Artificial Intelligence Researchers Publish New Report on Land-Use Change and Drought Risk

A recent study published in the Journal of Hydrology and Hydromechanics has revealed that land-use change is a significant driver of environmental degradation and increasing drought risk. The research, conducted by investigators from Slovak University of Technology, aimed to assess drought dynamics in the South Malang Plateau, East Java, by integrating remote sensing data with the Random Forest (RF) algorithm. The study used a multistep approach, including land-use scenario modeling, to predict drought risk and potential environmental impacts.

Key Takeaways:

Three land-use scenarios were developed: Business-as-Usual (BAU) for 2030, participatory mapping (PM), and land capability classification (LCC).
Using the RF model, the analysis achieved an overall accuracy of 92.57% for drought classification.
The study found that between 2017 and 2023, multistrata agroforestry declined by nearly 50%, natural forest cover decreased by 27.6%, and settlements more than doubled.
Under the 2030 BAU scenario, forest cover is projected to decline further to 9,195.16 ha.
Drought analysis showed a peak in "Severe Drought" at 18.1% in 2019, dropping to 3.1% by 2030, while "Extreme Drought" steadily rises from 6.2% to 7.0%, particularly in deforested areas.
The integrated LCCPM approach demonstrated higher potential to reduce drought vulnerability and land degradation.
The integrated land capability classification-participatory mapping (LCCPM scenario) is recommended to strengthen landscape resilience and promote sustainable land management.

Statistics:

92.57% overall accuracy for drought classification using the RF model.
50% decline in multistrata agroforestry between 2017 and 2023.
27.6% decrease in natural forest cover between 2017 and 2023.
18.1% peak in "Severe Drought" in 2019.
3.1% drought risk by 2030 under the BAU scenario.
6.2% to 7.0% increase in "Extreme Drought" particularly in deforested areas by 2030.
9,195.16 ha projected decline in forest cover by 2030 under the BAU scenario.

Sources:

Journal of Hydrology and Hydromechanics, 2025,73(3):260-272
Utilising land use scenario modeling and machine learning for mitigating drought risks in degraded landscapes
Journal of Engineering, October 13, 2025, p 4076