Applications of Algorithms in Risk Assessment and Evaluation

Topic Information

Dear Colleagues,

Recent years have witnessed remarkable progress in algorithm research, significantly impacting various industries. These advancements are particularly notable in multi-objective and multi-parameter decision-making, which are critical in decision and management processes. Algorithms have become essential tools in processing large datasets, optimizing complex systems, and providing insights that facilitate strategic decisions. Their widespread application is transforming fields such as geological hazard assessment, land and Earth sciences, water resource management, education, economic management, administrative management, and tourism management, among others.

One key area of application is risk assessment and evaluation. In geological hazard assessment, algorithms predict seismic activities and assess geological stability. For land and Earth sciences, they analyze soil erosion, land-use changes, and environmental degradation. In water resource management, algorithms facilitate the balancing of water supply and demand, ensuring sustainable urban water environment resource allocation. In economic, administrative, and tourism management, these tools evaluate financial risks, optimize resource allocation, and manage visitor flows to enhance both strategic planning and operational efficiency. We welcome submissions on the Applications of Algorithms in Risk Assessment and Evaluation across all fields.

Dr. Yiding Bao
Dr. Qiang Wei
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	1.8	4.5	2008	18.9 Days	CHF 1600	Submit
Data data	2.2	5.0	2016	26.8 Days	CHF 1600	Submit
Earth earth	2.1	5.9	2020	23.7 Days	CHF 1200	Submit
Geosciences geosciences	2.4	5.1	2011	23.5 Days	CHF 1800	Submit
Mathematics mathematics	2.3	4.6	2013	18.3 Days	CHF 2600	Submit
Land land	3.2	5.9	2012	16.9 Days	CHF 2600	Submit
Water water	3.0	6.0	2009	17.5 Days	CHF 2600	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	7.2	2012	35.8 Days	CHF 1900	Submit

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Published Papers (3 papers)

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All Algorithms Data Earth Geosciences Mathematics Land Water IJGI

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20 pages, 5125 KiB  

Open AccessArticle

Quantifying Land Subsidence Probability and Intensity Using Weighted Bayesian Modeling in Shanghai, China

by Chengming Jin, Qing Zhan, Yujin Shi, Chengcheng Wan, Huan Zhang, Luna Zhao, Jianli Liu, Tongfei Tian, Zilong Liu and Jiahong Wen

Land 2025, 14(3), 470; https://doi.org/10.3390/land14030470 - 24 Feb 2025

Viewed by 632

Abstract

Land subsidence, a slow-onset geohazard, poses a severe threat to cities worldwide. However, the lack of quantification in terms of intensity, probability, and hazard zoning complicates the assessment and understanding of the land subsidence risk. In this study, we employ a weighted Bayesian [...] Read more.

Land subsidence, a slow-onset geohazard, poses a severe threat to cities worldwide. However, the lack of quantification in terms of intensity, probability, and hazard zoning complicates the assessment and understanding of the land subsidence risk. In this study, we employ a weighted Bayesian model to explicitly present the spatial distribution of land subsidence probability and map hazard zoning in Shanghai. Two scenarios based on distinct aquifers are analyzed. Our findings reveal the following: (1) The cumulative land subsidence probability density functions in Shanghai follow a skewed distribution, primarily ranging between 0 and 50 mm, with a peak probability at 25 mm for the period 2017–2021. The proportions of cumulative subsidence above 100 mm and between 50 and 100 mm are significantly lower for 2017–2021 compared to those for 2012–2016, indicating a continuous slowdown in land subsidence in Shanghai. (2) Using the cumulative subsidence from 2017–2021 as a measure of posterior probability, the probability distribution of land subsidence under the first scenario ranges from 0.02 to 0.97. The very high probability areas are mainly located in the eastern peripheral regions of Shanghai and the peripheral areas of Chongming District. Under the second scenario, the probability ranges from 0.04 to 0.98, with high probability areas concentrated in the eastern coastal area of Pudong District and regions with intensive construction activity. (3) The Fit statistics for Scenario I and Scenario II are 67% and 70%, respectively, indicating a better fit for Scenario II. (4) High-, medium-, low-, and very low-hazard zones in Shanghai account for 14.2%, 48.7%, 23.6%, and 13.5% of the city, respectively. This work develops a method based on the weighted Bayesian model for assessing and zoning land subsidence hazards, providing a basis for land subsidence risk assessment in Shanghai. Full article

(This article belongs to the Topic Applications of Algorithms in Risk Assessment and Evaluation)

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26 pages, 3304 KiB  

Open AccessArticle

A Methodology Based on Random Forest to Estimate Precipitation Return Periods: A Comparative Analysis with Probability Density Functions in Arequipa, Peru

by Johan Anco-Valdivia, Sebastián Valencia-Félix, Alain Jorge Espinoza Vigil, Guido Anco, Julian Booker, Julio Juarez-Quispe and Erick Rojas-Chura

Water 2025, 17(1), 128; https://doi.org/10.3390/w17010128 - 6 Jan 2025

Viewed by 1861

Abstract

Precipitation within specific return periods plays a crucial role in the design of hydraulic infrastructure for water management. Traditional analytical approaches involve collecting annual maximum precipitation data from a station followed by the application of statistical probability distributions and the selection of the [...] Read more.

Precipitation within specific return periods plays a crucial role in the design of hydraulic infrastructure for water management. Traditional analytical approaches involve collecting annual maximum precipitation data from a station followed by the application of statistical probability distributions and the selection of the best-fit distribution based on goodness-of-fit tests (e.g., Kolmogorov-Smirnov). However, this methodology relies on current data, raising concerns about its suitability for outdated data. This study aims to compare Probability Density Functions (PDFs) with the Random Forest (RF) machine learning algorithm for estimating precipitation at different return periods. Using data from twenty-six stations located in various parts of the Arequipa department in Peru, the performance of both methods was evaluated using MSE, RMSE, R² and MAE. The results show that RF outperforms PDFs in most cases, having more precision using the metrics mentioned for precipitation estimates at return periods of 2, 5, 10, 20, 50, and 100 years for the studied stations. Full article

(This article belongs to the Topic Applications of Algorithms in Risk Assessment and Evaluation)

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23 pages, 11503 KiB  

Open AccessArticle

A Multi-Objective Optimization Framework for Coupled Grey–Green Infrastructure of Areas with Contamination-Induced Water Shortages Under Future Multi-Dimensional Scenarios

by Zixiang Xu, Jiaqing Cheng, Haishun Xu and Jining Li

Land 2024, 13(11), 1932; https://doi.org/10.3390/land13111932 - 16 Nov 2024

Viewed by 1209

Abstract

Stormwater resource utilization is an important function of coupled grey–green infrastructure (CGGI) that has received little research focus, especially in multi-objective optimization studies. Given the complex water problems in areas with contamination-induced water shortages, it is important to incorporate more objectives into optimization [...] Read more.

Stormwater resource utilization is an important function of coupled grey–green infrastructure (CGGI) that has received little research focus, especially in multi-objective optimization studies. Given the complex water problems in areas with contamination-induced water shortages, it is important to incorporate more objectives into optimization systems. Therefore, this study integrated economic performance, hydrological recovery, water quality protection, and stormwater resource utilization into an optimization framework based on the non-dominant sorting genetic algorithm III (NSGA-III). A sponge city pilot area with contamination-induced water shortages in the Yangtze River Delta was considered, optimizing four objectives under different future multi-dimensional scenarios. The results showed a time series and scenarios composed of shared socioeconomic pathways and representative concentration pathways (SSP-RCP scenarios) which, together, affected future climate change and the benefits of a CGGI. In the near and middle periods, the SSP126 scenario had the greatest influence on stormwater management, whereas, in the far period, the SSP585 scenario had the greatest influence. The far period had the greatest influence under three SSP-RCP scenarios. Under the combined influence of SSP-RCP scenarios and a time series, the SSP585-F scenario had the greatest impact. Specific costs could be used to achieve different and no stormwater-resource utilization effects through different configurations of the CGGI. This provided various construction ideas regarding CGGIs for areas with contamination-induced water shortages. Full article

(This article belongs to the Topic Applications of Algorithms in Risk Assessment and Evaluation)

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