一种基于多LSTM模型融合的降水量预测方法以及装置与流程Matlab 代码

以下是基于多LSTM模型融合的降水量预测方法的Matlab代码示例：

% 数据准备
data = readtable('rainfall_data.csv');
data.rainfall(isnan(data.rainfall)) = 0;
data = table2array(data);
scaler = fitlm(data);
scaled_data = predict(scaler, data(:, 2:end));

% 构建多个LSTM模型
num_models = 5;
look_back = 12;
train_size = floor(size(scaled_data, 1) * 0.7);
X_train = cell(num_models, 1);
X_test = cell(num_models, 1);
Y_train = cell(num_models, 1);
Y_test = cell(num_models, 1);
for i = 1:num_models
    trainX = [];
    trainY = [];
    testX = [];
    testY = [];
    for j = 1:(size(scaled_data, 1)-look_back-1)
        if rand < 0.7
            trainX = [trainX; scaled_data(j:(j+look_back-1), :)];
            trainY = [trainY; scaled_data(j+look_back, :)];
        else
            testX = [testX; scaled_data(j:(j+look_back-1), :)];
            testY = [testY; scaled_data(j+look_back, :)];
        end
    end
    X_train{i} = trainX;
    Y_train{i} = trainY;
    X_test{i} = testX;
    Y_test{i} = testY;
end

% 训练多个LSTM模型
models = cell(num_models, 1);
for i = 1:num_models
    model = lstmModel(50, look_back, size(scaled_data, 2), 1, 'adam', 'mse');
    models{i} = trainLSTM(model, X_train{i}, Y_train{i}, 'shuffle', true, ...
        'epoch', 20, 'miniBatchSize', 1, 'verbose', true);
end

% 对多个LSTM模型的预测结果进行加权平均
predictions = zeros(size(X_test{1}, 1), 1);
for i = 1:num_models
    pred = predictLSTM(models{i}, X_test{i});
    predictions = predictions + pred;
end
avg_pred = predictions / num_models;

% 对结果进行评估
mse = mean((avg_pred - Y_test{1}).^2);
rmse = sqrt(mse);
fprintf('RMSE: %.3f\n', rmse);

该代码示例包括数据准备、构建多个LSTM模型、训练多个LSTM模型、对多个LSTM模型的预测结果进行加权平均和对结果进行评估等步骤。其中，num_models是LSTM模型的个数，look_back是时间步长，train_size是训练集大小，X_train、Y_train、X_test和Y_test分别是训练集和测试集的输入和输出数据。训练模型时，采用了随机抽样的方法，提高了模型的泛化能力。最后，通过计算均方根误差（RMSE）来评估模型的预测精度。