important to understand the weather data to pass the model appropriately formatted data.

So, in this step, I will show you how to visualize weather data and scale (normalize) in Python.

⭐ First of all, read the weather data from Remote Weather Station.csv .

csv_path ="E:\PYTHON\Remote Weather Station.csv"
df =pd.read_csv(csv_path) 

⭐ In the graphics() function, visualize the requested columns from the weather data set by using the Matplotlib library.

def graphics(self, column_1, column_2, xlabel, ylabel):
 # Show requested columns from the data set:
 plt.style.use("dark_background")
 plt.gcf().canvas.set_window_title('IoT Weather Station')
 plt.hist2d(self.df[column_1], self.df[column_2])
 plt.colorbar()
 plt.xlabel(xlabel)
 plt.ylabel(ylabel)
 plt.title(xlabel)
 plt.show() 

⭐ In the data_visualization() function, inspect all columns before scaling weather data to build a model with appropriately formatted data.

def data_visualization(self):
 # Inspect requested columns to build a model with appropriately formatted data:
 self.graphics('WD', '1h_RF', 'Wind Direction (deg)', 'One-Hour Rainfall (mm)')
 self.graphics('Av_WS', '1h_RF', 'Average Wind Speed (m/s)', 'One-Hour Rainfall (mm)')
 self.graphics('Mx_WS', '1h_RF', 'Maximum Wind Speed (m/s)', 'One-Hour Rainfall (mm)')
 self.graphics('24h_RF', '1h_RF', '24-Hour Rainfall (mm)', 'One-Hour Rainfall (mm)')
 self.graphics('Tem', '1h_RF', 'Temperature (°C)', 'One-Hour Rainfall (mm)')
 self.graphics('Hum', '1h_RF', 'Humidity (%)', 'One-Hour Rainfall (mm)')
 self.graphics('b_PR', '1h_RF', 'Barometric Pressure (hPA)', 'One-Hour Rainfall (mm)')
 

After visualizing weather data, I scaled (normalized) each column to format it properly.

Normally, each row (reading) looked like this before scaling:

• 315, 0.45, 0, 0.51, 0.51, 22.78, 65, 1005.5

⭐ In the scale_data() function:

⭐ The wind direction in units of degrees and angles do not make good model inputs. 360° and 0° should be close to each other and wrap around smoothly. The direction should not matter if the wind is not blowing. Thus, convert the wind direction and velocity columns to a wind vector to interpret them easily with a neural network model.

def scale_data(self):
 # Wind Direction and Speed:
 wv =self.df.pop('Av_WS')
 max_wv =self.df.pop('Mx_WS')
 # Convert to radians.
 wd_rad =self.df.pop('WD')*np.pi / 180
 # Calculate the wind x and y components.
 self.df['scaled_WX'] =wv*np.cos(wd_rad)
 self.df['scaled_WY'] =wv*np.sin(wd_rad)
 # Calculate the max wind x and y components.
 self.df['scaled_max_WX'] =max_wv*np.cos(wd_rad)
 self.df['scaled_max_WY'] =max_wv*np.sin(wd_rad)
 
 ... 

⭐ For the remaining columns except for 1h_RF , divide them into average reading values to scale (normalize) and format.

...

 # Temperature:
 tem =self.df.pop('Tem')
 self.df['scaled_Tem'] =tem / 25
 # Humidity:
 hum =self.df.pop('Hum')
 self.df['scaled_Hum'] =hum / 70 
 # Barometric Pressure:
 bPR =self.df.pop('b_PR')
 self.df["scaled_bPR"] =bPR / 1013 
 # 24 Hour Rainfall (Approx.)
 rain_24 =self.df.pop('24h_RF')
 self.df['scaled_24h_RF'] =rain_24 / 24 

After completing scaling (normalizing), I extracted these new columns from the weather data set:

• scaled_WX
• scaled_WY
• scaled_max_WX
• scaled_max_WY
• scaled_Tem
• scaled_Hum
• scaled_bPR
• scaled_24h_RF

Step 5.2:Training the model (ANN) on the rainfall intensity classes

Before building and training a neural network model in TensorFlow, I needed to create the input array and the label array by preprocessing the scaled and normalized weather data set.

⭐ In the create_input_and_label() function:

⭐ Firstly, append each input element as a NumPy array to the input array and convert it to a NumPy array by using the asarray() función.

⭐ Each input element includes seven variables [shape=(7, )]:

• [scaled_WX, scaled_WY, scaled_max_WX, scaled_max_WY, scaled_Tem, scaled_Hum, scaled_bPR]

def create_input_and_label(self):
 n =len(self.df)
 # Create the input array using the scaled variables:
 for i in range(n):
 self.input.append(np.array([self.df['scaled_WX'][i], self.df['scaled_WY'][i], self.df['scaled_max_WX'][i], self.df['scaled_max_WY'][i], self.df['scaled_Tem'][i], self.df['scaled_Hum'][i], self.df['scaled_bPR'][i]]))
 self.input =np.asarray(self.input)
 
 ... 

⭐ Then, evaluate the approximate rainfall (precipitation) rate for each reading (row).

approx_RF_rate =(1h_RF + scaled_24h_RF) * 100

⭐ According to the rainfall rate, assign a class [0 - 4] for each input element and append them to the label array.

...
 
 for i in range(n):
 _class =0
 # Evaluate the approximate rainfall rate:
 approx_RF_rate =(self.df['1h_RF'][i] + self.df['scaled_24h_RF'][i]) * 100
 # As labels, assign classes of rainfall intensity according to the approximate rainfall rate (mm):
 if approx_RF_rate ==0:
 _class =0
 elif approx_RF_rate <2.5:
 _class =1
 elif 2.5  _class =2
 elif 7.6  _class =3
 else:
 _class =4
 self.label.append(_class)
 self.label =np.asarray(self.label) 

After preprocessing the scaled weather data to create input and label arrays, I split them as training (60%) and test (40%) data sets:

def split_data(self):
 n =len(self.df)
 # (60%, 40%) - (training, test)
 self.train_input =self.input[0:int(n*0.6)]
 self.test_input =self.input[int(n*0.6):]
 self.train_label =self.label[0:int(n*0.6)]
 self.test_label =self.label[int(n*0.6):] 

Then, I built my artificial neural network (ANN) model by using Keras and trained it with the training data set for nineteen epochs.

You can inspect these tutorials to learn about activation functions, loss functions, epochs, etc.

def build_and_train_model(self):
 # Build the neural network:
 self.model =keras.Sequential([
 keras.Input(shape=(7,)),
 keras.layers.Dense(16, activation='relu'),
 keras.layers.Dense(32, activation='relu'),
 keras.layers.Dense(64, activation='relu'),
 keras.layers.Dense(128, activation='relu'),
 keras.layers.Dense(5, activation='softmax')
 ])
 # Compile:
 self.model.compile(optimizer='adam', loss="sparse_categorical_crossentropy", metrics=['accuracy'])
 # Train:
 self.model.fit(self.train_input, self.train_label, epochs=19)
 
 ... 

After training with the preliminary training data set, the accuracy of the model is between 0.83 and 0.85 .

Step 5.3:Evaluating the model and making predictions on the rainfall intensity

After building and training my artificial neural network model, I tested its accuracy by using the preliminary testing data set.

For now, the evaluated accuracy of the model is between 0.72 and 0.73 due to inadequate testing data set overfitting the model. However, I am still collecting weather data to improve the model accuracy.

...
 
 # Test the accuracy:
 print("\n\nModel Evaluation:")
 test_loss, test_acc =self.model.evaluate(self.test_input, self.test_label) 
 print("Evaluated Accuracy:", test_acc) 

Then, I used my neural network model to make predictions on the rainfall intensity with a given prediction array consisting of readings from the weather station after the training of the model. As a starting point, the model works fine :)

The model predicts possibilities of labels for each input element as an array of 5 numbers. They represent the model's "confidence" that the given input element corresponds to each of the five different classes of rainfall intensity [0 - 4].

⭐ In the make_prediction() function, make a prediction for each input element in a given array and get the most accurate label [0 - 4] by using the argmax() function to display its class name.

def make_prediction(self, pre_array):
 print("\n\nModel Predictions:\n")
 prediction =self.model.predict(pre_array)
 for i in range(len(pre_array)):
 print("Prediction => ", self.class_names[np.argmax(prediction[i])]) 

Prediction Inputs:

• [0, 0, 0.31819805, 0.31819805, 0.6988, 0.81498571, 0.99349753]
• [0, -0, 0, -0, 0.8444, 1, 0.96835143]
• [0, 0, 0.45, 0, 0.87577, 0.95857143, 1.00128332]
• [-0, -0, -0, -0, 0.8224, 1.05714286, 0.99279368]

Prediction Outputs:

• 0 [None]
• 3 [Heavy Rain]
• 4 [Violent Rain]
• 4 [Violent Rain]

Connections and Adjustments (ESP8266)

// Connections
// NodeMCU ESP-12E (ESP8266) :
// Weather Station
// VV --------------------------- 5V
// D5 --------------------------- RX
// D6 --------------------------- TX
// G --------------------------- GND 

I connected the sensor (converter) board to the NodeMCU ESP-12E (ESP8266) development board and fastened them to a plastic box while collecting weather data at my balcony.

Connections and Adjustments (SIM808)

// Connections
// Arduino Uno:
// SIM808 GPS/GPRS/GSM Shield For Arduino
// D0 --------------------------- RX
// D1 --------------------------- TX
// D12 --------------------------- POWER 
// Weather Station
// 5V --------------------------- 5V
// D5 --------------------------- RX
// D6 --------------------------- TX
// GND --------------------------- GND 

⭐ Note:D0, D1, D12 pins are occupied by the SIM808 GPS/GPRS/GSM Shield.

⭐ Connect an external battery (7-23V) for the SIM808 module to work properly.

⭐ Attach the GPS antenna and the GSM antenna to the SIM808 shield.

⭐ Insert a SIM card into the SIM slot on the SIM808 shield.

⭐ Before uploading the code, set the function switch on the shield to None (1).

⭐ Upload the code.

⭐ Then, set the function switch to Arduino (3).

⭐ Press the Boot button on the shield until seeing the Net indicator LED flashing every 1 second and wait for the SIM card to register the network - the Net indicator LED will slowly flash every 3 seconds.

⭐ Click here to get more information about the SIM808 GSM/GPS/GPRS Shield.

After setting up the SIM808 shield with Arduino Uno, I connected the sensor (converter) board to it.

I placed all components into a plastic box to collect weather data outside, even in extreme weather conditions, since the sensor board is not weather-proof.

Then, I fastened the plastic box to the weather station.

Videos and Conclusion

After completing coding and assembling the weather station, I collated local weather data for more than three months every five minutes to build my neural network model successfully.

I am still collecting weather data at my balcony and backyard to improve my neural network model and its accuracy :)

Further Discussions

☔ 💧 Since we need local weather data sets to get more accurate predictions on weather phenomena, budget-friendly NN-enabled weather stations like this can be placed on farms and greenhouses to avert the detrimental effects of excessive rainfall on agriculture.

☔ 💧 As early warning systems working with neural networks, we can use them simultaneously to create a swarm of weather stations communicating and feeding each other to forecast rainfall intensity precisely in local areas.

☔ 💧 Also, we can utilize that information for assessing:

• vital water resources,
• agriculture,
• crop productivity,
• ecosystems,
• hydrology.

References

[1] Extreme weather - heavy rainfall , NIWA, https://niwa.co.nz/natural-hazards/extreme-weather-heavy-rainfall

[2] University of Illinois at Urbana-Champaign, News Bureau. "Excessive rainfall as damaging to corn yield as extreme heat, drought. " ScienceDaily. ScienceDaily, 30 April 2019. www.sciencedaily.com/releases/2019/04/190430121744.htm.

[3] Andrew Culclasure, Using Neural Networks to Provide Local Weather Forecasts , Georgia Southern University, Spring 2013, 29https://digitalcommons.georgiasouthern.edu/cgi/viewcontent.cgi?article=1031&context=etd.

Código

• IoT_weather_station_neural_network.py
• ESP12E_remote_weather_station.ino
• SIM808_remote_weather_station.ino
• index.php (web application)
• account_verification_token.php (web application)

IoT_weather_station_neural_network.pyPython

# IoT Weather Station Predicting Rainfall Intensity w/ TensorFlow# Windows, Linux, or Ubuntu# By Kutluhan Aktar# Collates weather data on Google Sheets and interprets it with a neural network built in TensorFlow to make predictions on the rainfall intensity. # For more information:# https://www.theamplituhedron.com/projects/IoT_Weather_Station_Predicting_Rainfall_Intensity_with_TensorFlowimport tensorflow as tffrom tensorflow import kerasimport matplotlib.pyplot as pltimport numpy as npimport pandas as pd# Create a class to build a neural network after getting, visualizing, and scaling (normalizing) weather data.class Weather_Station:def __init__(self, data):self.df =data self.input =[] self.label =[] # Define class names for different rainfall intensity predictions and values. self.class_names =['None', 'Light Rain', 'Moderate Rain', 'Heavy Rain', 'Violent Rain'] # Create graphics for requested columns. def graphics(self, column_1, column_2, xlabel, ylabel):# Show requested columns from the data set:plt.style.use("dark_background") plt.gcf().canvas.set_window_title('IoT Weather Station') plt.hist2d(self.df[column_1], self.df[column_2]) plt.colorbar() plt.xlabel(xlabel) plt.ylabel(ylabel) plt.title(xlabel) plt.show() # Visualize data before creating and feeding the neural network model. def data_visualization(self):# Inspect requested columns to build a model with appropriately formatted data:self.graphics('WD', '1h_RF', 'Wind Direction (deg)', 'One-Hour Rainfall (mm)') self.graphics('Av_WS', '1h_RF', 'Average Wind Speed (m/s)', 'One-Hour Rainfall (mm)') self.graphics('Mx_WS', '1h_RF', 'Maximum Wind Speed (m/s)', 'One-Hour Rainfall (mm)') self.graphics('24h_RF', '1h_RF', '24-Hour Rainfall (mm)', 'One-Hour Rainfall (mm)') self.graphics('Tem', '1h_RF', 'Temperature (C)', 'One-Hour Rainfall (mm)') self.graphics('Hum', '1h_RF', 'Humidity (%)', 'One-Hour Rainfall (mm)') self.graphics('b_PR', '1h_RF', 'Barometric Pressure (hPA)', 'One-Hour Rainfall (mm)') # Scale (normalize) data depending on the neural network model. def scale_data(self):# Wind Direction and Speed:wv =self.df.pop('Av_WS') max_wv =self.df.pop('Mx_WS') # Convert to radians. wd_rad =self.df.pop('WD')*np.pi / 180 # Calculate the wind x and y components. self.df['scaled_WX'] =wv*np.cos(wd_rad) self.df['scaled_WY'] =wv*np.sin(wd_rad) # Calculate the max wind x and y components. self.df['scaled_max_WX'] =max_wv*np.cos(wd_rad) self.df['scaled_max_WY'] =max_wv*np.sin(wd_rad) # Temperature:tem =self.df.pop('Tem') self.df['scaled_Tem'] =tem / 25 # Humidity:hum =self.df.pop('Hum') self.df['scaled_Hum'] =hum / 70 # Barometric Pressure:bPR =self.df.pop('b_PR') self.df["scaled_bPR"] =bPR / 1013 # 24 Hour Rainfall (Approx.) rain_24 =self.df.pop('24h_RF') self.df['scaled_24h_RF'] =rain_24 / 24 # Define the input and label arrays. def create_input_and_label(self):n =len(self.df) # Create the input array using the scaled variables:for i in range(n):self.input.append(np.array([self.df['scaled_WX'][i], self.df['scaled_WY'][i], self.df['scaled_max_WX'][i], self.df['scaled_max_WY'][i], self.df['scaled_Tem'][i], self.df['scaled_Hum'][i], self.df['scaled_bPR'][i]])) self.input =np.asarray(self.input) # Create the label array using the one-hour and 24-hour rainfall variables:for i in range(n):_class =0 # Evaluate the approximate rainfall rate:approx_RF_rate =(self.df['1h_RF'][i] + self.df['scaled_24h_RF'][i]) * 100 # As labels, assign classes of rainfall intensity according to the approximate rainfall rate (mm):if approx_RF_rate ==0:_class =0 elif approx_RF_rate <2.5:_class =1 elif 2.5  ", self.class_names[np.argmax(prediction[i])]) # Save the model for further usage without training steps:def save_model(self):self.model.save("E:\PYTHON\weather_station.h5") # Run Artificial Neural Network (ANN):def Neural_Network(self, save):self.scale_data() self.create_input_and_label() self.split_data() self.build_and_train_model() if save ==True:self.save_model() # Example Input and Layer:print("\nScaled Input [EXP]:\n") print(self.train_input[0]) print("\nScaled Label [EXP]:\n") print(self.train_label[0]) # Read data (Remote Weather Station.csv):csv_path ="E:\PYTHON\Remote Weather Station.csv"df =pd.read_csv(csv_path)# Define a new class object named 'station':station =Weather_Station(df)# Visualize data:#station.data_visualization()# Artificial Neural Network (ANN):station.Neural_Network(False)# Enter inputs for making predictions:prediction_array =np.array([ [0, 0, 0.31819805, 0.31819805, 0.6988, 0.81498571, 0.99349753], [0, -0, 0, -0, 0.8444, 1, 0.96835143], [0, 0, 0.45, 0, 0.87577, 0.95857143, 1.00128332], [-0, -0, -0, -0, 0.8224, 1.05714286, 0.99279368]])# Prediction Results:station.make_prediction(prediction_array)

ESP12E_remote_weather_station.inoArduino

 ///////////////////////////////////////////// // IoT Weather Station Predicting // // Rainfall Intensity w/ TensorFlow // // ------------------------ // // NodeMCU (ESP-12E) // // by Kutluhan Aktar // // // ///////////////////////////////////////////////// Collates weather data on Google Sheets and interprets it with a neural network built in TensorFlow to make predictions on the rainfall intensity. //// For more information:// https://www.theamplituhedron.com/projects/IoT_Weather_Station_Predicting_Rainfall_Intensity_with_TensorFlow//// Connections// NodeMCU (ESP-12E) :// Weather Station// VV --------------------------- 5V// D5 --------------------------- RX// D6 --------------------------- TX// G --------------------------- GND// Include required libraries:#include #include  #include #include #include // Define your WiFi settings.const char *ssid ="";const char *password ="";// Define weather station settings:char databuffer[35];double temp;int transferring =0;// Define the serial connection pins - RX and TX.SoftwareSerial Serial_1(D6, D5); // (Rx, Tx)void setup() { // Wait until connected. retraso (1000); // Initiate serial ports:Serial.begin(115200); Serial_1.begin(9600); // It is just for assuring if connection is alive. WiFi.mode(WIFI_OFF); retraso (1000); // This mode allows NodeMCU to connect any WiFi directly. WiFi.mode(WIFI_STA); // Connect NodeMCU to your WiFi. WiFi.begin(ssid, password); Serial.print("\n\n"); Serial.print("Try to connect to WiFi. Please wait! "); Serial.print("\n\n"); // Halt the code until connected to WiFi. while (WiFi.status() !=WL_CONNECTED) { delay(500); Serial.print("*"); } // If connection is successful:Serial.print("\n\n"); Serial.print("-------------------------------------"); Serial.print("\n\n"); Serial.print("Connection is successful!"); Serial.print("\n\n"); Serial.print("Connected WiFi SSID :"); Serial.print(ssid); Serial.print("\n\n"); Serial.println("Connected IPAddress :"); Serial.println(WiFi.localIP()); Serial.print("\n\n");}void loop() { // Get data from the remote weather station:getBuffer(); // Debug the information and create the link:String weather_data ="wd=" + String(WindDirection()) + "&a_ws=" + String(WindSpeedAverage()) + "&m_ws=" + String(WindSpeedMax()) + "&1_rf=" + String(RainfallOneHour()) + "&24_rf=" + String(RainfallOneDay()) + "&tem=" + String(Temperature()) + "&hum=" + String(Humidity()) + "&b_pr=" + String(BarPressure()); String server ="http://192.168.1.24/remote_weather_station/?"; Serial.println("Weather Data => " + weather_data); Serial.println("Buffer => " + String(databuffer)); // Send data packets every 5 minutes to Raspberry Pi (or any server). transferring++; Serial.println("Time => " + String(transferring) + "s / " + String(int(5*60)) + "s\n\n"); if(transferring ==5*60){ // Create the HTTP object to make a request to the server. HTTPClient http; http.begin(server + weather_data); int httpCode =http.GET(); String payload =http.getString(); Serial.println("Data Send...\nHTTP Code => " + String(httpCode) + "\nServer Response => " + payload + "\n\n"); http.end(); transferring =0; } // Wait 1 second... delay(1000);}// WEATHER STATIONvoid getBuffer(){ int index; for (index =0;index <35;index ++){ if(Serial_1.available()){ databuffer[index] =Serial_1.read(); if (databuffer[0] !='c'){ index =-1; } } else{ index --; } }}int transCharToInt(char *_buffer, int _start, int _stop){ int _index; int result =0; int num =_stop - _start + 1; int _temp[num]; for (_index =_start;_index <=_stop;_index ++){ _temp[_index - _start] =_buffer[_index] - '0'; result =10*result + _temp[_index - _start]; } return result;}int WindDirection(){ return transCharToInt(databuffer,1,3); } // Wind Direction (deg)float WindSpeedAverage(){ temp =0.44704 * transCharToInt(databuffer,5,7); return temp; } // Average Air Speed (1 minute)float WindSpeedMax(){ temp =0.44704 * transCharToInt(databuffer,9,11); return temp; } //Max Air Speed (5 minutes)float Temperature(){ temp =(transCharToInt(databuffer,13,15) - 32.00) * 5.00 / 9.00; return temp; } // Temperature ("C")float RainfallOneHour(){ temp =transCharToInt(databuffer,17,19) * 25.40 * 0.01; return temp; } // Rainfall (1 hour)float RainfallOneDay(){ temp =transCharToInt(databuffer,21,23) * 25.40 * 0.01; return temp; } // Rainfall (24 hours)int Humidity(){ return transCharToInt(databuffer,25,26); } // Humidity (%)float BarPressure(){ temp =transCharToInt(databuffer,28,32); return temp / 10.00; } // Barometric Pressure (hPA)

SIM808_remote_weather_station.inoArduino

 ///////////////////////////////////////////// // IoT Weather Station Predicting // // Rainfall Intensity w/ TensorFlow // // ------------------------ // // Arduino Uno (SIM808) // // by Kutluhan Aktar // // // ///////////////////////////////////////////////// Collates weather data on Google Sheets and interprets it with a neural network built in TensorFlow to make predictions on the rainfall intensity. //// For more information:// https://www.theamplituhedron.com/projects/IoT_Weather_Station_Predicting_Rainfall_Intensity_with_TensorFlow//// Connections// Arduino Uno:// SIM808 GPS/GPRS/GSM Shield For Arduino// D0 --------------------------- RX// D1 --------------------------- TX// D12 --------------------------- POWER // Weather Station// 5V --------------------------- 5V// D5 --------------------------- RX// D6 --------------------------- TX// GND --------------------------- GND// Include required libraries:#include #include // Define the sim808.DFRobot_SIM808 sim808(&Serial);// Define weather station settings:char databuffer[35];double temp;int transferring =0;// Define the serial connection pins - RX and TX.SoftwareSerial Serial_1(6, 5); // (Rx, Tx)void setup() { // Wait until connected. retraso (1000); // Initiate serial ports:Serial.begin(9600); Serial_1.begin(9600); //******** Initialize sim808 module ************* while(!sim808.init()) { delay(1000); Serial.print("Sim808 init error\r\n"); } delay(2000); // Continue if the SIM808 Module is working accurately. Serial.println("Sim808 init success"); delay(5000);}void loop() { // Get data from the remote weather station:getBuffer(); // Debug the information and create the link:String weather_data_1 ="wd=" + String(WindDirection()) + "&a_ws=" + String(WindSpeedAverage()) + "&m_ws=" + String(WindSpeedMax()); String weather_data_2 ="&1_rf=" + String(RainfallOneHour()) + "&24_rf=" + String(RainfallOneDay()) + "&tem=" + String(Temperature()); String weather_data_3 ="&hum=" + String(Humidity()) + "&b_pr=" + String(BarPressure()); Serial.print("Weather Data => " + weather_data_1); Serial.print(weather_data_2); Serial.println(weather_data_3); Serial.println("Buffer => " + String(databuffer)); // Send data packets every 5 minutes to the server. transferring++; Serial.println("Time => " + String(transferring) + "s / " + String(int(5*60)) + "s\n\n"); if(transferring ==5*60){ //*********** Attempt DHCP ******************* while(!sim808.join(F("cmnet"))) { Serial.println("Sim808 join network error!"); retraso (2000); } //************ Successful DHCP **************** delay(5000); Serial.println("Successful DHCP"); //*********** Establish a TCP connection ************ if(!sim808.connect(TCP,"192.168.1.24", 80)) { // Change it with your server. Serial.println("Connection Error"); }else{ Serial.println("Connection OK"); } delay(2000); //*********** Send a GET request ***************** String line ="GET /remote_weather_station/?" + weather_data_1 + weather_data_2 + weather_data_3 + " HTTP/1.0\r\n\r\n"; Serial.println (línea); char buffer[512]; // Convert the line from string to char array to make an HTTP Get Request with the SIM808. char web_hook[110]; String_to_Char(line, 110, web_hook); sim808.send(web_hook, sizeof(web_hook)-1); while (true) { int ret =sim808.recv(buffer, sizeof(buffer)-1); if (ret <=0){ Serial.println("Fetch Over..."); descanso; } // Uncomment to view the response in the serial monitor. /* buffer[ret] ='\0'; Serial.print("Recv:"); Serial.print(ret); Serial.print(" bytes:"); Serial.println(buffer); */ Serial.println("\nData Send"); descanso; } //************* Close TCP or UDP connections ********** sim808.close(); //*** Disconnect wireless connection, Close Moving Scene ******* sim808.disconnect(); // Exit. transferring =0; } // Wait 1 second... delay(1000);}// WEATHER STATIONvoid getBuffer(){ int index; for (index =0;index <35;index ++){ if(Serial_1.available()){ databuffer[index] =Serial_1.read(); if (databuffer[0] !='c'){ index =-1; } } else{ index --; } }}int transCharToInt(char *_buffer, int _start, int _stop){ int _index; int result =0; int num =_stop - _start + 1; int _temp[num]; for (_index =_start;_index <=_stop;_index ++){ _temp[_index - _start] =_buffer[_index] - '0'; result =10*result + _temp[_index - _start]; } return result;}int WindDirection(){ return transCharToInt(databuffer,1,3); } // Wind Direction (deg)float WindSpeedAverage(){ temp =0.44704 * transCharToInt(databuffer,5,7); return temp; } // Average Air Speed (1 minute)float WindSpeedMax(){ temp =0.44704 * transCharToInt(databuffer,9,11); return temp; } //Max Air Speed (5 minutes)float Temperature(){ temp =(transCharToInt(databuffer,13,15) - 32.00) * 5.00 / 9.00; return temp; } // Temperature ("C")float RainfallOneHour(){ temp =transCharToInt(databuffer,17,19) * 25.40 * 0.01; return temp; } // Rainfall (1 hour)float RainfallOneDay(){ temp =transCharToInt(databuffer,21,23) * 25.40 * 0.01; return temp; } // Rainfall (24 hours)int Humidity(){ return transCharToInt(databuffer,25,26); } // Humidity (%)float BarPressure(){ temp =transCharToInt(databuffer,28,32); return temp / 10.00; } // Barometric Pressure (hPA)void String_to_Char(String _String, int _size, char _convert[]){ for(int i=0;i<_size;i++){ _convert[i] =_String[i]; }} 

index.php (web application)PHP

 (int)$_GET['wd'], "a_ws" => (float)$_GET['a_ws'], "m_ws" => (float)$_GET['m_ws'], "1_rf" => (float)$_GET['1_rf'], "24_rf" => (float)$_GET['24_rf'], "tem" => (float)$_GET['tem'], "hum" => (int)$_GET['hum'], "b_pr" => (float)$_GET['b_pr'] ];}else{ $variables_from_module =[ "wd" => "err", "a_ws" => "err", "m_ws" => "err", "1_rf" => "err", "24_rf" => "err", "tem" => "err", "hum" => "err", "b_pr" => "err" ];}/** * Returns an authorized API client. * @return Google_Client the authorized client object */function getClient(){ $client =new Google_Client(); $client->setApplicationName('Remote Weather Station'); // Enter your application name. $client->setScopes('https://www.googleapis.com/auth/spreadsheets'); $client->setAuthConfig('credentials.json'); $client->setAccessType('offline'); $client->setPrompt('select_account consent'); // Load previously authorized token from a file, if it exists. // The file token.json stores the user's access and refresh tokens, and is // created automatically when the authorization flow completes for the first // time. $tokenPath ='token.json'; if (file_exists($tokenPath)) { $accessToken =json_decode(file_get_contents($tokenPath), true); $client->setAccessToken($accessToken); print("Token Found!"); } // If there is no previous token or it's expired. if ($client->isAccessTokenExpired()) { // Refresh the token if possible, else fetch a new one. if ($client->getRefreshToken()) { $client->fetchAccessTokenWithRefreshToken($client->getRefreshToken()); } else { // Request authorization from the user. $authUrl =$client->createAuthUrl(); // Do not forget to refresh the page after getting the verification code and entering it to the account_verification_token.php. printf("Open the following link in your browser:

%s

", $authUrl); // <=Comment print 'Do not forget to refresh the page after getting the verification code and entering it to the account_verification_token.php.

Set the verification code in the account_verification_token.php file.'; // <=Comment // Set the verification code to create the token.json. $authCode =trim($GLOBALS['account_verification_token']); // Exchange authorization code for an access token. $accessToken =$client->fetchAccessTokenWithAuthCode($authCode); $client->setAccessToken($accessToken); // Check to see if there was an error and the account_verification_token is entered. if (array_key_exists('error', $accessToken)) { throw new Exception(join(', ', $accessToken)); }else{ print("Successful! Refresh the page."); } } // Save the token to a file. if (!file_exists(dirname($tokenPath))) { mkdir(dirname($tokenPath), 0700, true); } file_put_contents($tokenPath, json_encode($client->getAccessToken())); } return $client;}// Get the API client and construct the service object.$client =getClient();$service =new Google_Service_Sheets($client);// Enter your spreadsheetId:$spreadsheetId ='';// Enter the range (the first row) under which new values will be appended (8 rows):$range ='A1:H1';// Append recent findings from the weather station to the spreadsheet.$values =[ [$variables_from_module["wd"], $variables_from_module["a_ws"], $variables_from_module["m_ws"], $variables_from_module["1_rf"], $variables_from_module["24_rf"], $variables_from_module["tem"], $variables_from_module["hum"], $variables_from_module["b_pr"]]];$body =new Google_Service_Sheets_ValueRange([ 'values' => $values]);$params =[ 'valueInputOption' => "RAW"];// Append if only requested!if(isset($_GET['wd']) &&isset($_GET['a_ws']) &&isset($_GET['m_ws']) &&isset($_GET['1_rf']) &&isset($_GET['24_rf']) &&isset($_GET['tem']) &&isset($_GET['hum']) &&isset($_GET['b_pr'])){ $result =$service->spreadsheets_values->append($spreadsheetId, $range, $body, $params); printf("

%d cells appended.", $result->getUpdates()->getUpdatedCells());}else{ print ("
Missing Data!");}

account_verification_token.php (web application)PHP

"; // Enter the verification code after authorization. ?>

Piezas y carcasas personalizadas

remote_weather_station_OobOCQNPSW.csv

Esquemas

Juego de dinosaurios de Chrome controlado en la vida real E-Health:Sensores de bajo costo para la detección temprana de enfermedades