사물인터넷 | TensorFlow 기상 관측소는 강우 강도를 예측합니다

⭐ 스프레드시트 ID와 범위를 입력하여 기상 관측소의 최근 결과를 스프레드시트에 추가합니다.

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

// 스프레드시트 ID 입력:
$spreadsheetId ='';
// 새 값이 추가될 범위(첫 번째 행) 입력(8행) :
$range ='A1:H1';
// 기상 관측소의 최근 결과를 스프레드시트에 추가합니다.
$values ​​=[
 [$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"_ $variables_from_module["b_pr"]]
];
$body =new Google_Service_Sheets_ValueRange([
 '값' => $values
]);
$params =[
 'valueInputOption' => "RAW"
];

...

 $result =$service->spreadsheets_values->append( $spreadsheetId, $range, $body, $params);
 printf("

%d개의 셀이 추가되었습니다.", $result->getUpdates()->getUpdatedCells()); 

💻 account_verification_token.php

⭐ 생성된 AuthUrl으로 웹 애플리케이션을 승인한 후 , 주어진 인증 코드를 입력하여 액세스 토큰을 설정하십시오.

$account_verification_token ="<토큰 입력>"; // 인증 후 인증번호를 입력하세요. 

3.1단계:Raspberry Pi(또는 모든 서버)에서 웹 애플리케이션 설정

PHP 웹 애플리케이션을 만든 후 내 라즈베리 파이에서 실행하기로 했지만 PHP 서버라면 어떤 서버에서도 실행할 수 있습니다.

Raspberry Pi를 사용하고 싶지만 Raspberry Pi에서 LAMP 웹 서버를 설정하는 방법을 모르는 경우 이 튜토리얼을 살펴보세요.

⭐ 우선 아파치 서버는 보호된 위치이므로 터미널을 이용하여 애플리케이션 폴더(remote_weather_station)를 아파치 서버(/var/www/html)로 이동합니다.

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

⭐ 웹 애플리케이션을 처음 열 때 인증 코드 누락으로 인해 오류가 발생합니다. 인증 코드를 받으려면 애플리케이션에서 생성된 인증 링크로 이동하십시오.

⭐ 그런 다음 원격 기상 관측소로 이동(안전하지 않음)을 클릭합니다. .

⭐ 필요한 권한을 애플리케이션에 부여합니다.

⭐ 이제 인증 코드를 복사하여 account_verification_token.php에 붙여넣으세요. .

⭐ 웹 애플리케이션으로 돌아갑니다. 확인 메시지가 표시되어야 합니다. 성공! 페이지 새로고침 .

⭐ 페이지를 새로고침한 후 웹 애플리케이션은 인증 코드를 사용하여 액세스 토큰을 가져오고 token.json이라는 파일에 액세스 토큰을 저장합니다. . 따라서 다시 오류가 발생하지 않습니다.

⭐ PHP 웹 애플리케이션이 새 데이터를 주어진 스프레드시트에 정확하게 추가하는지 테스트하려면:

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

4단계:ESP8266(WiFi)으로 날씨 데이터 읽기 및 보내기

발코니에서 기상 데이터를 수집하기 위해 기상 관측소와 함께 NodeMCU ESP-12E(ESP8266) 개발 보드를 사용했습니다.

5분마다 PHP 웹 애플리케이션에 날씨 데이터를 보내도록 프로그래밍했습니다.

⭐ 필수 라이브러리를 포함하고 WiFi 설정을 정의합니다.

#include 
#include  
#include 
#include 
 #include 

// WiFi 설정을 정의합니다.
const char *ssid ="";
const char *password =" "; 

// 기상 관측소 설정 정의:
char databuffer[35];
double temp;
int transfer =0;

// Define 직렬 연결 핀 - RX 및 TX.
SoftwareSerial Serial_1(D6, D5); // (수신, 전송) 

void getBuffer(){
 int index;
 for (index =0;index <35;index ++){
 if(Serial_1.available()){ 
 데이터 버퍼[인덱스] =Serial_1.read();
 if (databuffer[0] !='c'){
 인덱스 =-1;
 }
 }
 else{
 인덱스 --;
 }
 }
} 

문자열 weather_data ="wd=" + 문자열(WindDirection()) + "&a_ws=" + 문자열(WindSpeedAverage()) + "&m_ws=" + 문자열(WindSpeedMax()) + "&1_rf=" + String(RainfallOneHour()) + "&24_rf=" + String(RainfallOneDay()) + "&tem=" + String(Temperature()) + "&hum=" + String(Humidity()) + "&b_pr=" + String( BarPressure());
문자열 서버 ="http://192.168.1.24/remote_weather_station/?";

...

int WindDirection(){ return transCharToInt(databuffer,1,3); } // 바람의 방향(deg)

float WindSpeedAverage(){ temp =0.44704 * transCharToInt(databuffer,5,7); 반환 온도; } // 평균 공기 속도(1분)

float WindSpeedMax(){ temp =0.44704 * transCharToInt(databuffer,9,11); 반환 온도; } //최대 공기 속도(5분)

float Temperature(){ temp =(transCharToInt(databuffer,13,15) - 32.00) * 5.00 / 9.00; 반환 온도; } // 온도("C")

float RainfallOneHour(){ temp =transCharToInt(databuffer,17,19) * 25.40 * 0.01; 반환 온도; } // 강우량(1시간)

float RainfallOneDay(){ temp =transCharToInt(databuffer,21,23) * 25.40 * 0.01; 반환 온도; } // 강우량(24시간)

int Humidity(){ return transCharToInt(databuffer,25,26); } // 습도(%)

float BarPressure(){ temp =transCharToInt(databuffer,28,32); 반환 온도 / 10.00; } // 기압(hPA) 

⭐ 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:

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:

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):

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

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:

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:

⭐ 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:

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() 기능.

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

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:

Prediction Outputs:

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:

참조

[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.

코드

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. 지연(1000); // Initiate serial ports:Serial.begin(115200); Serial_1.begin(9600); // It is just for assuring if connection is alive. WiFi.mode(WIFI_OFF); 지연(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. 지연(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!"); 지연(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(줄); 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..."); 부서지다; } // 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"); 부서지다; } //************* 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. ?>

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