Potential Flood Research Articles

Penggunaan Media Sosial untuk Penyebarluasan Informasi Peringatan Dini Banjir ke Masyarakat (Studi Kasus: BPBD Provinsi DKI Jakarta)

This study aims to analyze the role of social media in the dissemination of flood early warning information carried out by the Jakarta Provincial BPBD. Against the background of the importance of an effective early warning system, this study explores the use of social media as a communication channel that can reach the wider community quickly. Using a qualitative approach, this case study observed post data from BPBD social media platforms such as X (Twitter), Facebook, and Instagram in the period from November 2023 to February 2024. The results of the study show that BPBD actively uses social media to provide early warning information related to floods from various sources, such as upstream rain, local rain, and coastal floods. The conclusion of this study is that social media is effective in increasing public preparedness against potential floods, despite the challenges in maintaining public trust and ensuring understanding of the message conveyed.

Enhancement of surfactant performance via titanium dioxide nanoparticles: implication for oil recovery in sandstone

The application of titanium dioxide nanoparticles in the petroleum research area has received ample attention in recent years owing to its impact on wettability-altering agents. Further, employing a surfactant injection to improve oil production in sandstone formations on an industrial scale has become an alternative solution, particularly for mature fields. However, the existing literature on the combination of alkyl ethoxy carboxylate (AEC) surfactant with titanium dioxide nanoparticles on the application of enhanced oil recovery in sandstone formations remains underreported. This study explores the impact of combining AEC surfactant with titanium dioxide nanoparticles on recovering trapped oil in sandstone by examining the interfacial tension, contact angle, zeta potential, and core flooding with various concentrations of added titanium dioxide nanoparticles (0, 0.01, 0.025, and 0.05 wt%) on AEC surfactant. Although the addition of 0.05 wt% TiO2 to AEC surfactant can significantly reduce the interfacial tension to the lowest value of 5.85 × 10−5 mN/m, our results show that the highest oil recovery in Berea sandstone (59.52% recovery factor) is achieved at the concentration of 0.025 wt% added TiO2 to AEC surfactant. We find that the stability of TiO2 nanoparticles on AEC surfactant plays a significant role in getting maximum oil recovery. These important findings from this study contribute to improving our understanding on the application of TiO2 combined with AEC surfactant to achieve more efficient and sustainable enhanced oil recovery in sandstone.

Unveiling the role of storm surges as a driver of flooding on the western Mediterranean: a case study of the Ebro Delta

AbstractStorm surges in the Western Mediterranean are generally low in magnitude, making their contribution to coastal flooding less significant compared to wave overtopping. Nonetheless, low-lying, sheltered coasts such as deltas and wetlands, which are frequent along the Mediterranean basin are particularly vulnerable to storm surges. This study, focusing on the Ebro Delta as representative of this type of coastal environment, investigates the flooding caused by storm surge alone and in conjunction with other non-wave related factors like astronomical tides and sea level rise (SLR), using the LISFLOOD-FP model. The findings highlight the significant flooding potential of storm surges on passive, and unprotected coasts, while also indicate that astronomical tides have a minor effect on flood extent under prevalent microtidal conditions. SLR greatly increases the impact of storm surges, amplifying temporary inundation in the short term and becoming the dominant factor over time. The study underscores the importance of accurately representing surge duration and small topographic features in flood models to ensure robust coastal inundation assessments in low-lying areas.

Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra

The downtown area of the city of Coimbra, Portugal, is at low altitude and has historically suffered floods that have caused serious economic losses. The present research proposes a mobile augmented reality (MAR) application aimed at visualising the effect of possible scenarios of flooding in an area of higher risk in the city. A realistic 3D model of the city was created, using data extracted with BLosm and processed through Blender, followed by its integration into Unity with Vuforia for AR visualisation. The methodology encompasses the extraction and simplification of 3D models, mapping real-world coordinates in Unity, analysing several datasets, obtaining a model through regression and implementing a workflow to manage interactions between various Unity objects. The MAR application enables users to visualise potential flood impacts on buildings, utilising colour-coded indicators to represent different levels of water contact. The system’s efficacy was evaluated by simulating various use-case scenarios, demonstrating the application’s capability to provide real-time, interactive flood risk assessments. The results underline the potential of integrating AR and machine learning for enhancing urban flood management and prevention.

Employing deep learning in crisis management and decision making through prediction using time series data in Mosul Dam Northern Iraq

Specifically, Iraq is threatened in its second-largest northern city, Mosul, by the collapse of the Mosul Dam due to problems at the root of the dam, causing a wave of floods that will cause massive loss of life, resources, and public property. The objective of this study is to effectively monitor the level of dam water by predicting the level of water held by the dam In anticipation of achieving flood stage and breaking the dam, and supporting its behavior through formation 14-day time series data to predict seven days later. Used six deep learning models (deep neural network (DNN), convolutional neural network (CNN), convolutional neural network long short-term memory (CNN-LSTM), CNN-LSTM-Skip and CNN-LSTM Skip Attention) that models were trained to predict the water level in the dam; these levels of being under surveillance and prepared In case of danger, alert people to potential flood threats depending on the dam’s water level. These time series were created from the actual data sets of the dam; it’s a fundamental historical reading for 13 years (1993–2006) of the water level stored in the Mosul dam and was adopted in coordination with the Iraqi Ministry of Water Resources and the Centre for Research on Dams and Water Resources at Mosul University. The methodology applied in this study shows the model’s performance efficiency and the prediction results’ low error rate. It also compares those practical results to determine and adopt the performance-efficient model and a lower error rate. The comparison of these results proved the accuracy of its results, and superior to the CNN-LSTM model, it has the highest ability to perform through high accuracy with MAE = 0.087153 and time steps = 0 s 196 ms/step and loss = 0.00067. The current study demonstrated the ability to predict the water level in Mosul Dam, which suffers from foundation problems and may collapse in the future. Therefore, the water level in the dam must be monitored accurately. It also aims to test the effectiveness of the six models proposed in this study after evaluating their performance and applying the prediction process within a scenario to obtain predictive values after 14 days. The results showed the practical effectiveness of the hybrid CNN-LSTM model in correctly and accurately obtaining predictive values within the integrated framework of the required scenario. The study concluded that it is possible to enhance the ability to monitor and identify the potential risk of Mosul Dam at an early stage, and it also allows for proactive crisis management and sound decision-making, thus mitigating the adverse effects of crises on public safety and infrastructure and reducing human losses in areas along the Tigris River.

The Surface Water Potentiality in Arid and Semi-Arid Basins Using GIS and HEC-HMS Modeling, Case Study: Gebel El Sibai Watershed, Red Sea

The Red Sea region is considered one of the regions that suffer most from water scarcity among the Egyptian areas. This situation reinforces the importance of maximizing the utilization of available water sources. Rainwater and flood harvesting may form a good water source if good harvesting practices are applied. Natural pastures, Bedouin communities, and wild plants may be affected by severe droughts expected due to climate change. Additional water resources are very important to enhance the resilience of the Bedouin communities to probable droughts. Five main hydrographic basins are issued from Gebel El Sibai (+1435 m), including Wadi Esel, Wadi Sharm El Bahari, Wadi Sharm El Qibli, Wadi Wizr, and Wadi Umm Gheig. Detailed investigation of morphometric parameters, runoff/rainfall relationship, and flood volume using GIS and HEC-HMS model of each basin were estimated as well as natural vegetation. This study reveals that rainfall ranges from 84 mm to 0 mm, and a storm of 84 mm (highest event) is expected to occur every 42 years with a probability of 2.4%. Quantitative morphometric analysis implies that the area has good potential for flooding, especially Wadi Sharm El Qibli and Wadi Umm Gheig, where Wadi Sharm El Bahri represents the lowest priority for flooding. The flood volume of Umm Gheig basin is the greatest: 12 million m3 at the basin outlet with a rainfall event of 15 mm. Wadi Esel is expected to collect 8.7 million m3 due to the ratio of the impervious soil and rainfall quantity, Wadi Sharm El Bahari 2.1 million m3, Wadi Sharm El Qibli 1.6 million m3, and Wadi Wizer 1.04 million m3. Seven storage dams (SD1-SD7) were proposed to enhance the utilization of the surface water potentialities of this study area.

Multi-criteria analysis and mapping of the vulnerable flood risk areas of Jigawa State, Nigeria

This study evaluates flood risk factors and maps flood-prone areas in Jigawa State, Nigeria, using geospatial techniques. By integrating various flood-causing factors—such as rainfall distribution, elevation, slope, drainage network and density, land cover and use, flow accumulation, and soil type— through a multi-parametric approach, it employs the Analytical Hierarchy Process (AHP) to perform pairwise comparisons and assign priority weights to each factor. Spatial multi-criteria analysis (MCA) then ranks and identifies potential flood locations. Using ArcGIS's weighted overlay tool, these layers are combined to produce a final flood risk map, classifying areas into high, very high, moderate, low, and very low-risk categories. Elevation and slope were found to have the greatest impact on flooding, with normalized criterion weights of 30 and 22, respectively. The Consistency Ratio (CR) of 0.06 validated the strength of the judgment, and these AHP-derived factor weights were used to create the flood risk map showing areas with varying risk levels, highlighting eight local governments in the study area. —Auyo, Hadejia, Kirika-samma, Kafin-Hausa, Ringim, Miga, Jahun, and Dutse—compose areas of very high risk, while Guri, some portions of northern Kiri-kasamma, southern Jahun, northern Auyo, Birnin-Kudu, and northwestern Ringim local governments comprise high-risk areas. Areas of moderate risk comprise a small portion of northern Kirika-samma, larger portions of Gwaram, small portions of Jahun, Kafin-Hausa, Dutse and Birnin-kudu local governments respectively and areas of low to very low risks consist of many parts of Gwaram and some tiny portions of Birnin-kudu.

The Affect of Physical Parameters on Flood Potential in the Upstream River and the Musi Watershed of Kepahiang, Indonesia

Fllod often occur if the rainfall is high, the flood-affected areas are downstream areas around the Musi Watershed of Kepahiang. The purpose of the study was to find out the characteristics of the physical parameters that affect the potential for flooding in the upstream river and the Musi Watershed. The method used are in situ measurement and laboratory analysis. The results of research, the characteristics of the river's physical parameters after the rain, the river flow discharge is 14.50 m3/s, floating sediment discharge 1.13 kg/s, bottom sediment discharge 43 x 10-4 kg/s, and total sediment discharge 113 x 10-2 kg/s. While the condition is not raining, the river flow discharge is 5.37 m3/s, the floating sediment discharge is 0.30 kg/s, the bottom sediment discharge is 32 x 10-4 kg/s, and the total sediment discharge is 30 x 10-2 kg/s. The dominant sediment texture is sand, clay and silt with a grain size of 1.4 mm - 600 µm. The river bathymetry has a lower elevation at the confluence of the Gegasan River and Lanang River which causes the river currents to reverse, if there is an increase in rainfall it will cause flooding in the area around the river.

Daily Rainfall Patterns During Storm “Daniel” Based on Different Satellite Data

Extreme rainfall from a long-lived weather system called storm “Daniel” occurred from 4th to 11th September 2023 over the central and eastern Mediterranean, leading to many devastating flood events mainly in central Greece and the western coastal parts of Libya. This study analyzes the daily rainfall amounts over all the affected geographical areas during storm “Daniel” by comparing three different satellite-based rainfall data products. Two of them are strictly related to Meteosat multispectral imagery, while the other one is based on the Global Precipitation Measurement (GPM) satellite mission. The satellite datasets depict extreme daily rainfall (up to 450 mm) for consecutive days in the same areas, with the spatial distribution of such rainfall amounts covering thousands of square kilometers almost during the whole period that the storm lasted. Moreover, the spatial extent of the heavy rainfall patterns was calculated on a daily basis. The convective nature of the rainfall, which was also recorded, characterizes the extremity of this weather system. Finally, the intercomparison of the datasets used highlights the satisfactory efficiency of the examined satellite datasets in capturing similar rainfall amounts in the same areas (daily mean error of 15 mm, mean absolute error of up to 35 mm and correlation coefficient ranging from 0.6 to 0.9 in most of the examined cases). This finding confirms the realistic detection and monitoring of the different satellite-based rainfall products, which should be used for early warning and decision-making regarding potential flood events.

Participatory multi-criteria decision making for optimal siting of multipurpose artificial reservoirs

Water resources management is a crucial activity due to climate change concerns and increasing water scarcity. The increase of water storage by building new artificial reservoirs is one of the most common solutions to fulfill the community’s needs for potable water, energy, irrigation, and prevention of flood risk. The siting of the reservoirs is usually assessed based on topographical and hydrological considerations. However, there are additional crucial factors, often disregarded in common engineering top-down design approaches, that should be considered to determine suitable locations, such as bio-physical, socio-economic, regulatory, and environmental factors, especially where there are competing interests in the use of territory. Involving the communities in the initial stages of the decision-making process is therefore crucial. In this work, we present a methodology, based on multi-criteria decision-making analysis (MCDA), for identifying the optimal locations for new reservoirs while simultaneously addressing all the aforementioned factors through community engagement. The presented method aims at overcoming two major challenges in existing works, i.e., retrieving quantitative information from the participatory approach and going beyond standard hydro-morphological suitability of a dam site. In a first step, an algorithm assesses a large number of sites using a Digital Elevation Model (DEM). Then, MCDA is conducted to rank all potential locations. The selection criteria are defined quantitatively, based on a territorial analysis combined with hydrological modelling. The criteria include: geometric and morphological aspects (reservoir volume, etc.), hydrological indicators (water balance, potential flood mitigation), anthropization (population, infrastructures, etc.), landscape, archaeological heritage, ecology, environmental components and natural hazards. A web-based survey tool based on the pairwise comparison is developed to engage the community in the decision-making process, allowing them to express their perspectives on the relative significance of each criterion. The application of this methodology is demonstrated through a case study in the Pesa river watershed, Italy, showcasing its effectiveness in identifying the most suitable reservoir locations while targeting the highest environmental preservation and community acceptance. The results show that sole engineering criteria might not reflect the sensibility of the community and that variations of the size of the dam can lead to distinct trade-offs between costs and benefits where interference with impact elements occurs.

Evaluation of Potential Flood Areas in the Basin of Lake Ladik through AHP and GIS Integration, (Samsun, Türkiye)

Floods are a problem of many countries on a global scale. In Türkiye, especially in the summer months, large floods occur in the Black Sea Region coastal belt. The number and the destructive power of experienced floods are increasing day by day. It is not possible to prevent the occurrence of floods. But it is possible to take the necessary measures to prevent it from turning into a disaster. Recently, susceptibility analyses have been carried out for floods and similar natural disasters and the results help decision-makers. Within the scope of this study, flood susceptibility analysis was carried out by using multiple geographical factors together in the Basin of Lake Ladik of Samsun Province in the Black Sea Region. In the study, Analytical Hierarchy Process (AHP), Geographic Information Systems (GIS) and Remote Sensing (RS) techniques were used, which are multi-criteria decision-making methods. In this context, flood susceptibility analysis of the basin was carried out by using nine (9) different geographical factors (slope, aspect, lithology, soil, basin size, land use, landforms, precipitation, and drainage density). As a result of the study, for floods, there were identified four (4) different levels as low, medium, high, and very high; and 36.77% of the basin was found to be low, 30.03% was medium, 11.43% was high and 21.77% was found to be sensitive to possible floods at a very high level. The results of the study are also important for decision-makers to make flood risk planning.

The Coupled Application of the DB-IWHR Model and the MIKE 21 Model for the Assessment of Dam Failure Risk

The phenomenon of global climate change has led to an increase in the frequency of extreme precipitation events, an acceleration in the melting of glaciers and snow cover, and an elevation of the risk of flooding. In this study, the DB-IWHR model was employed in conjunction with the MIKE 21 hydrodynamic model to develop a simulation system for the dam failure flow process of an earth and rock dam. The study concentrated on the KET reservoir, and 12 dam failure scenarios were devised based on varying design flood criteria. The impact of reservoir failures on flood-risk areas was subjected to detailed analysis, with consideration given to a range of potential failure scenarios and flood sizes. It was determined that under identical inflow frequency conditions, the higher the water level, the more rapid the breakout process and the corresponding increase in flood peak discharge. Conversely, for a given frequency of incoming water, an elevated water level results in a transient breach process, accompanied by a reduction in flood peak flow. Moreover, for a given water level, an increase in water frequency results in a reduction in breaching time, an extension of flood duration, and an increase in flood peak flow. The observed trend of flood spreading is generally north-south, and this process is highly compatible with the topographic and geomorphological features, demonstrating good adaptability.

Smart hotspot detection using geospatial artificial intelligence: A machine learning approach to reduce flood risk

This study employs Geospatial Artificial Intelligence (GeoAI) and the Random Forest Machine Learning (ML) algorithm to enhance flood hazard assessments in Portugal. It utilizes NASA's LP DAAC (2023) Digital Elevation Model (DEM) and slope data from EPIC WEBGIS PORTUGAL DATA, offering detailed topographical insights for environmental planning. Additionally, it incorporates data on proximity to water bodies from the Portuguese Environment Agency and the European Environment Agency, and soil characteristics from EPIC WEBGIS PORTUGAL DATA, facilitating a thorough examination of flood risks. This approach prioritizes long-term land features over short-term weather patterns, providing a comprehensive understanding of flood vulnerability. The study processes data at a 1 km x 1 km resolution, adapting TIFF maps for compatibility with the Random Forest model. The produced flood hazard maps identify potential flood hotspots at both national and city levels, crucial for urban planning. These maps aid in assessing the vulnerability of key infrastructure and assets, such as transport networks and buildings. The research highlights the importance of integrating additional data on assets and socioeconomic factors to enhance urban resilience. It sets the stage for future research aimed at improving predictive accuracy and underscores the necessity of extensive geospatial analytics in managing infrastructure risks.

Application of GRACE-based satellite estimates in the assessment of flood potential: A case study of Gangetic-Brahmaputra basin, India.

The high frequency of flood occurrences and the uneven distribution of hydrological stations make it difficult to monitor large-scale floods. Emergence of the Gravity Recovery and Climate Experiment (GRACE) satellite system sets up a new era of large-scale flood monitoring without much reliance on in situ hydrological observations. The GRACE-derived flood potential index (FPI) exhibits its ability to monitor major events of 2003, 2004, 2007, and 2008 over the Indo-Gangetic-Brahmaputra Basin (IGBB). Precipitation and soil moisture are the major influencing factors of flood. However, the response of potential flooding to such parameters is little known. Pearson's lag correlation analysis is used to examine the response of the GRACE-based FPI to precipitation and soil moisture over the study region comparing seasonal time series of the variables. Results exhibited a 2-month lagged response of FPI to precipitation in the Upper Gangetic Yamuna Chambal Basin (UGYCB) and the Lower Gangetic Basin (LGB) and 1-month lagged response in the Lower Brahmaputra Basin (LBB). With context to soil moisture, a 1-month lag is observed in the Gangetic basins, and no lag is observed in the LBB. Event wise analysis of the lags portrays slightly varying lags for different events; however, it provides a picture on the interaction between these variables. This study also assesses the agreement between FPI and satellite-based river discharge, i.e. Dartmouth Flood Observatory (DFO) discharge. A good correlation (> 0.60) between the two is observed. Threshold values of FPI are determined for the LBB due to its annual flood frequency. The nearly similar accuracy of threshold FPI, determined using DFO discharge, in monitoring floods and the predictive skill measure of FPI for LBB to the previous studies demonstrates the utility of satellite-based discharge in the quantification of threshold FPI values for different percentile floods.

Building sustainable mitigation through adaptive green open space on tanjung mas sub district

Abstract Mitigation is a step to reduce the risk of disasters in a particular area. Tanjung Mas sub-district is an area that has a high potential for land subsidence and tidal flooding because it is located on the North Coast of Java. Temporary evacuation points are urgently needed to protect residents from the dangers of tidal floods and distribute logistics from volunteers. Based on community preferences, the Green Open Space in Tambak Lorok are used as an evacuation point for flood disasters. The green open space, which was initially used for sustainable water absorption, was converted into an evacuation point as a flood disaster mitigation measure. This research aims to measure the suitability of green open space as a temporary evacuation site. The research method used is a Space Syntax approach and qualitative description analysis. Based on the results of the analysis, almost all the requirements for evacuation places by the green open space location. It is easily accessible because it is on the primary residential route, easily accessible by vehicles, and has building structures higher than the road. However, the area of green open space still needs to be increased for the minimum standard capacity per person. Apart from that, the infrastructure to support evacuation, such as electricity, communication equipment, or rubber boats, needs to be improved. The construction of green open space as a temporary evacuation site needs to be adjusted to minimum standards and the needs of the population. It is also hoped that the development of green open space will be the responsibility of not only the government but also cooperation between stakeholders. Green open space isn’t just for environment impacts, but can also increase the community resilience.

Frequency and Spatial Variability of European Record Floods

AbstractRegional envelope curves (RECs) have been used to characterize the flooding potential of regions worldwide. However, a comprehensive assessment for Europe is still missing. In this study we use the largest European flood database to quantify the magnitude of record floods, their dependence on catchment size, and the amount of available independent information, which is needed for estimatinig the REC's exceedance probability. We propose a framework for estimating REC parameters across large areas, consisting in partitioning the domain according to the observation density and estimating the REC slope through quantile regression. The results show that the REC parameters vary substantially across Europe. The envelope unit flood associated with a catchment area of 1,000 km2 varies between 0.1 and 6 m3s−1 km−2 and is highest in Mediterranean and Alpine areas and lowest in central‐eastern Europe. The slope of the envelope varies between 0 and −1 and is larger in Southern Europe than in Northern Europe. These differences are explained by steeper landscape in mountaineous regions and localized short duration storms producing large floods in small catchments in the Mediterranean, whose intensities are larger than in other parts of Europe. Based on the framework of probabilistic envelope curves we show that, despite the uneven observation density, the obtained RECs are associated with comparable exceedance probabilities.

A simple analytical method to estimate runoff generation and accumulation

The study of runoff formation in semiarid regions relies generally on the solution of Richards equations using numerical approaches such as those provided by Hydrus-1D. We demonstrate here that a Simple Analytical Method (SAM) could be easily applied to estimate areas under flooding risk, under different soil and rainfall conditions, without the need to run burdensome numerical models. The impact of antecedent soil moisture condition was expressed quantitatively, conferring to SAM a predictive capability based on a-priori information on soil properties, rainfall characteristics, and initial conditions. The SAM was applied to the Shikma catchment, in the center of Israel, to illustrate its capability to estimate the spatial distribution of time of runoff occurrence and runoff coefficient for a prescribed rainfall event. The results suggest that SAM can be used as part of early warning systems of flood risks. Radars, carried by satellites and drones, can be used to provide refined data on rainfall intensity and soil surface initial conditions. SAM could be then coupled with these remotely sensed data to improve runoff initiation and accumulation estimates, thus reducing the time interval required to reach operational decisions regarding potential floods and inundations, and related water erosion hazards.

Precipitation Proxies for Flash Flooding: A Seven-Year Analysis over the Contiguous United States

Abstract Flash flooding remains a challenging prediction problem, which is exacerbated by the lack of a universally accepted definition of the phenomenon. In this article, we extend prior analysis to examine the correspondence of various combinations of quantitative precipitation estimates (QPEs) and precipitation thresholds to observed occurrences of flash floods, additionally considering short-term quantitative precipitation forecasts from a convection-allowing model. Consistent with previous studies, there is large variability between QPE datasets in the frequency of “heavy” precipitation events. There is also large regional variability in the best thresholds for correspondence with reported flash floods. In general, flash flood guidance (FFG) exceedances provide the best correspondence with observed flash floods, although the best correspondence is often found for exceedances of ratios of FFG above or below unity. In the interior western United States, NOAA Atlas 14 derived recurrence interval thresholds (for the southwestern United States) and static thresholds (for the northern and central Rockies) provide better correspondence. The 6-h QPE provides better correspondence with observed flash floods than 1-h QPE in all regions except the West Coast and southwestern United States. Exceedances of precipitation thresholds in forecasts from the operational High-Resolution Rapid Refresh (HRRR) generally do not correspond with observed flash flood events as well as QPE datasets, but they outperform QPE datasets in some regions of complex terrain and sparse observational coverage such as the southwestern United States. These results can provide context for forecasters seeking to identify potential flash flood events based on QPE or forecast-based exceedances of precipitation thresholds. Significance Statement Flash floods result from heavy rainfall, but it is difficult to know exactly how much rain will cause a flash flood in a particular location. Furthermore, different precipitation datasets can show very different amounts of precipitation, even from the same storm. This study examines how well different precipitation datasets and model forecasts, used by forecasters to warn the public of flash flooding, represent heavy rainfall leading to flash flooding around the United States. We found that different datasets have dramatically different numbers of heavy rainfall events and that high-resolution model forecasts of heavy rain correspond with observed flash flood events about as well as precipitation datasets based on rain gauge and radar in some regions of the country with few observations.

IoT based Smart Weather Monitoring with Flood and Earthquake Detection

Weather monitoring is essential for understanding the changing climatic conditions. Traditional weather monitoring system are slow, labor intensive, and outdated, often prone to error and inaccurate predictions. To overcome these challenges and provide an up-to date weather information, the IoT (Internet of Things) has been implemented in weather monitoring. This research presents an IoT-based weather monitoring system integrated with sensors to ensure accurate weather monitoring, including flood and earth quake detection. The collected data, such as temperature, humidity, atmospheric pressure, rainfall, flood levels, and seismic activity, is transmitted through IoT protocols to a centralized repository (Cloud) and visualized in the Blynk App. Flood sensors are strategically positioned in flood-prone areas, continuously monitoring water levels to detect abnormal rises indicative of potential floods. Similarly, seismic sensors detect ground vibrations and seismic activity, providing early alerts for earthquakes. The proposed system offers a comprehensive approach to monitor the weather, contributing to enhanced preparedness and mitigation strategies for natural disasters.

Investigating the flood potential of basins to locate the implementation of remedial operations in the Siah Khor watershed using VIKOR, TOPSIS, and HEC-HMS model (case study: Siah Khor watershed-Islamabad west)

Investigating the flood potential of basins to locate the implementation of remedial operations in the Siah Khor watershed using VIKOR, TOPSIS, and HEC-HMS model (case study: Siah Khor watershed-Islamabad west)