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

Water, Journal Year: 2023, Volume and Issue: 15(15), P. 2707 - 2707

Published: July 27, 2023

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

Language: Английский

---

Landslide susceptibility zonation using the analytical hierarchy process (AHP) in the Great Xi’an Region, China

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 5, 2024

Abstract This study aims to delineate landslide susceptibility maps using the Analytical Hierarchy Process (AHP) method for Great Xi’an Region, China, which is a key planning project urban construction in Shaanxi Province, China from 2021 2035. Multiple data as elevation, slope, aspect, curvature, river density, soil, lithology, and land use have been considered delineating maps. Spatially thematic layers distributed of all aforementioned parameters were created GIS environment. Determine relative importance these occurrence landslides area concerning historical assign appropriate weights. Landslide sensitivity generated by weighted combination environment after being analyzed AHP method. The categorized “very high (11.06%), (19.41%), moderate (23.03%), low (28.70%), very (17.80%)”. Overlay analysis test with LSM showed that zones able contain 82.58% historic landslides. results help determine landslide-prone areas provide reference subsequent construction. In addition, contributes similar loess sites.

Language: Английский

---

Application of artificial intelligence in three aspects of landslide risk assessment: A comprehensive review

Rock Mechanics Bulletin, Journal Year: 2024, Volume and Issue: 3(4), P. 100144 - 100144

Published: July 6, 2024

Landslides are one of the geological disasters with wide distribution, high impact and serious damage around world. Landslide risk assessment can help us know landslides occurring, which is an effective way to prevent landslide in advance. In recent decades, artificial intelligence (AI) has developed rapidly been used a range applications, especially for natural hazards. Based on published literatures, this paper presents detailed review AI applications assessment. Three key areas where application prominent identified, including detection, susceptibility assessment, prediction displacement. Machine learning (ML) containing deep (DL) emerged as primary technology considered successfully due its ability quantify complex nonlinear relationships soil structures predisposing factors. Among algorithms, convolutional neural networks (CNNs) recurrent (RNNs) two models that most widely satisfactory results The generalization ability, sampling training strategies, hyper-parameters optimization these crucial should be carefully considered. challenges opportunities also fully discussed provide suggestions future research

Language: Английский

---

Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance

Sustainability, Journal Year: 2023, Volume and Issue: 15(11), P. 9024 - 9024

Published: June 2, 2023

Landslides pose a serious threat to human lives and property. Accurate landslide susceptibility mapping (LSM) is crucial for sustainable development. Machine learning has recently become an important means of LSM. However, the accuracy machine models limited by heterogeneity environmental factors imbalance samples, especially large-scale To address these problems, we created improved random forest (RF)-based LSM model applied it Guangdong Province, China. First, RF-based was constructed using rainfall-induced samples 13 exploring optimal positive-to-negative training-to-test sample ratios. Second, performance evaluated compared with three other models. The results indicate that: (1) proposed best highest area under curve (AUC) 0.9145, based on ratios 1:1 8:2, respectively; (2) introduction rainfall global modification (GHM) can increase AUC from 0.8808 0.9145; (3) topography are two dominant in landslides. These findings facilitate risk prevention serve as technical reference accurate

Language: Английский

---

Exploration of Coupling Effects in the Digital Economy and Eco-Economic System Resilience in Urban Areas: Case Study of the Beijing-Tianjin-Hebei Urban Agglomeration

Sustainability, Journal Year: 2023, Volume and Issue: 15(9), P. 7258 - 7258

Published: April 27, 2023

Exploring the interaction and coupling effects within digital economy eco-economic system resilience in urban agglomeration areas is conducive to promoting high-quality sustainable development. Based on effect perspective, we construct a coordination development with multiple elements, information, flow. The JJJ from 2010 2019 was used as study sample. spatiotemporal differences spatial of coupled were evaluated by combining tools combined weight model, nuclear density estimation, exploratory data analysis. main results can be summarized follows. (1) From 2019, economic index maintained an upward trend, time series characteristics two sides showed significant positive correlation. Additionally, overall better than system. (2) In terms type coordination, region has experienced dynamic evolution process imbalance primary 2019. coordinated levels Beijing Tianjin are obviously those Hebei Province whole. (3) shows certain distribution. pattern presents core, gap between north south gradually narrowing. (4) Spatial spillovers diffusion evident. However, influential factors have this neighboring regions. may provide theoretical support for continuous improvement ecological environment quality green efficiency agglomeration. It provides decision-making reference regional synergistic strategy optimizing integration.

Language: Английский

---

Landslide Susceptibility Evaluation Based on a Coupled Informative–Logistic Regression Model—Shuangbai County as an Example

Sustainability, Journal Year: 2023, Volume and Issue: 15(16), P. 12449 - 12449

Published: Aug. 16, 2023

Shuangbai County, located in Yunnan Province, Southwest China, possesses a complex and diverse geological environment experiences frequent landslide disasters. As significant area for disaster prevention control, it is crucial to assess the susceptibility of landslides effective prevention, urban planning, development. This research focuses on eleven influencing factors, including elevation, slope, slope direction, rainfall, NDVI, distance from faults, selected as evaluation indexes. The assessment model constructed using information quantity method logistic regression coupling analyze County. entire region’s classified into four categories: not likely occur, low susceptibility, medium high susceptibility. accuracy reasonableness models are tested compared. results indicate that coupled information–logistic (80.0% accuracy) outperforms single (74.2% accuracy). Moreover, density points high-susceptibility higher, making more reasonable. Thus, this can serve valuable tool evaluating regional County basis mitigation planning by relevant authorities.

Language: Английский

---

Risk Assessment and Prevention Planning for Collapse Geological Hazards Considering Extreme Rainfall—A Case Study of Laoshan District in Eastern China

Land, Journal Year: 2023, Volume and Issue: 12(8), P. 1558 - 1558

Published: Aug. 6, 2023

Geological disasters refer to adverse geological phenomena that occur under the influence of natural or human factors and cause damage life property. Establishing prevention control zones based on disaster risk assessment results in land planning management is crucial for ensuring safe regional development. In recent years, there has been an increase extreme rainfall events, so it necessary conduct effective hazard assessments different conditions. Based first national survey results, this paper uses analytic hierarchy process (AHP) combined with information method (IM) construct four conditions, namely, 10-year, 20-year, 50-year, 100-year return periods. The susceptibility, hazard, vulnerability, Laoshan District eastern China are evaluated, established evaluation results. show that: (1) There 121 collapse District, generally at a low susceptibility level. (2) A positive correlation exists between hazards/risks. With condition changing from 10-year period period, proportion high-hazard increased 20% 41%, high-risk 31% 51%, respectively. Receiver operating characteristic (ROC) proved accuracy was acceptable. (3) Key, sub-key, general have established, corresponding suggestions proposed, providing reference early warning other regions.

Language: Английский

---

GIS-based landslide susceptibility mapping using AHP, FMEA, and Pareto systematic analysis in central Yalova, Türkiye

Engineering Science and Technology an International Journal, Journal Year: 2025, Volume and Issue: 64, P. 102013 - 102013

Published: Feb. 21, 2025

Language: Английский

---

Risk Assessment of Yellow Muddy Water in High-Construction-Intensity Cities Based on the GIS Analytic Hierarchy Process Method: A Case Study of Guangzhou City

Land, Journal Year: 2025, Volume and Issue: 14(4), P. 779 - 779

Published: April 4, 2025

During urbanisation, extensive production and construction activities encroach on ecological spaces, leading to changes in environmental structures soil erosion. The issue of yellow muddy water caused by rainfall cities with high intensity has garnered significant attention. Taking Guangzhou City as the research area, this study is first propose a risk assessment model for intensity, influence sites was fully considered. Rainfall were used indicators assess hazards water. Elevation, slope, normalised difference vegetation index (NDVI), erosion modulus, stream power (SPI), surface permeability, roads represent exposure evaluation indicators. Population number GDP (Gross Domestic Product) vulnerability Based analytic hierarchy process (AHP) method, weights each indicator determined, system established. By overlaying weighted layers different geographic information (GIS) platform, degree distribution map disasters generated. results demonstrated that disaster levels within area exhibited spatial differentiation, areas higher accounting 14.76% total. compared historical event from Guangzhou, effectiveness verified receiver operating characteristic (ROC) curve. validation indicate provides accuracy assessing high-construction-intensity cities, offering effective technical support precise prevention mitigation.

Language: Английский

---

Geological hazards susceptibility evaluaiton using ICM-ANN and ICM-LR ensemble models (a case study of Jiuzhai gully after Mw 7.0 earthquake in 2017)

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(4)

Published: May 14, 2025

Language: Английский

---