智领未来:人工智能在空气质量检测与污染治理中的应用代码实例
时间: 2024-11-09 09:18:39 浏览: 23
在智领未来的人工智能应用于空气质量检测中,常见的做法是结合传感器数据和机器学习算法进行实时监测和预警。这里是一个简单的Python代码示例,利用TensorFlow构建一个基于时间序列的空气质量预测模型(如LSTM或GRU):
```python
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
# 加载并预处理数据
data = pd.read_csv('air_quality_data.csv') # 假设数据已经清洗好
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data['AirQualityIndex'].values.reshape(-1, 1))
# 划分训练集和测试集
train_size = int(len(scaled_data) * 0.8)
train_data, test_data = scaled_data[:train_size], scaled_data[train_size:]
# 将数据转换成适合LSTM的形状
train_data = np.reshape(train_data, (train_data.shape[0], 1, train_data.shape[1]))
test_data = np.reshape(test_data, (test_data.shape[0], 1, test_data.shape[1]))
# 构建模型
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(train_data.shape[1], 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=50))
model.add(Dropout(0.2))
model.add(Dense(units=1))
# 编译模型
model.compile(optimizer='adam', loss='mean_squared_error')
# 训练模型
model.fit(train_data, train_labels, epochs=50, batch_size=1)
# 预测和评估
predictions = model.predict(test_data)
inverse_predictions = scaler.inverse_transform(predictions)
#
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