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

Abstract

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

1. Introduction

Geological disasters refer to adverse geological processes or phenomena caused by natural or human factors. Geological disasters can be divided into more than 30 types. Natural geological disasters are induced by rainfall, snowmelt, and earthquakes, while artificial geological disasters are caused by engineering excavation, loading, and blasting. Common geological disasters mainly include six types of disasters related to geological processes, including collapse, landslide, mud-rock flow, subsidence, ground fissures, and land subsidence (Figure 1). Geological disasters have the characteristics of being sudden, uncontrollable, and highly destructive, causing harm to people’s lives and property safety [1]. Especially in recent years, extreme weather has increased, and excessive rainfall is more likely to trigger geological disasters [2,3]. In 2022, there were 5659 geological disasters in China, mainly landslides and collapses (Figure 2 and Figure 3). Conducting geological hazard risk assessment, analyzing the occurrence patterns of regional geological hazards, mastering the risk and hidden dangers of critical geological hazards, and predicting and identifying the occurrence of geological hazards are necessary means of effectively resolving disaster risks [4].

The research on geological hazards and risk assessment is in the development stage. It can be divided into three categories: qualitative evaluation, quantitative evaluation, and a combination of qualitative and quantitative reviews. Qualitative evaluation methods include the Analytic Hierarchy Process (AHP) [5,6] and the Comprehensive Indicator Method (CIM) [7,8]. The AHP is a multi-objective decision analysis method that quantifies the empirical judgments of decision-makers. It is widely used for analysis and decision-making when the target structure is complex and needs more necessary data. It can obtain a satisfactory decision structure, especially for complex problems that are difficult to quantify fully. The CIM refers to comprehensively evaluating benefits by weighting the average number of individual benefit indicators and calculating the comprehensive value based on a reasonable set of benefit indicator systems. Commonly, these methods heavily rely on the expertise of individuals, which is subjective and susceptible to human influence. On the other hand, quantitative evaluation methods are grounded in data and offer a more objective approach to inferring the likelihood of geological disasters. Some prominent examples include logistic regression analysis [9,10], neural network methods [11,12], random forest methods [13,14], and the information value model method [15,16]. Logistic regression analysis, while unaffected by subjective factors, can introduce uncertainty in areas with dense vegetation. The neural network method exhibits substantial capabilities in addressing complex issues involving incomplete or insufficient data but suffers from limitations in sample selection and iterative processes. The random forest method demonstrates high predictive ability but necessitates extensive geological disaster data for reliable results, making it less suitable for regions with fewer incidents. The information value model method calculates the contribution of each factor through the information value of known geological disaster points and factors, establishing a prediction model. Compared to other methods, it requires less data. Still, it can only reflect the likelihood of geological disasters occurring under specific combinations of influencing factors and does not account for variations in the impact levels of each factor [17]. Hence, a combination of quantitative and qualitative evaluation proves more beneficial in enhancing the accuracy of geological disaster assessment results.

With the rapid development of GIS and machine learning technology (ML), the means and methods for risk assessment of geological disasters are also maturing. Based on remote sensing and GIS, Tan et al. [18] introduced a hierarchical entropy variable weight method using an entropy algorithm to reduce personal impact and obtain more accurate evaluation results. Lyu and Yin [19] merged AHP and the analytical network process (ANP) into a geographic information system and integrated interval numbers into the fuzzy hierarchical analysis process (FAHP) to evaluate the risks of various disasters in Hong Kong, improving the accuracy of the multi-risk assessment. Yang et al. [20] proposed an improved coupling landslide susceptibility evaluation model by combining the theory of unascertained measures (UM), dynamic, comprehensive weighting (DCW) based on the AHP entropy weight method and set pair analysis (SPA) theory. Chen and Zhang [21] conducted a comparative study on GIS-based Bayesian networks (BN), Hoeffding trees (HT), and logistic model trees for landslide susceptibility modeling, demonstrating that the HT model is a good classifier for landslide susceptibility modeling. Rong et al. [22], based on the integrated machine learning model (MLM) and scenario simulation technology, calculate the precipitation in different extreme precipitation return periods and evaluate the landslide risk with the susceptibility results. It is found that the optimized Random Forest model has the best all-around performance in sensitivity evaluation.

In 2020, the State Council of China launched the first comprehensive survey of natural disaster risks (regarding geological disasters), which shifted its work philosophy from focusing on post-disaster relief to pre-disaster prevention and from reducing disaster losses to reducing disaster risks. After two years of effort, by the end of 2022, 2041 counties completed geological hazard risk surveys, and 1522 counties completed 1:50,000 geological hazard risk surveys. The first survey task has been fully completed as scheduled. The system has systematically conducted the comprehensive remote sensing identification of geological hazards in 713 counties (cities and districts) with an area of 4.07 million Square kilometers that is prone to geological disasters in China, completed the fine survey of 2161 cities and towns, 6615 important hazard surveys, 6250 engineering treatments, 2676 hazard elimination and removal, and relocated 125,000 people from 34,000 households threatened by geological disasters. In addition, more than 20,000 universal professional monitoring projects (rainfall monitoring, slope displacement and crack monitoring, groundwater level monitoring, video monitoring, etc.) were completed and operated before the flood season, and the inspection and monitoring system for more than 264,000 geological disaster group measurement and prevention personnel was improved. More than 200 ministerial-level experts are stationed in 30 provinces nationwide to strengthen risk prevention technical support. The integration of “civil air defense” and “technical prevention” was further enhanced, and geological disaster investigation and evaluation, monitoring and early warning, comprehensive management, emergency response, and grassroots disaster prevention capabilities were further improved. This article is a follow-up study based on the results of this work.

Laoshan District is the most economically developed area in Qingdao, Shandong Province, China. How to effectively avoid the harm caused by geological disasters in urban planning is an urgent problem for government departments to solve. At the same time, there are many mountains in Laoshan District that are prone to collapse disasters under the conditions of rainstorms. This article introduces extreme rainfall factors based on conventional geological hazard risk assessment in response to this issue. Four extreme rainfall conditions have been set: 10-year, 20-year, 50-year, and 100-year return periods. Through the combination of AHP and IM methods, a complete geological hazard assessment, including susceptibility, hazard, vulnerability, and risk, was carried out in Laoshan District. Based on the evaluation results, prevention and control zones were divided, and corresponding prevention measures were proposed. The research results can provide authoritative disaster risk information and a scientific basis for decision-making for effective local natural disaster prevention and control work, proper regional land planning, and sustainable economic and social development. Finally, an automatic geological hazard monitoring and warning system is introduced, which can provide a reference for geological hazard prevention in similar rainstorm areas.

2. Areas and Methods

2.1. Study Area

Laoshan District is located in Qingdao, Shandong Province. Its geographical coordinates are 120°24′33″~120°43′ E and 36° 03′~36°23′ N, with a north-south length of 25 km, an east-west width of 17 km, and a total area of 395.79 km² (Figure 4). Laoshan District belongs to the northern temperate continental monsoon climate zone, with humid air, abundant rainfall, and moderate humidity. As it is close to the Yellow Sea, it is regulated by the sea. Also, it shows the characteristics of an Oceanic climate, such as no severe cold in winter, no intense heat in summer, a slight temperature difference between day and night, a long frost-free period, and high humidity. The annual average temperature is 12.1 °C. The area has abundant rainfall, with an average annual rainfall of 849.9 mm and a maximum annual rainfall of 1426.1 mm (Data sourced from Shandong Provincial Meteorological Bureau, China).

Tectonically, Laoshan District is located at the northern end of Jiaonan Uplift, a medium-low hilly area with steep mountains, ravines, and complex terrain. Centered around the Laoshan Mountains, it is high in the middle and low on both sides. The fault structure in the area is developed, the crust rises significantly, Erosion is intense, and the slope is generally greater than 30°. The lithology is mainly magmatic rock with extreme Weathering. The study area is also affected by human engineering and economic activities. Many activities, such as quarrying, road construction, and engineering construction, have promoted the formation of geological disasters.

2.2. Current Situation of Geological Disasters

There are 121 potential geological hazards in Laoshan District, all of which are collapses on a small scale (affected area < 1 × 10⁴ m³, the standard is sourced from China’s “Technical Requirements for Geological Disaster Risk Investigation and Evaluation“), including 16 geological disaster points, resulting in direct financial losses of CNY 10.55 million. There are 108 potential geological disaster points, all of which are small-scale, with a predicted financial loss of CNY 49.16 million and a threatened population of 472 people (data sourced from the first comprehensive survey of natural disaster risks in China). Based on the location of geological hazard points and economic development planning, key areas with a concentrated number of geological hazards are selected for evaluation, covering an area of 232.91 km². The relationship between the key areas and the overall area is shown in Figure 5.

2.2.1. Collapse Type

Analyze the development characteristics of geological hazards based on the failure mode, structural type, stability of the dangerous rock mass, and risk assessment level. According to the state of action, it can be divided into three categories: tilting, sliding, and pulling apart. According to the slope structure type, the collapsed slope in the area can be divided into three types: Dip slope, transverse slope, and oblique slope. According to the current stability status, it can be divided into two categories: stable and unstable. According to the risk level, it can be divided into three categories: low risk, medium risk, and high risk (

Table 1. Collapse type.

Category	Quantity	Percentage
Failure mode
Tilting type	81	66.94
Sliding-type	37	30.57
Pull-apart type	3	2.49
Slope structure type
Dip slope	101	83.47
Oblique slope	19	15.71
Transverse slope	1	0.82
Stability of dangerous rock mass
Stable	30	24.79
Instability	91	75.21
	Risk
Low risk	2	1.65
Medium risk	10	8.26
High risk	109	90.09

).

Fissures mainly cut through the collapse in Laoshan District to form missing bodies. The deformation and destruction of cutting bodies in the early stage are especially caused by fissure expansion and base erosion. The cracks in the collapse area are mainly unloading shots and joint planes perpendicular to the ground. The images are multi-opening, with some extending to form a connected structure. When a nearly vertical tensile crack is created, a cutting body is made if the damage is not connected and the dangerous rock body is not separated from the parent body. Under the influence of self-weight stress and other external forces, the cracks expand, and after the trailing edge penetrates, an independent body is formed (Figure 6).

Transverse joints are developed on the surface of the slope. The slope surface has been subjected to weathering for a long time, blocks are produced under the self-weightless, and the cutting body and separation body base are eroded, forming a free face at the lower part. The rock mass overhangs or reclines on the cliff, and the cutting and separation bodies are transformed into the dangerous rock mass. In addition, the natural collapse lithology in the area is primarily Metamorphic rock and marble. Due to “differential weathering”, the base is cut out, forming a concave rock cavity and transforming into a dangerous rock mass. After the cutting and separating bodies are transformed into perilous rock bodies, they enter the later stage of deformation and failure, i.e., the deformation and failure of dangerous rock bodies. When the foundation support point of a hazardous rock body reaches its limit, rock collapse will occur under any external force, such as earthquakes or heavy rainfall. Moreover, most of the leading edge of the dangerous rock body is airborne, which provides favorable conditions for the problematic rock body to fall [23].

2.2.2. Time Distribution Pattern

Based on the statistics of historical rainfall monitoring data from many meteorological stations in Laoshan District, the authors found that during the period from 2006 to 2022, rainfall was concentrated in the flood season (June to August), accounting for 58% of the annual rainfall (Figure 7). The temporal pattern of collapsed geological disasters in the Laoshan District is roughly the same as rainfall. A hefty rainstorm is one of the main factors causing geological disasters in the region [24]. Especially in July 2020, there were 16 geological disasters caused by collapses, resulting in direct financial losses of 8 million yuan and putting 76 people at risk.

2.3. Evaluation Method

This assessment adopts the analytic hierarchy process (AHP) and information quantity method (IM) [25]. The AHP refers to the systematic approach of decomposing a complex multi-objective decision-making problem into multiple objectives or criteria as a system. It then decomposes them into various levels of numerous indicators. Using qualitative indicator fuzzy quantification methods, the hierarchical single ranking and total ranking are calculated, which serve as the objective and multi-scheme optimization decision-making systems. The IM converts the measured values reflecting factors affecting regional stability into information quantity values as quantitative indicators for risk zoning. The quantity and quality of information obtained in the process of geological disaster prediction are related to the actual occurrence of geological disasters, which can be used to evaluate and predict geological disasters. At the same time, GIS has also been applied to this evaluation, and the evaluation process is shown in Figure 8.

The impact and control of geological hazard susceptibility are caused by the superposition of various factors, which can be summarized as essential and induced factors, with varying degrees of contribution from each factor. The risk assessment of geological disasters in the Laoshan District is divided into three layers. The target layer is the zonal susceptibility assessment, and the criterion layer is divided into primary and induced factors. The scheme layer selects slope aspect, slope, elevation, rock and soil mass, distance to fault, river, road, and rainfall as the assessment factors and the hierarchical structure model. Quantify based on the 1–9 scale [26] and construct a judgment matrix for each level (

Table 2. A-B matrix and weights of each factor in layer B.

A	B1	B2	Wi
B1	1	2	0.6667
B2	1/2	1	0.3333

,

Table 3. B1-C matrix and weights of various factors in layer C.

B1	C1	C2	C3	C4	C5	C6	Wi
C1	1	1/2	1/9	1/8	1/2	1/2	0.0335
C2	2	1	1/7	1/7	1/2	1/2	0.0456
C3	9	7	1	3	7	9	0.4788
C4	8	7	1/3	1	7	7	0.3101
C5	2	2	1/7	1/7	1	3	0.0795
C6	2	2	1/9	1/7	1/3	1	0.0524

and

Table 4. B2-C matrix and weights of various factors in layer C.

B2	C7	C8	Wi
C7	1	1/9	0.1
C8	9	1	0.9

). Based on expert opinions, score each factor and ultimately calculate the weight values of each factor (

Table 5. Combination weight values of various factors in layer C.

	A-B	B-C	Weight
C1	0.6667	0.0335	0.02233445
C2	0.6667	0.0456	0.03040152
C3	0.6667	0.4788	0.31921596
C4	0.6667	0.3101	0.20674367
C5	0.6667	0.0795	0.05300265
C6	0.6667	0.0524	0.03493508
C7	0.3333	0.1	0.03333
C8	0.3333	0.9	0.29997

). Calculate the information value of each factor based on the ratio of the number of geological hazard distribution units within each evaluation factor unit to the total distribution of geological hazards in the study area [27]. To eliminate the differences in the impact of different factors on geological disasters, the weight values obtained by AHP are used to assign the information content of various evaluation factors, and the formula is as follows:

$$\mathrm{I}={\displaystyle \sum _{i=1}^n{\mathrm{W}}_{\mathrm{i}}}{\mathrm{I}}_{\mathrm{i}}={\displaystyle \sum _{i=1}^n{\mathrm{W}}_{\mathrm{i}}}\ln \frac{{\mathrm{N}}_{\mathrm{i}}/N}{{\mathrm{S}}_{\mathrm{i}}/S}$$

(1)

In the formula, W_i is the weight value of the evaluation factor i calculated based on the AHP. It is the amount of information on the occurrence of geological disasters in the evaluation factor i; N_i is the number of units in the distribution of geological hazards for the evaluation factor i; N is the total number of teams with known geological hazard distribution in the study area, and in this study, N = 121; S_i is the unit area of the i evaluation factor; S represents the total area of the study area units, and in this study, S = 232.91.

3. Results

3.1. Susceptibility Assessment

Select seven evaluation factors from C1 to C7 for susceptibility evaluation. The first step is to use the GIS Spatial analysis function to calculate the amount of information for evaluation factors [28]. Select a grid cell size of 30 × 30 m², obtain the grid layer of each factor, and then overlay each index Factor graph with the geological hazard distribution map to obtain the distribution of geological hazards in each factor classification (Figure 9). The second step is to use the weight of each factor determined by the AHP to reclassify the grid layers of each factor using the reclassification function of GIS [29]. The information content map of each factor is regenerated based on the information content value [30]. Finally, the information content calculation is completed using the grid calculation function of GIS, and the calculation results are shown in

Table 6. Weighted information content of evaluation factors.

Classification	Factor	Grade	Ni	Si	Information Value	Layered Weight Value	Weighted Information Value
B1	C1	−1	1	1.28	0.4149	0.02233445	0.009267
		0~22.5°	3	13.40	−0.8328	0.02233445	−0.0186
		22.5~67.5°	12	24.51	−0.0502	0.02233445	−0.00112
		67.5~112.5°	15	29.52	−0.0130	0.02233445	−0.00029
		112.5~157.5°	21	33.12	0.2086	0.02233445	0.004659
		157.5~202.5°	26	28.49	0.4924	0.02233445	0.010997
		202.5~247.5°	16	25.22	0.0000	0.02233445	0
		247.5~292.5°	16	31.16	0.0000	0.02233445	0
		292.5~337.5°	7	29.54	−0.7758	0.02233445	−0.01733
		337.5–360°	4	14.92	−0.6525	0.02233445	−0.01457
	C2	0~50 m	14	19.80	0.3083	0.03040152	0.009373
		50~100 m	25	26.43	0.5994	0.03040152	0.018223
		100~150 m	24	24.78	0.6228	0.03040152	0.018934
		150~200 m	11	20.52	0.0314	0.03040152	0.000955
		200~250 m	11	17.99	0.1631	0.03040152	0.004958
		>250 m	36	123.40	−0.5770	0.03040152	−0.01754
	C3	0~5°	9	20.48	−0.1583	0.31921596	−0.05053
		5~10°	22	23.60	0.5940	0.31921596	0.189614
		10~15°	21	32.81	0.2180	0.31921596	0.069589
		15~20°	22	42.08	0.0157	0.31921596	0.005012
		20~25°	17	44.54	−0.2991	0.31921596	−0.09548
		25~30°	9	35.55	−0.7094	0.31921596	−0.22645
		30~35°	10	20.30	−0.0438	0.31921596	−0.01398
		>35°	11	11.85	0.3892	0.31921596	0.124239
	C4	Semi-hard volcanic rock, debris	3	10.51	−0.5989	0.20674367	−0.12382
		Coastal soft alluvial and marine deposits	0	2.18	0.0000	0.20674367	0
		Hard block intrusive rock subregion	114	209.22	0.0476	0.20674367	0.009841
		Loose alluvial-proluvial layer in mountain valleys	4	11.00	−0.3563	0.20674367	−0.07366
	C5	0~500 m	52	70.65	0.3484	0.05300265	0.018466
		500~1000 m	24	59.73	−0.2569	0.05300265	−0.01362
		1000~1500 m	15	48.28	−0.5140	0.05300265	−0.02724
		1500~2000 m	12	31.58	−0.3126	0.05300265	−0.01657
		2000~2500 m	7	16.46	−0.2003	0.05300265	−0.01062
		2500~3000 m	4	5.49	0.3380	0.05300265	0.017915
		>3000 m	7	0.73	2.9161	0.05300265	0.154561
	C6	0~200 m	45	49.97	0.5500	0.03493508	0.019214
		200~400 m	16	43.93	−0.3552	0.03493508	−0.01241
		400~600 m	17	38.61	−0.1654	0.03493508	−0.00578
		600~800 m	15	32.11	−0.1062	0.03493508	−0.00371
		800~1000 m	10	26.35	−0.3142	0.03493508	−0.01098
		>1000 m	18	41.93	−0.1909	0.03493508	−0.00667
B2	C7	<100 m	65	39.45	1.1543	0.03333	0.038473
		100~200 m	21	27.08	0.4005	0.03333	0.013349
		200~400 m	15	40.87	−0.3475	0.03333	−0.01158
		400~600 m	10	30.71	−0.4672	0.03333	−0.01557
		600~800 m	6	23.81	−0.7236	0.03333	−0.02412
		6: >800 m	4	70.99	−2.2213	0.03333	−0.07404

. The third step is to stack the evaluation factors according to the weighted information amount through GIS to obtain the total information amount of geological hazard susceptibility in Laoshan District (the more significant the information amount value, the easier the geological hazard is), and finally output the evaluation map (Figure 10a).

In combination with the geological environment of Laoshan District, the degree of susceptibility to geological disasters in Laoshan District is divided into three zones according to the classification standard (

Table 7. Recommended indicators for susceptibility assessment and grading (The standard is sourced from China’s “Technical Requirements for Geological Disaster Risk Investigation and Evaluation (1:50,000)”).

Assessment Indicator	High	Medium	Low	Non
Subarea characteristics	The mountain is high and steep, the Tectonic uplift is intense, and human activities strongly impact the natural and geological environment. It is a section where rainstorm frequently occurs, and geological disasters occasionally arise in the early stage.	Mountainous and hilly areas with steep terrain belong to the Tectonic uplift area. Human activities have a substantial impact on the geological environment. There are many rainstorms, and medium and small geological disasters are developed.	The hilly residual gentle slope area has a relatively flat terrain, developed vegetation, intense human activities, and relatively small geological disasters in the early stage.	Plain area, with flat terrain and lack of terrain conditions formed by landslides and collapses, with no geological disasters occurring in the early stage
The area density of geological hazard (%)	≥50	20~50	5~20	<5
Point density of geological disaster (points/100 km2)	≥50	20~50	5~20	<5

): medium-susceptibility zone, low-susceptibility zone, and non-susceptibility zone (Figure 10b). Among them, the area of the medium-susceptibility zone is 49.93 km², accounting for 21% of the total area of the region. The low-susceptibility zone is 94.86 km², accounting for 41% of the region’s total area. The location of the nonsusceptibility zone is 88.12 km², accounting for 38% of the region’s total area. In general, the geological disasters in Laoshan District are at a low level of susceptibility.

3.2. Hazard Assessment

Overlay rainfall data (C8) based on susceptibility and conduct geological hazard assessment through qualitative and quantitative methods. Considering that the main inducing factor of geological disasters in Laoshan District is extreme rainfall, the 10-year return period, 20-year return period, 50-year return period, and 100-year return period rainfall conditions are used as the risk assessment indicators of geological disasters in Laoshan District in this hazard assessment (

Table 8. Rainfall-weighted information quantity.

Classification	Factor	Grade	Ni	Si	Information Value	Layered Weight Value	Weighted Information Value
C1	Once in 10 years	<2070 mm	15	53.72	0.0000	0.29997	0
		2070–2110 mm	37	52.76	0.3000	0.29997	0.089991
		3:2110–2150 mm	45	51.66	0.5169	0.29997	0.155054
		4:2150–2190 mm	17	53.54	−0.4924	0.29997	−0.14771
		5:>2190 mm	7	21.22	−0.4544	0.29997	−0.13631
	Once in 20 years	<1900 mm	4	16.13	−0.7394	0.29997	−0.2218
		1900–2150 mm	29	69.23	−0.2153	0.29997	−0.06458
		2150–2400 mm	71	93.95	0.3747	0.29997	0.112399
		2400–2650 mm	11	38.11	−0.5878	0.29997	−0.17632
		>2650 mm	6	15.48	−0.2932	0.29997	−0.08795
	Once in 50 years	<1900 mm	1	3.07	−0.4679	0.29997	−0.14036
		1900–2200 mm	14	38.88	−0.3666	0.29997	−0.10997
		2200–2500 mm	77	114.62	0.2571	0.29997	0.077122
		2500–2800 mm	22	57.82	−0.3115	0.29997	−0.09344
		>2800 mm	7	18.51	−0.3177	0.29997	−0.0953
	Once in 100 years	<2000 mm	2	3.61	0.0654	0.29997	0.019618
		2100–2350 mm	13	45.31	−0.5938	0.29997	−0.17812
		2350–2700 mm	81	123.05	0.2367	0.29997	0.071003
		2700–3050 mm	21	50.04	−0.2134	0.29997	−0.06401
		>3050 mm	4	10.90	−0.3474	0.29997	−0.10421

and Figure 11). The rainfall index refers to the historical rainfall data for each station in Laoshan District.

The natural discontinuity method of GIS divides Laoshan into three zones: high, medium, and low. The assessment results under each rainfall condition are shown in Figure 12.

According to the hazard assessment results, the area of each division and the number of geological disaster points are counted, respectively. The results show that most geological disasters are distributed in high- and medium-hazard areas. The density of disaster points increases from low-hazard areas to high-hazard areas. The density of disaster points in high-hazard regions is the highest, indicating that the hazard division of Laoshan District is relatively reasonable (Figure 13). Also, there is a positive correlation between rainfall and hazard, with the more significant the rain, the greater the hazard. As the rainfall conditions change from once every 10 years to once every 100 years, the proportion of high-hazard zones has increased from 20% to 41%. According to the Receiver operating characteristics [31,32], the accuracy of the hazard assessment results was tested (Figure 14). The calculated Area Under Curve (AUC) values were 56.3%, 63.8%, 64.2%, and 68.1%, with acceptable assessment accuracy. The above results show that the hazard zoning accuracy rate obtained using the AHP-IM method is high and suitable for hazard assessment in Laoshan District.

3.3. Vulnerability Assessment

The vulnerability assessment of geological disasters is an essential link in risk assessment, which evaluates the disaster-bearing body and human engineering. Vulnerability mainly includes the following parts: ① Building vulnerability is the primary carrier of population distribution and has its own economic value. Normalizing the building area is adopted, and the normalized value is used as the basic vulnerability within the survey area. ② Personnel vulnerability is obtained by investigating the number of geological hazard points and potential threat populations, using GIS and the kernel density algorithm to obtain personnel vulnerability, and then reclassifying. ③ The vulnerability of transportation facilities and other living facilities is assigned based on different types and levels of facilities. ④ Comprehensive vulnerability assessment involves overlaying the vulnerability of different types of disaster-bearing bodies to obtain a comprehensive vulnerability assessment chart. The vulnerability assessment of these three factors is conducted separately, and the weights of each factor are determined to be 0.4, 0.4, and 0.2 through expert scoring. The vulnerability assessment results for Laoshan District are obtained by superposition analysis according to weight (Figure 15). From the vulnerability assessment map of Laoshan District, the high-vulnerability zone of Laoshan District is 19.69 km², accounting for 9% of the total area of the district; the medium-vulnerability zone is 74.95 km², accounting for 32% of the total area of the community; and the low-vulnerability zone is 138.26 km², accounting for 59% of the total area of the district. Laoshan District’s vulnerability is mainly low and medium, and the size of its high vulnerability is slight, primarily concentrated in urban areas and villages.

3.4. Risk Assessment

The United Nations believes that “Risk = Hazard× Vulnerability” and most researchers use this definition to describe the geological hazard risk assessment [33]. Therefore, this study applies this calculation model to the geological hazard risk assessment in Laoshan District. The hazards and vulnerability of geological disasters in Laoshan District are superposed and analyzed by GIS to form the risk zoning of geological disasters in the study area according to the classification standard (

Table 9. Recommended indicators for risk assessment and grading (the standard is sourced from China’s “Technical Requirements for Geological Disaster Risk Investigation and Evaluation (1:50,000)”).

	Hazard	Extremely High	High	Medium	Low
Vulnerability
Extremely high		Extremely high	Extremely high	High	Medium
High		Extremely high	High	Medium	Medium
Medium		High	High	Medium	Low
Low		High	Medium	Low	Low

), divided into high-risk, medium-risk, and low-risk areas (Figure 16). There is also a positive correlation between rainfall and risk (Figure 17); the more significant the rain, the greater the risk. As rainfall conditions change from once every 10 years to once every 100 years, the proportion of high-risk zones has increased from 31% to 51%. The calculated Area Under Curve (AUC) values were 73.9%, 66.9%, 62.4%, and 68.3%, with acceptable assessment accuracy (Figure 18).

4. Prevention

To effectively mitigate and prevent geological disasters, it is crucial to establish a proficient and scientific system for geological disaster prevention and control. Alongside conducting regional assessments of geological disaster risks, it is imperative to establish designated prevention and control zones while also developing precise monitoring and early warning systems [34]. With improved monitoring and early warning technology, the number of successfully predicted geological disasters in China has gradually increased. Among them, in 2022, China successfully predicted 321 geological disasters and avoided direct financial losses of 600 million yuan (Figure 19). Based on the risk assessment results in the third section, the author’s team carried out the geological disaster prevention and control zoning work in Laoshan District.

4.1. Prevention and Control Zoning

The division is based on the number of significant geological disasters and geomorphic units, combined with national economic and social development plans, and considering the relative integrity of administrative units. According to the above zoning principles and methods, the geological disaster prevention and control zone in Laoshan District is divided into three regions according to the development degree, scale, hazard degree, and landform of specific geological disasters, namely, the key prevention zone, the sub-key prevention zone, and the general prevention zone (Figure 20).

4.1.1. The Key Prevention Zone

The key prevention zone is 112.70 km², accounting for 48% of the region’s total area. There are 95 geological hazards. Most of the main geomorphic types in this area are hilly regions with significant ground elevation differences. The bedrock lithology is mainly granite, with developed fault structures, high rainfall, dense personnel, and crisscrossed roads. There are many potential collapse hazards.

When conducting urban economic planning in this region, focusing on the risk of geological disasters is necessary. We have put forth the following policy recommendations: ① Comprehensive management of geological disaster points that seriously threaten the safety of people’s lives and property. ② Select hidden danger points with poor stability and a significant threat to the population for relocation and avoidance work. ③ Establish a spatial database of geological hazards and carry out professional monitoring and geological hazard warning and prediction work. Establish a meteorological warning information system for geological disasters and an emergency response system for sudden geological disasters during the flood season. ④ A geological hazard risk assessment is required during the construction of the megaproject.

4.1.2. The Sub-Key Prevention Zone and the General Prevention Zone

The sub-key prevention zone is 71.17 km², accounting for 31% of the region’s total area and 12 geological hazard points. The general prevention zone covers an area of 49.04 km², accounting for 21% of the region’s total area. There are 14 geological hazards. Most of the main geomorphic types in the two parts are medium-hilly areas with minor ground elevation differences.

The risk of geological disasters in these two regions is relatively low, and attention should be paid to the prevention of geological disasters when conducting regional land management and planning in these two regions. We have put forward the following policy suggestions: ① Establish a sound system for mass measurement and prevention of geological disasters; carry out popular science training for mass measurement and prevention; and improve public awareness of prevention. ② Set up warning signs at potential geological hazards. ③ The natural resources department regularly organizes professional teams to inspect hidden danger points, especially during the flood season, to strengthen duty.

4.2. Monitoring and Early Warning

The author’s research group has established a geological hazard monitoring and warning information system in the key prevention zone (Figure 21). As mentioned earlier, this work is crucial for the prevention and control of geological disasters in key prevention zones. The system is based on the Internet of Things, automatic expansion and load balancing technology, multi-source heterogeneous big data management, and other technologies that can achieve complete automated geological disaster monitoring and early warning throughout the life cycle. The system monitors real-time parameters such as rainfall, stress, crack displacement, ground sound, vibration acceleration, and soil moisture content for potential collapse hazards. When the monitoring values exceed the threshold, warning information can be issued promptly.

By leveraging the monitoring data obtained during the flood seasons of 2021 and 2022, we conducted a thorough analysis to evaluate the efficacy of the monitoring and early warning platform. Our study involved comparing data collected from three distinct types of devices, namely stress gauges and crack monitoring equipment, which demonstrated superior precision and device reliability in monitoring geological hazards (Figure 22). Furthermore, both device types exhibited heightened sensitivity to data fluctuations, facilitating more accurate tracking of changes in geological hazards. We scrutinized the periods associated with various extreme points by comprehensively examining the curves. Additionally, by considering other curve results, we performed a comparative analysis to assess the characteristic changes in potential hazardous rock masses and predict the likelihood of their occurrence. This system exhibits significant potential for practical application in geological hazard prevention and control endeavors in similar regions.

5. Conclusions

Based on the first national geological hazard risk survey, this paper combines the analytic hierarchy process (AHP) with the information quantity method (IM). It takes different extreme rainfall conditions as the main inducing factors to evaluate the susceptibility, hazard, vulnerability, and risk of geological hazards in Laoshan District, eastern China. At the same time, geological disaster prevention and control zoning was carried out, and corresponding policy recommendations were proposed to serve regional land management and planning. The main conclusions obtained are as follows:

(1)

There are 121 geological disasters in Laoshan District. Using the AHP to assign values to the information content of evaluation factors, the susceptibility assessment results of geological disasters were obtained, generally at a relatively low level of susceptibility;

(2)

There is a positive correlation between extreme rainfall and hazards/risks. As the rainfall conditions change from once every 10 years to once every 100 years, the proportion of high-hazard zones has increased from 20% to 41%, and the ratio of high-risk zones has increased from 31% to 51%. AUC values of hazard assessment calculated by ROC are 56.3%, 63.8%, 64.2%, and 68.1%, and AUC values of risk assessment calculated by ROC are 73.9%, 66.9%, 62.4%, and 68.3%. The accuracy is acceptable but could be higher. In the future, different evaluation methods will be compared to determine the one with the highest accuracy;

(3)

According to the risk assessment results, Laoshan is divided into key, sub-key, and general prevention zones for geological disasters. Based on the different focus of each prevention and control zone, corresponding prevention and control policy recommendations have been proposed, including comprehensive management, relocation and avoidance, science popularization training, setting warning signs, etc. The established geological disaster monitoring and warning information system and emergency response system for sudden geological disasters during flood season can effectively predict the probability of geological disasters and provide a reference for preventing and controlling geological disasters in similar regions.