ITOGON, Benguet — Local legislators in Itogon are questioning whether the Landslide Early Warning System (LEWS) installed in their municipality is effectively providing early alerts before landslides occur.
During the regular session on Monday, Itogon Councilor Alejandro Palangdan raised concerns about the system’s efficiency and coverage, asking for clarification on the radius of the area that can be monitored by the LEWS.
The LEWS, funded by the Department of Science and Technology (DOST) under the Calamity Program and installed two years ago by the University of the Cordilleras (UC) in Barangay Ampucao, is designed to predict possible landslides by monitoring rainfall and soil moisture.
Palangdan posed the question while the council was discussing a proposal authorizing the mayor to enter into a Memorandum of Agreement (MOA) with a private company for the operation and maintenance of the warning system.
He cited recent incidents of landslides triggered by typhoons and the southwest monsoon, questioning whether the LEWS had provided any actionable data that could have warned the local government unit (LGU) and residents beforehand.
“Before entering into an agreement with the company, it’s better to clarify first whether the early warning device is really working,” Palangdan said, adding that the municipality appeared not to have received any warning data from the system before the disasters.
Councilor Ernani Pis-oy supported the call for an inquiry, noting that two landslides occurred recently in Barangays Acupan and Goldfield—both within the 50-meter radius of where the LEWS was installed.
Pis-oy emphasized that the LGU should first assess the system’s effectiveness before approving a MOA that would cost ₱500,000 annually for maintenance.
The Municipal Council referred the matter to its Committee on Laws, which will conduct a consultation or inquiry with the officials or institutions responsible for the LEWS project.
LEWS uses sensors to collect real-time data on rainfall, soil movement, and moisture levels, analyzing them through predictive models to determine landslide risk and issue alerts aimed at saving lives and minimizing damage.