It have been proved that LoRa networks are suitable to do the localization for outdoor environment  . Through the performance investigation in simulation models and real experiments, it showed that the performance of RSSI-based LoRa localization algorithms in  is compatible to The Global Positioning System (GPS), the most popular outdoor localization system. However, in very noisy outdoor environment, the performance of the algorithms degrades significantly because the effect of noisy nodes (anchor nodes that are highly affected by noise) cannot be totally avoided in localization. Based on this observation, we propose two new RSSI-based LoRa localization algorithms to further improve the accuracy of the localization for very noisy outdoor environment. One new algorithm iteratively removes all noisy nodes and use the remaining anchor nodes to process the localization; while the other new algorithm uses density clustering to get the best estimation. Our performance investigation shows that the proposed algorithms outperform the algorithms in  significantly in terms of the localization error if the outdoor environment is very noisy.