What question did this study set out to answer?

The study aims to estimate the impact of land use changes on total suspended solids in the Davao River and to forecast future trends.

January 23, 2026

Estimation and modeling of total suspended solids of Davao River: Basis for sustainable resource management

Key Points

The study aims to estimate the impact of land use changes on total suspended solids in the Davao River and to forecast future trends.
Estimated TSS impacts from land use and land cover changes from 2016 to 2023.
Used Cellular Automata-Artificial Neural Network for predictions in QGIS.
Applied the Mann-Kendall Test to identify trends in water monitoring stations.
Developed predictive models for 2030 and 2050 with a Kappa coefficient verification.
Predictive models for 2030 and 2050 yielded Kappa coefficients of 0.94 and 0.89, indicating high accuracy.
Changes in land use showed increases in built-up and cultivated areas, correlating with TSS increases.
All monitoring stations, except 7, 9, and 10, exceeded TSS thresholds by 2030; by 2050, stations 7 and 9 also exceeded thresholds.
A trend of increasing TSS is observed at 14 water monitoring stations, highlighting growing concerns.

Abstract

Watersheds play a significant part in ecological and hydrological balance; however, natural phenomena and anthropogenic activities continue to threaten this harmony. The quality of the riverine environments can be measured in terms of the water quality parameters such as Total Suspended Solids (TSS). This study aimed to estimate the TSS impacts of TSS by Land Use and Land Cover (LULC) changes of Davao River from 2016 to 2023 and forecast in 2030 and 2050 utilizing Cellular Automata-Artificial Neural Network through QGIS and identify trend using Mann-Kendall Test. Based on the results, the 2030 and 2050 predictive models produced a Kappa coefficient of 0.94 and 0.89, respectively. The 2016 to 2023 LULC percent change revealed that built-up, cultivated and High TSS areas increased to 5.83, 38.18 and 48.23, respectively while forest and shrublands decreased by 12.05 and 11.43, respectively. The study showed increasing trend for 14 water monitoring stations but no trend for stations 3d, 4a, and 10 from 2013 to 2023. Predictions revealed that all stations are above threshold except stations 7, 9 and 10 in 2030, while stations 7 and 9 in 2050. Recommended mitigation includes leveraging Nature-based Solutions with engineering measures, addressing root causes, and better policy.

Demander à l'IA

Bookmark