请用英文翻译：在任务2中，我们首先分析了四类土地的特点。随后我们对数据进行了标准化处理与主成分分析。之后，我们进行了KMO检验、Barttle球状检验的共线性检验，来判断主成分分析的可行性。在主成分分析的基础上，我们选用BP神经网络算法，加权求出新的变量。经过多次训练后，发现回归效果显著，以此为基础，针对四个典型地区做出了相应权重的修正。

In Task 2, we first analyzed the characteristics of the four types of land. Then we standardized the data and performed principal component analysis. Afterwards, we conducted KMO test and Barttle sphere test to assess the feasibility of principal component analysis. Based on the principal component analysis, we selected BP neural network algorithm to calculate the new variables with weighting. After multiple trainings, we found that the regression effect was significant. On this basis, we made corresponding weight corrections for the four typical regions.

相关问题

请用英文翻译：在任务5中，我们总结了模型的优点与劣势，并进行了灵敏性检验。最后，根据任务4中得到的针对陕西省西安市最有效的策略，我们绘制了醒目而美观的传单。

In Task 5, we summarized the advantages and disadvantages of the model and conducted sensitivity tests. Finally, based on the most effective strategy obtained in Task 4 for Xi'an City, Shaanxi Province, we drew an eye-catching and attractive leaflet.

请用英文翻译：在任务3中，我们根据光污染产生原理与光污染影响原理，提出了三种可能有效的干预策略。并根据策略，向已有的光污染风险综合评价模型引入新的参数，以评估干预策略的影响。随后通过数据检验，说明了三种干预策略都是有效的。

In Task 3, we proposed three possible effective intervention strategies based on the principles of light pollution generation and light pollution impact. And based on these strategies, new parameters were introduced into the existing light pollution risk comprehensive evaluation model to evaluate the impact of intervention strategies. Then, through data verification, it is shown that the three intervention strategies are effective.