Location Selection for Air Quality Monitoring with Consideration of Limited Budget and Estimation Error

In this paper, a general location selection strategy is proposed based on active learning, which involves iterations of a selector and an estimator. We implement four instances of this general strategy to embody it: KAL (Active Learning based on Kriging), TAL (Active Learning based on Regression Tree), KMAL (Active Learning based on Kriging and MPGR) and TMAL (Active Learning based on Regression Tree and MPGR). The estimator of KAL or TAL can estimate the air quality at remaining locations from air quality samples at monitoring locations leveraging spatial or cross-domain correlation of air quality. The selecting indicators of their selectors are designed to measure the uncertainty of unlabeled samples according to their estimators. KMAL and TMAL are upgrades of the former two respectively, by introducing MPGR (Manifold Preserving Graph Reduction) to also take the representativeness of unlabeled samples into account. The experimental results show that the proposed strategy can achieve low estimation error with few monitoring locations. Particularly, given the same budget (i.e., the number of monitoring locations), the estimation error is reduced from about 20% of baselines to 15% by KAL and to 5% by KMAL, and TAML likewise.