Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals

Riverside landslides present a significant geohazard globally, posing threats to infrastructure and human lives. In line with the United Nations’ Sustainable Development Goals (SDGs), which
aim to address global challenges, professionals in the field have developed diverse methodologies
to analyze, assess, and predict the occurrence of landslides, including quantitative, qualitative, and
semi-quantitative approaches. With the advent of computer programs, quantitative techniques have
gained prominence, with computational intelligence and knowledge-based methods like artificial
neural networks (ANNs) achieving remarkable success in landslide susceptibility assessments. This
article offers a comprehensive review of the literature concerning the utilization of ANNs for landslide susceptibility assessment, focusing specifically on riverside areas, in alignment with the SDGs.
Through a systematic search and analysis of various references, it has become evident that ANNs
have emerged as the preferred method for these assessments, surpassing traditional approaches. The
application of ANNs aligns with the SDGs, particularly Goal 11: Sustainable Cities and Communities,
which emphasizes the importance of inclusive, safe, resilient, and sustainable urban environments. By
effectively assessing riverside landslide susceptibility using ANNs, communities can better manage
risks and enhance the resilience of cities and communities to geohazards. While the number of
ANN-based studies in landslide susceptibility modeling has grown in recent years, the overarching
objective remains consistent: researchers strive to develop more accurate and detailed procedures. By
leveraging the power of ANNs and incorporating relevant SDGs, this survey focuses on the most
commonly employed neural network methods for riverside landslide susceptibility mapping, contributing to the overall SDG agenda of promoting sustainable development, resilience, and disaster
risk reduction. Through the integration of ANNs in riverside landslide susceptibility assessments, in
line with the SDGs, this review aims to advance our knowledge and understanding of this field. By
providing insights into the effectiveness of ANNs and their alignment with the SDGs, this research
contributes to the development of improved risk management strategies, sustainable urban planning,
and resilient communities in the face of riverside landslides.