Flood prediction: analyzing land use scenarios and strategies in Sumber Brantas and Kali Konto watersheds in East Java, Indonesia

Aditya Nugraha Putra (), Salsabila Fitri Alfaani, Danny Dwi Saputra, Yosi Andhika, Erwin Ismu Wisnubroto, Fandy Tri Admajaya, Febrian Maritimo, Saskia Karyna Paimin, Irma Ardi Kusumawati, Novandi Rizky Prasetya, Michelle Talisia Sugiarto, Istika Nita, Sudarto Sudarto, Sujarwo Sujarwo, Mochtar Lutfi Rayes, Didik Suprayogo, Mohd. Hasmadi Ismail and Meine van Noordwijk
Additional contact information
Aditya Nugraha Putra: Brawijaya University
Salsabila Fitri Alfaani: Brawijaya University
Danny Dwi Saputra: Brawijaya University
Yosi Andhika: Brawijaya University
Erwin Ismu Wisnubroto: Tribhuwana Tunggadewi University
Fandy Tri Admajaya: Geospatial Information Agency
Febrian Maritimo: Geospatial Information Agency
Saskia Karyna Paimin: Brawijaya University
Irma Ardi Kusumawati: Yayasan Bumi Hijau Lestari
Novandi Rizky Prasetya: Brawijaya University
Michelle Talisia Sugiarto: Brawijaya University
Istika Nita: Brawijaya University
Sudarto Sudarto: Brawijaya University
Sujarwo Sujarwo: Brawijaya University
Mochtar Lutfi Rayes: Brawijaya University
Didik Suprayogo: Brawijaya University
Mohd. Hasmadi Ismail: Universiti Putra Malaysia
Meine van Noordwijk: Centre of International Forestry Research and World Agroforestry (CIFOR-ICRAF)

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 12, No 43, 15025-15053

Abstract: Abstract Previous studies have emphasized the significant influence of land use and land cover (LULC) on flood hazard severity. However, the analysis has been restricted to a single dataset and scenario. This study is carried out to analyze the land use options for flood prediction by examining three distinct scenarios namely business as usual (BAU), regional spatial planning (RSP), and land capability (LC). The BAU (2025) scenario was forecasted by using a multitemporal LULC baseline (2017, 2019, 2021 and 2022) and modelled with the ANN Cellular Automata-Markov Chain. The RSP and LC scenarios were developed based on the official regional spatial planning of Malang Regency and Batu City, while LC was developed through the land capability classification limiting factor method. These scenarios were applied to predict flood levels using the InVEST model, incorporating factors such as rainfall depth, hydrologic soil group, curve number, and a biophysical table for infiltration analysis, by using SCS Curve Number analysis in InVEST. The result shows a decline in forest cover (from 31 to 23%) and agroforestry (from 3 to 2%) to correspond with a 16% increase in flood hazard levels. This correlation was identified using pearson model and validated (Kappa accuracy) through ground-check surveys, achieving an overall classification accuracy of 75%. If there are no interventions, high and very high flood hazard levels could escalate to 12% and 4% in 2025. In contrast, the RSP and LC scenarios show promise in reducing flood hazards by 16% and 10%, respectively. Remarkably, the LC scenario has shown to be the most effective strategy for the land use approach, showcasing a potential to prevent flood hazards because it maintains the existence of forests according to their land capabilities.

Keywords: Land use; Spatial planning; Disaster; Flood prediction; Remote sensing; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07363-4

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