Landslide susceptibility mapping using hybridized block modular intelligence model

Abstract

Landslide susceptibility map (LSM) provide useful tool for decision makers in hazard mitigation concerns. In the present paper, a novel hybrid block-based neural network model (HBNN) for the purpose of producing high-resolution LSM was developed. This hybrid approach was found through the mixture of expert modular structures and divide-and-conquer strategy incorporated with genetic algorithm (GA). The introduced HBNN then was applied on southern part of Guilan province (north of Iran) using 14 causative factors covering topographic and geomorphologic features, and geological and tectonical factors as well as hydrology, land data, and climate conditions. The landslide inventory map was provided using a synergy work from monitored events, interpretation of aerial photographs, and carried out geotechnical investigations in the area as well as field surveys. To insight, the predictability of proposed HBNN was compared with two developed models using multilayer perceptrons (MLPs) and generalized feed forward neural network (GFFN). The generated LSM was validated using receiver operating characteristic (ROC) curves, statistical error indices, and sensitivity and weight analyses as well as monitored landslides. Based on the compared metrics, HBNN with 86.52% and 90.15% in prediction and success rate as well as 89.36% for precision-recall curve demonstrated more consistent tool for future landslide susceptibility zonation. This implies on capability of developed HBNN in producing higher resolution and more reliable LSM for urban and land-use planners.

Determining the Geotechnical Slope Failure Factors via Ensemble and Individual Machine Learning Techniques: A Case Study in Mandi, India

“…However, these authors did not propose the investigation of factors important for landslides and relied on a small class of ML techniques. Shahri and Maghsoudi, (2021) proposed a hybrid block-based neural network model (HBNN) for producing landslide susceptibility mapping. The HBNN was compared against an MLP generalized feed-forward neural network (GFFN).…”

Multi-risk assessment in a historical city

“…Only in 2019, 820 events were registered in the NatCatSERVICE database (Munich 2020). Of these, 7% were earthquakes, and 45% floods, flash floods and landslides (Munich 2020;Shahri and Moud 2021). Every year disaster cost the global economy US$ 520 billion (UNISDR 2018).…”

Subsurface Topographic Modeling Using Geospatial and Data Driven Algorithm

“…In recent years, predictive geospatial remotely sensed-based models in combination with field surveyed data through the GIS platform have been used to interpret different variant of interested geo-objects [5][6][7][8][9]. Despite to drawbacks of GIS in parametric modeling tools [10,11], its incorporation with innovative intelligence approaches have shown significant degree of success in a series of emerging environmental phenomena on the ground surface [12][13][14][15][16]. However, developing such models through geospatial resources for subsurface investigations due to limited data requires exploratory interpolation tools and the complexity of the prospected geo-objects is a difficult and cumbersome task [17,18].…”