Hazard Maps Research Articles

Territorial exceedance of probabilistic seismic hazard from ShakeMap data

Current seismic structural design makes use of a ground motion intensity that has a certain probability of being exceeded at a site of interest in a time interval or, equivalently, exceedance return period. The design intensities with the same return period are often collected in the form of maps deriving from probabilistic seismic hazard analysis (PSHA) for each of the sites of interest. Probability theory underlying PSHA dictates that, in any time interval, design intensities are expected to be exceeded in a fraction of sites that depends on the return period the map refers to. In the case of Italy, three different nationwide PSHA studies can be currently considered of relevance. In the study, the estimated areal fraction of the Italian territory exposed to exceedance of the design intensity from 2008 to 2019 was quantified for the three hazard models, based on ShakeMap data for instrumental earthquakes. In addition, the same fraction was calculated considering a sparse catalog of inferred ShakeMap for historical earthquakes that occurred over almost 1000 years. It was found that, despite the apparent differences in the hazard models, the estimated fraction of territory exposed to exceedance is comparable for all the considered hazard maps.

Modeling Flood Susceptible Areas Using Deep Learning Techniques with Random Subspace: A Case Study of the Mae Chan Basin in Thailand

Flooding is a recurring global issue that leads to substantial loss of life and property damage. A crucial tool in managing and mitigating the impact of flooding is using flood hazard maps, which help identify high-risk areas and enable effective planning and management. This study presents a study on developing a predictive model to identify flood-prone areas in the Mae Chan Basin of Thailand using machine learning techniques, precisely the random sub-space ensemble method combined with a deep neural network (RS-DNN) and Nadam optimizer. The model was trained using 11 geographic information system (GIS) layers, including rainfall, elevation, slope, distance from the river, soil group, NDVI, road density, curvature, land use, flow accumulation, geology, and flood inventory data. Feature selection was carried out using the Gain Ratio method. The model was validated using accuracy, precision, ROC, and AUC metrics. Using the Wilcoxon signed-rank test, the effectiveness was compared to other machine learning algorithms, including random tree and support vector machines. The results showed that the RS-DNN model achieved a higher classification accuracy of 97% in both the training and testing datasets, compared to random tree (93%) and SVM (82%). The model's performance was also validated by its high AUC value of (0.99), compared to a random tree (0.93) and SVM (0.82) at a significance level of 0.05. In conclusion, the RS-DNN model is a highly accurate tool for identifying flood-prone areas, aiding in effective flood management and planning.

ON RELOCATION OF SETTLEMENT AFTER THE CIANJUR EARTHQUAKE AND LANDSLIDE 2022

An Earthquake and landslide occurred in Cianjur, Indonesia, in November 2022. This resulted in a lot of losses, both fatalities and the disappearance of some settlements. Relocation would be obligatory as post-disaster mitigation policies for areas with highest disaster threats. This research was conducted to find its relocation areas. The method used is overlay and scoring by utilizing GIS applications. The used data are rainfall data, slope, geological maps, Cianjur earthquake hazard maps, fault line buffering, and supervised classification of land cover/use maps. Analysis of the results in the form of landslide hazard maps and disaster safety maps. The search for the expected relocation area is carried out by overlaying a disaster-safe map with residential areas, so that a post-disaster residential relocation recommendation map is produced with five classes, namely very good, good, medium, bad, and very bad. An analysis is also carried out for directions for the use of the affected area for the very bad class used as a protected area and no buildings are allowed on it. Bad class is not allowed to live on it, but can be used for social activities, plantations and paddy fields. The moderate vulnerability class can still be used as a residence, economic centre, or other social activities. Meanwhile, the safe and very safe vulnerability classes do not have any land use restrictions.

Optimizing Tourism Development Through Landslide Hazard Mapping in Raung Volcano

The series of volcanic activities of Mount Raung triggered primary and secondary hazards in the area around the volcano. Kalibaru watershed is one of the areas prone to landslides due to secondary hazards of eruption activity in the direction of west and northwest part of this region. This study aims to optimize tourism by mapping landslide hazard using Spatial Multi-Criteria Evaluation (SMCE) around Mount Raung. This research used 46 points of landslide data through remote sensing, field observation. Ten landslide triggering factors, namely TPI (Topographic Position Index), TWI (Topographic Wetness Index), SPI (Stream Power Index), slope, distance to river, rainfall, geology, land use, distance to road, and soil type was used to map the landslide hazard. This study used ROC (Receiver Operating Characteristic) analysis to validate the landslide susceptibility mapping with an AUC (Area Under Curve) value of 0.93, which indicates that the mapping has a high accuracy value. The results showed that the landslide susceptibility is divided into three classes: high susceptible, moderate susceptible, and low susceptible. The high susceptible area covers 151.62 km2 (21%), the moderate susceptible area covers 407.99 km2 (56%), and the low susceptible area covers 166.79 km2 (23%). Based on the results of the mapping, tourism development in the area of Mount Raung is recommended in areas that are classified as medium and low landslide susceptibility.Keywords: Mount Raung, Landslides, SMCE, Tourism Development

Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover

Wildfires produce smoke that can affect an area >1000 times the burn extent, with far-reaching human health, ecologic, and economic impacts. Accurately estimating aerosol load within smoke plumes is therefore crucial for understanding and mitigating these impacts. We evaluated the effectiveness of the latest Collection 6.1 MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in estimating aerosol optical depth (AOD) across the U.S. during the historic 2020 wildfire season. We compared satellite-based MAIAC AOD to ground-based AERONET AOD measurements during no-, light-, medium-, and heavy-smoke conditions identified using the Hazard Mapping System Fire and Smoke Product. This smoke product consists of maximum extent smoke polygons digitized by analysts using visible band imagery and classified according to smoke density. We also examined the strength of the correlations between satellite- and ground-based AOD for major land cover types under various smoke density levels. MAIAC performed well in estimating AOD during smoke-affected conditions. Correlations between MAIAC and AERONET AOD were strong for medium- (r = 0.91) and heavy-smoke (r = 0.90) density, and MAIAC estimates of AOD showed little bias relative to ground-based AERONET measurements (normalized mean bias = 3 % for medium, 5 % for heavy smoke). During two high AOD, heavy smoke episodes, MAIAC underestimated ground-based AERONET AOD under mixed aerosol (i.e., smoke and dust; median bias = −0.08) and overestimated AOD under smoke-dominated (median bias = 0.02) aerosol. MAIAC most overestimated ground-based AERONET AOD over barren land (mean NMB = 48 %). Our findings indicate that MODIS MAIAC can provide robust estimates of AOD as smoke density increases in coming years. Increased frequency of mixed aerosol and expansion of developed land could affect the performance of the MAIAC algorithm in the future, however, with implications for evaluating wildfire-associated health and welfare effects and air quality standards.

Dynamic response of existing embankment dams in specific geotechnical and seismological conditions: contemporary framework for Serbian national guidelines

We suggest a new approach for evaluating the dynamic response of existing embankment dams as a guideline for national engineering regulations in Serbia. This novel concept is applied to the case study of the existing embankment dam “Zavoj” (Serbia), built at the place of the previously formed natural dam (due to large landslide activity), and exposed to Mw7.4 Vrančea earthquake and under double impact of Kresna earthquake (Mw6.8 and Mw7.1). Results of transient FEM and direct dynamical analysis of the three-dimensional FEM model of the “Zavoj” dam indicate that the existing dam is stable in dynamic conditions, while the surrounding terrain is either on the verge of stability or becomes unstable, depending on the assumed geotechnical conditions and a relevant earthquake analyzed. As a result, we construct a landslide hazard map in dynamic conditions and the dam damage map, which enables the identification of unstable, damaged, or weak parts of the dam, its immediate surroundings, and the accumulation zone in general. The results indicate that direct dynamical analysis is obligatory when two succeeding earthquakes occur and temporal evolution of displacements in the model is required. Additionally, the application of the remaining bearing capacity criterion indicates a much larger zone of potential instability compared to the extent of equivalent plastic strain and the location of the old sliding surface. By following the proposed guidelines, one secures the inclusion of all the relevant influential factors in the comprehensive dynamic analysis of existing embankment dams, leading eventually to reliable decisions on further design, (re)construction, and/or monitoring activities.

Modelling Soil Erosion Using RUSLE and GIS for Kadalundi River Basin in Kerala, India

The main objective of the study was to estimate the potential average annual soil loss from Kadalundi river basin using RUSLE model and to prepare the spatial distribution map of soil erosion hazard using GIS to suggest suitable soil conservation and management measures for the basin. The individual factor maps for each factor in the RUSLE equation were prepared and multiplied in raster calculator to obtain the spatial distribution map of soil erosion with the help of ArcGIS. The daily rainfall data, basic soil data, Digital Elevation Model (DEM) and satellite imageries were used as the input data for the calculation of rainfall erosivity factor, soil erodibility factor, topographic factor, cover management factor and conservation practice factor. The results indicated an average annual erosion of 5.48 t·ha-1·yr-1 in the basin during the period of 2001 to 2021. The total quantity of soil washed away from the basin was 694836.60 t·yr-1. The estimated values of R-factor ranged from 938.97 MJ·mm·ha-1·h-1·yr-1 to 1102.65 MJ·mm·ha-1·h-1·yr-1, and the computed erodibility factor values varied between 0.0075 to 0.025 t·h·MJ⁻1·mm⁻1. The topographic factor value of the basin was found between 0 to 9.07. The basin had C-factor values of 0.08 to 1.10 and the P-factor values varied from 0.1 to 1. The spatial distribution map of erosion suggest that major part of the basin is under slight erosion class and only a minor part is under very severe erosion class. Areas in the very severe erosion class is characterised by moderate to steep slopes, or in other words areas with less slope is having slight erosion. As most of the area of the basin is having slight erosion, simple agronomical measures can be adopted for the mitigation purposes.

Analysis of Basidiomycete Fungal Communities in Soil and Wood from Contrasting Zones of the AWPA Biodeterioration Hazard Map across the United States

Wood deterioration due to basidiomycetous decay fungi shortens the useful life span of wood and wood-based materials. Prescriptive preservative treatment is the most effective way to reduce the detrimental effects of these microorganisms, particularly in soil contact and areas of critical use (difficult to replace or vital to structure). Current American Wood Protection Association (AWPA) guidelines in the standardized use category system specify 3 zones of severity regarding wood decay fungal hazards but contain very little information on the diversity and abundance of these fungi colonizing soil and wood. In this study, amplicon based sequencing was utilized to compare fungal communities in wood and adjacent soil to provide baseline data on the fungi involved in the process. A thorough understanding of decay hazards is critical for the proper selection and use of wood in soil contact. The goal of this work is to provide baseline data on basidiomycete fungal diversity and species composition in different zones of the existing 3-zone AWPA hazard map as compared to the previous 5-zone hazard map and Scheffer decay indices and discuss the ecological implications for wood decay.

Estimating peak flow of Farahzad River in Tehran under climate change and debris flow scenarios: A novel approach and its implications for urban flood hazard mapping

Abstract Urban river flooding is a serious threat to cities that have altered their river buffer zones due to urbanization and climate change. This study aims to estimate the peak flow of urban rivers by considering the effects of climate change and debris flow on flood hazard. A novel approach is proposed that integrates hydrological, sedimentological, and hydraulic method and models to account for the influence of sediment volume, woody debris, and culvert blockage on peak flow estimation and flow parameters. The approach is applied to the Farahzad River basin in Tehran, Iran, using future data downscaled from a global climate model under the RCP8.5 scenario for 50- and 100-year return periods. The results show a significant increase in the peak flood discharge by nearly 3.2 times, the flood zone by 10–20%, the flood velocity by 15–30%, and the inundation depth by 10–40% due to climate change and debris flow scenarios. The study demonstrates the importance of considering multiple factors in estimating the peak flow of urban rivers and provides a useful tool for urban flood risk management.

Multi-Disaster Hazard Analysis, The Case of Elazığ Province

In this study, a comprehensive assessment of disaster hazards in Elazığ province, where the Eastern Anatolian Fault Zone passes through, was conducted. Hazard maps for earthquakes, floods, landslides, rockfalls, avalanches, desertification, and erosion were integrated to create a multi-hazard map. Various methods, such as the Analytic Hierarchy Process (AHP) and machine learning models, including the Random Forest algorithm, were employed to assess the severity and probability of exposure for each hazard type. Independent variables, including VS30, liquefaction potential, Digital Elevation Model (DEM)-derived data, and climatic data, were selected based on relevant literature and the study area. For earthquake and erosion hazards, intuitive models were used due to the absence of a single dependent variable. The desertification map was obtained from the Ministry of Environment, Urban Planning, and Climate Change. The Random Forest model was used for other disaster hazard maps. All hazard maps were combined using a hierarchical approach with the Weighted Overlay tool. The study generated a spatial synthesis and database intended to offer proactive insights into disaster preparedness, optimizing resource allocation, and expediting recovery efforts post-disaster within the Elazığ Province. Its primary objective is to provide assistance to local authorities and emergency response teams. In the province, a significant portion of urban settlements and the majority of rural areas face high earthquake hazards. Floods pose a considerable risk, particularly in low-lying areas downstream of numerous dams scattered across the province, as well as at the confluence points of seasonal riverbeds. The hazard of landslides is high in the rugged areas along the EAF and in steep terrains eroded by rivers. Moreover, rock falls occur more frequently in mountainous areas along the Hazarbaba-Akdağ axis due to erosion and physical dissolution. Erosion and desertification represent significant slow-moving hazards, with erosion intensifying on steep slopes and barren lands, while desertification notably affects Baskil and its surrounding low-lying areas in the western part of the province. Considering multiple hazards, areas with concentrated settlements and economic activities such as Elazığ, Baskil, Kovancılar, Karakoçan, and Behrimaz plains are categorized as very high and high-risk zones.

Assessing LISFLOOD-FP with the next-generation digital elevation model FABDEM using household survey and remote sensing data in the Central Highlands of Vietnam

Abstract. Flooding is an endemic global challenge with annual damages totalling billions of dollars. Impacts are felt most acutely in low- and middle-income countries, where rapid demographic change is driving increased exposure. These areas also tend to lack high-precision hazard mapping data with which to better understand or manage risk. To address this information gap a number of global flood models have been developed in recent years. However, there is substantial uncertainty over the performance of these data products. Arguably the most important component of a global flood model is the digital elevation model (DEM), which must represent the terrain without surface artifacts such as forests and buildings. Here we develop and evaluate a next generation of global hydrodynamic flood model based on the recently released FABDEM DEM. We evaluate the model and compare it to a previous version using the MERIT DEM at three study sites in the Central Highlands of Vietnam using two independent validation data sets based on a household survey and remotely sensed observations of recent flooding. The global flood model based on FABDEM consistently outperformed a model based on MERIT, and the agreement between the model and remote sensing was greater than the agreement between the two validation data sets.

Integrated flood risk assessment in Hunza-Nagar, Pakistan: unifying big climate data analytics and multi-criteria decision-making with GIS

Floods are a widespread natural disaster with substantial economic implications and far-reaching consequences. In Northern Pakistan, the Hunza-Nagar valley faces vulnerability to floods, posing significant challenges to its sustainable development. This study aimed to evaluate flood risk in the region by employing a GIS-based Multi-Criteria Decision Analysis (MCDA) approach and big climate data records. By using a comprehensive flood risk assessment model, a flood hazard map was developed by considering nine influential factors: rainfall, regional temperature variation, distance to the river, elevation, slope, Normalized difference vegetation index (NDVI), Topographic wetness index (TWI), land use/land cover (LULC), curvature, and soil type. The analytical hierarchy process (AHP) analysis assigned weights to each factor and integrated with geospatial data using a GIS to generate flood risk maps, classifying hazard levels into five categories. The study assigned higher importance to rainfall, distance to the river, elevation, and slope compared to NDVI, TWI, LULC, curvature, and soil type. The weighted overlay flood risk map obtained from the reclassified maps of nine influencing factors identified 6% of the total area as very high, 36% as high, 41% as moderate, 16% as low, and 1% as very low flood risk. The accuracy of the flood risk model was demonstrated through the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis, yielding a commendable prediction accuracy of 0.773. This MCDA approach offers an efficient and direct means of flood risk modeling, utilizing fundamental GIS data. The model serves as a valuable tool for decision-makers, enhancing flood risk awareness and providing vital insights for disaster management authorities in the Hunza-Nagar Valley. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.

Mapping of flood hazard induced by land subsidence in Semarang City, Indonesia, using hydraulic and spatial models

Mapping of flood hazard induced by land subsidence in Semarang City, Indonesia, using hydraulic and spatial models

Integration of HEC-RAS and HEC-HMS with GIS in Flood Modeling and Flood Hazard Mapping

Floods are among the most devastating disasters in terms of socio-economics and casualties. However, these natural disasters can be managed and their effects can be minimized by flood modeling performed before the occurrence of a flood. In this study, flood modeling was developed for the Göksu River Basin, Mersin, Türkiye. Flood hazard and risk maps were prepared by using GIS, HEC-RAS, and HEC-HMS. In hydraulic modeling, Manning’s n values were obtained from 2018 CORINE data, return period flow rates (Q25, Q50, Q100, Q500) were obtained from HEC-HMS, and the application was carried out on a 5 m resolution digital surface model. In the study area, the water depths could reach up to 10 m, and water speeds were approximately 0.7 m/s. Considering these values and the fact that the study area is an urban area, hazard maps were obtained according to the UK Department for Environment, Food and Rural Affairs (DEFRA) method. The results indicated that possible flood flow rates from Q25 to Q500, from 1191.7 m3/s to 1888.3 m3/s, were detected in the study area with HEC-HMS. Flooding also occurred under conditions of the Q25 flow rate (from 4288 km2 to 5767 km2), and the impacted areas were classified as extremely risky by the DEFRA method.

Mapping Geoelectric Field Hazards in Ireland

AbstractGeoelectric fields are generated at the Earth's surface and can lead to the induction of hazardous geomagnetically induced currents (GIC) in infrastructure like power grids, railways and pipelines during geomagnetic storms. Magnitude and orientation of the geoelectric fields, in relation to the infrastructure, are key features needed to determine the intensity of GIC. Here, we developed the first geoelectric hazard map for the island of Ireland, with the aim of providing detailed information that can help stakeholders mitigate the impact of GICs. The hazard map was developed by modeling and mapping the geoelectric field across Ireland for 28 years (1991–2018) using magnetic field data with magnetotelluric transfer functions. The approach for developing the hazard map calculates the probability of exceeding a hazardous geoelectric field threshold (500 mV/km) during large geomagnetic storms, taking directionality and amplitude into account. We found hazardous geoelectric fields to be mostly localized in areas in the west, south‐west and northern coast. We observed that the geoelectric field have a stronger dominant orientation than the orientation of the geomagnetic field, often constraining the hazardous geoelectric field in particular directions only. We demonstrate a seasonal/diurnal effect is present in the geoelectric field time series. The impact of galvanic distortion was also assessed, and we demonstrate that there is a significant difference in terms of amplitude and direction between both models.

Development of a hazard risk map for assessing pedestrian risk in urban flash floods: A case study in Cúcuta, Colombia

AbstractThe rapid growth of impervious areas in urban basins worldwide has increased the number of impermeable surfaces in cities, leading to severe flooding and significant economic losses for civilians. This trend highlights the urgent need for methodologies that assess flood hazards and specifically address the direct impact on pedestrians, which is often overlooked in traditional flood hazard analyses. This study aims to evaluate a methodology for assessing the risk to pedestrians from hydrodynamic forces during urban floods, with a specific focus on Cúcuta, Colombia. The methodology couples research outcomes from other studies on the impact of floodwaters on individuals of different ages and sizes with 1D/2D hydrological modeling. Advanced computational algorithms for image recognition were used to measure water levels at 5‐s intervals on November 6, 2020, using drones for digital elevation model data collection. In Cúcuta, where flood risk is high and drainage infrastructure is limited, the PCSWMM (Computer‐based Urban Stormwater Management Model) was calibrated and validated to simulate extreme flood events. The model incorporated urban infrastructure details and geomorphological parameters of Cúcuta's urban basin. Four return periods (5, 10, 50, 100), with extreme rainfall of 3 h, were used to estimate the variability of the risk map. The output of the model was analyzed, and an integrated and time‐varying comparison of the results was done. Results show that the regions of high‐water depth and high velocity could vary significantly along the duration of the different extreme events. Also, from 5 to 100 years return period, the percentage of area at risk increased from 9.6% to 16.6%. The pedestrian sensitivity appears much higher than the increase in velocities or water depth individually. This study identified medium to high‐risk locations, which are dynamic in time. We can conclude dynamics are spatiotemporal, and the added information layer of pedestrians brings vulnerability information that is also dynamic. Areas of immediate concern in Cúcuta can enhance pedestrian safety during flash flood events. The spatiotemporal variation of patterns requires further studies to map trajectories and sequences that machine learning models could capture.

Economic Risk Assessment of Future Debris Flows by Machine Learning Method

A reliable economic risk map is critical for effective debris-flow mitigation. However, the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application. To estimate the economic risks caused by future debris flows, a machine learning-based method was proposed to generate an economic risk map by multiplying a debris-flow hazard map and an economic vulnerability map. We selected the Gyirong Zangbo Basin as the study area because frequent severe debris flows impact the area every year. The debris-flow hazard map was developed through the multiplication of the annual probability of spatial impact, temporal probability, and annual susceptibility. We employed a hybrid machine learning model—certainty factor-genetic algorithm-support vector classification—to calculate susceptibilities. Simultaneously, a Poisson model was applied for temporal probabilities, while the determination of annual probability of spatial impact relied on statistical results. Additionally, four major elements at risk were selected for the generation of an economic loss map: roads, vegetation-covered land, residential buildings, and farmland. The economic loss of elements at risk was calculated based on physical vulnerabilities and their economic values. Therefore, we proposed a physical vulnerability matrix for residential buildings, factoring in impact pressure on buildings and their horizontal distance and vertical distance to debris-flow channels. In this context, an ensemble model (XGBoost) was used to predict debris-flow volumes to calculate impact pressures on buildings. The results show that residential buildings occupy 76.7% of the total economic risk, while road-covered areas contribute approximately 6.85%. Vegetation-covered land and farmland collectively represent 16.45% of the entire risk. These findings can provide a scientific support for the effective mitigation of future debris flows.

Disaster Awareness and Preparedness and Disaster Risk Reduction Practices among Secondary Schools

Inevitable disasters pose a concealed threat that school leaders must address to prevent calamity and safeguard lives and assets. This study determined the disaster risk reduction awareness in selected secondary schools, aiming to mitigate current risks and enhance the resilience of vulnerable institutions. This study uses a quantitative approach. The research employed online surveys to gauge disaster risk reduction management among 50 secondary school heads and teachers in Northern Puerto Princesa City, Philippines. The findings revealed a high level of awareness among respondents, highlighting the importance of implementing programs like budget allocation for DRRM activities, student-led hazard mapping, and collaboration with local government units and NGOs. In conclusion, strengthening disaster risk reduction management and fostering partnerships are recommended for secondary schools to enhance preparedness and resilience.

Comparison of peak ground acceleration using deterministic and probabilistic approach in the coastal area of Loh Buaya, Rinca Island, Indonesia

An area located near an active volcano, subduction zone, and/or active fault has a risk of an earthquake. Earthquakes can cause damage to buildings with a large number of losses. One of the earthquake-related parameters required in planning an earthquake-resistant building is the Peak Ground Acceleration (PGA). The objective of this study is to compare the PGA value at the coastal area of Loh Buaya, Rinca Island, Indonesia, using Probabilistic Seismic Hazard Analysis (PSHA) and Deterministic Seismic Hazard Analysis (DSHA). The DSHA is calculated based on earthquake data series from the United States Geological Survey (USGS) in the period between 1922 to 2022. Also, it is calculated using 10 data of Ground Motion Prediction Equation (GMPE). Data from the USGS is constrained with magnitude > 5.0 MW and earthquake distance < 200 km from the study site. The PSHA is based on the Indonesian earthquake hazard map and the de-aggregation map from the National Earthquake Study Centre of Indonesia with a probability exceeding 2% in 50 years. Based on the parameters of PGA maximum, distance, and magnitude, the DSHA by Kanno method has the closest result to the PSHA. It is concluded that this area has a high earthquake risk with a PGA maximum of more than 0.5 g.

Flood Hazards Mapping by Linking CF, AHP, and Fuzzy Logic Techniques in Urban Areas

Flood Hazards Mapping by Linking CF, AHP, and Fuzzy Logic Techniques in Urban Areas