National Disaster Management Authority Research Articles

E-Governance for Crisis Communication: A Study of Environmental Disaster in Pakistan

This study explores the state of social media as an e-governance platform which is currently in preliminary stage in crisis communication during three major climate disasters in Pakistan. The researchers aim to scrutinize the proactive or reactive approach of social media handles belonging to Disaster Management Authorities (DMA’s) of Pakistan during the natural calamities of 2022: During Murree Snowstorm, Karachi Monsoon Rainfall, and Floods. This study targets various authorities, namely the National Disaster Management Authority (NDMA) and the Provincial Disaster Management Authorities of Punjab, Sindh, KPK, and Baluchistan. Using quantitative content analysis techniques, the Social Mediated Crises Communication Model provided theoretical implications to determine which authority gathered more feedback likes, comments, and shares/retweets. The data is purposively sampled from the Twitter and Facebook handles of the National Disaster Management Authority and Provincial Disaster Management Authorities beginning from 1 January 2022 till 31 December 2022. The descriptive analysis on SPSS indicated that, most of the times, disaster management authorities adopted a reactive approach, with Twitter being a more active platform than Facebook. Additionally, contrary to other DMA’s, the Punjab Disaster Management Authority received more feedback on their social media platforms.

Development of a spatial framework for flash flood damage assessment and mitigation by coupling analytics of machine learning and household level survey data – A case study of rapid collaborative assessments and disbursement of public funds to the affectees of floods 2022, Punjab Pakistan

In August 2022, one of the most devastating floods in the history of Pakistan was triggered due to heavy and exceptionally high monsoon rainfall. It has affected thirty-six million people across the country and agriculture losses were recorded in the most productive Indus plains which aggravated the food insecurity in the country. This study focuses on the two districts of Punjab provinces includes DG Khan and Rajanpur, where huge flood damages were reported. As part of damage and loss assessment methodologies, GIS/RS based approach is developed in this study to estimate flood extent in DG Khan and Rajanpur. Field survey was conducted using android based devices with the help of military and government organizations such as PDMA, to verify and record flood damages. Information regarding damage includes property, livestock, people, crop and other damages with real-time images captured and stored via android app and flood data monitoring was visualized on a web-based dashboard. SAR data (Sentinel 1-A) was used to calculate and verify extent of the flood along with Sentinel 2-A dataset to extract Land cover classes. SAR flood maps were validated using the survey data retrieved during flood events. This data was compared with the Sentinel-1A dual polarised (VV & VH). The SAR data (Sentinel-1A) was also collected for the same day to extract the Flood Extent of August/September 2022 using Modified Normalized Difference Water Index (mNDWI). Hence, flood inundation map was prepared using polarisation. This examination is confirmed through the kappa coefficient (κ), which is 0.88 for VV polarisation and 0.86 for VH polarisation. In VV polarisation, PA and UA of ‘water’ are less than of all ‘other classes. Mauza level mapping was also conducted to identify the damaged mauzas in both floods affected districts to figure out damaged villages and find out damaged property with khasra or mauza name. GIS RS based and survey-based model was developed in this research, which replicated on two districts of Punjab to assess flood damage, it will help Pakistan government to always asses flood damages to crop, houses, livestock and other assets. This helped to communicate with the local governments and identify and rank high affected villages; so that priority shall be given as per intensity of the damages. An android application for flood survey was developed. The flood survey application provided facility to estimate the on-ground reality of each individual, property, crop and livestock damage due to flood. This application was mainly used for the assessment purpose that provides detail information with precise location and real visualization of the damage assets. Joint survey teams were constituted including members from the Provincial Disaster Management Authority, National Disaster Management Authority, Pakistan Army, Local Government and the Urban Unit. Each village classified as flood damages/affected was visited by the survey team through a proper survey plan. After all the flood data was recorded, now the next step was to build transparent system to distribute funds to the flood victims. For this purpose, Bank has developed dashboard to distribute funds to the affected by the Government of Punjab.

Earthquake Bond Pricing Model Involving the Inconstant Event Intensity and Maximum Strength

Traditional insurance’s earthquake contingency costs are insufficient for earthquake funding due to extreme differences from actual losses. The earthquake bond (EB) links insurance to capital market bonds, enabling higher and more sustainable earthquake funding, but challenges persist in pricing EBs. This paper presents zero-coupon and coupon-paying EB pricing models involving the inconstant event intensity and maximum strength of extreme earthquakes under the risk-neutral pricing measure. Focusing on extreme earthquakes simplifies the modeling and data processing time compared to considering infinite earthquake frequency occurring over a continuous time interval. The intensity is accommodated using the inhomogeneous Poisson process, while the maximum strength is modeled using extreme value theory (EVT). Furthermore, we conducted model experiments and variable sensitivity analyses on EB prices using earthquake data from Indonesia’s National Disaster Management Authority from 2008 to 2021. The sensitivity analysis results show that choosing inconstant intensity rather than a constant one implies significant EB price differences, and the maximum strength distribution based on EVT matches the data distribution. The presented model and its experiments can guide EB issuers in setting EB prices. Then, the variable sensitivities to EB prices can be used by investors to choose EB according to their risk tolerance.

Analysis of historical cases of liquefaction (sand boils) case study: Kelapa Gading Residence, Sigi Regency, Central Sulawesi Province

The Palu earthquake on September 28th, 2018, caused liquefaction in several locations in Central Sulawesi. According to The National Disaster Management Authority of Indonesia data, five prominent areas with massive impacts experienced liquefaction in flow liquefaction: Balaroa, Petobo, Jono Oge, Lolu, and Sibalaya. Meanwhile, the locations that experienced liquefaction in the form of sand boils needed to be recorded optimally. This research aims to see the parameters of soil layer behavior in areas that experience liquefaction in the form of sand boils. The study was conducted in Kelapa Gading housing, Sigi Regency, Central Sulawesi. Based on the initial survey, this location experienced sand boils in several areas. The research used the CPT-based liquefaction triggering procedure method and soil behavior index method. The result showed that the liquefaction potential was found at a depth of 2.5-3.3 meters for PGA 0.34 and -2.5-(-3.5) for PGA 0.68. When receiving cyclic load, the soil layer with an Ic value > 2.60 with a thickness of 1.6 m at the upper layer can withstand liquefaction with a thickness of 0.8 meters in the form of quicksand on the surface and only causes sand boils. Sand boils would not happen in the Kelapa Gading area if the excess pore water were redirected to discharge wells.

Role of Community in Disaster Management

The goal of this paper is to examine how communities may lessen the effects of disasters. Putting the community first during a crisis is crucial for its prevention, preparedness, mitigation, response, and recovery. Prior to the arrival of official assistance, it is the first line of defense to react to the crisis and assist fellow citizens. This exploratory study is based on information and secondary data that were obtained from pertinent sources. The study design is descriptive in nature, outlining the community's initiatives and the courses of action to be taken in order to escape the dire circumstances. In order to promote community-based disaster management, the National Disaster Management Authority of India introduced the Aapda Mitra Scheme in 2016. Children, people with disabilities and members of minority groups are among the vulnerable populations whose needs are met by CBDM, which is implemented in a number of areas including education, health, water, sanitation, transportation, and communication.

Satellite-based landslide distribution mapping with the adoption of deep learning approach in the Kuantan River Basin, Pahang

Landslides are one of the major geological phenomena that is widespread across the globe and have caused destructive outcomes to human life and the overall economic system. Tedious work is required to conventionally collect all evidence of multiple sizes of landslide occurrences in such a huge, developing city, including the Kuantan River Basin (KRB). In fact, landslides are difficult to identify in remote areas, such as in thick and mountainous areas, if no aerial devices or sensor technology is provided at the incident area. Ironically, the landslide distribution map is a useful tool that helps in staging the landslide mitigation plan for landslide-prone areas. Thus, the objectives of this study are (i) to identify landslide events using deep learning and vegetation index approaches on optical satellite data; and (ii) to develop landslide distribution mapping in KRB using the best approach. Remotely sensed optical images of Landsat 8 OLI and Worldview-2 were used to map the landslide distribution and study the spectral pattern of the landslide area. Normalized Difference Vegetation Index (NDVI) were generated for two consecutive years, which is from the year 2022 to 2023. Spectral bands in red and infrared are used to generate the NDVI for visual interpretation of landslide occurrences. The deep learning based on Convolutional Neural Network (CNN) model were used for the pixel classification process. The main output of this study would be a landslide distribution map for the KRB area with high accuracy. The result has also been verified using drone monitoring at the incident sites, which was able to improve landslide detection in tropical environments. Landslide distribution maps accuracy was measured by using the ROC-AUC method, the map accuracy is 88.9%. This map should help the government and private sector plan for the city’s urban development and provide proper planning for geohazard mitigation. An accurate landslide distribution map could be a source of reference for the National Disaster Management Authority (NADMA) for a quick rescue during emergency.

SARS-COV-2: THE 2019 NOVEL CORONAVIRUS

The Nationwide Lockdown has been a major part of the planning of government to overcome the pandemic COVID -19 which includes complete shutdown of businesses, disrupted food supply chains, extended limits of time slots and terminated contacts. This pandemic leads to unorganized economic sector with loss at its peak. There has been a total shutdown in all the areas, leaving individuals who are most adversely affected with few or no legal options. On the other hand, the lockdown has assisted in reducing the disease's transmission throughout the society while allowing for a legal and legislative examination of this action that has thus far escaped scrutiny. In the seventh week of the lockdown, it was crucial and appropriate that we assessed the overall legality of the lockdown as well as all the actions the Indian government has taken to combat COVID-19. According the Laws governing this lockdown, it is well known that the State governments and district authorities have implemented a lockdown in accordance with instructions from the Union Ministry of Home Affairs under the Disaster Management Act of 2005whose intentions were “to provide for the effective management of disasters and for the matters which are connected there with incidental thereto”. On March 24, 2020, the NDMA (National Disaster Management Authority) and NEA (National Executive Committee) which were established at the direction of the Prime Minister and chaired by the Home Secretary respectively, they issued orders and guidelines to Union Ministers. To prevent the spread of COVID-19, state governments and relevant authorities should take appropriate, lawful action. They should also follow any instructions that specify which businesses should be closed and which services should be discontinued during this lockdown period. The government issued instructions to impose “social distancing” and the isolation measures to remain “under strict home quarantine” and people to “stay at home and come out only when it is necessary for basic necessities and essential services while following social distancing norms”.

ANALISIS PENGARUH TUNJANGAN KINERJA, PELATIHAN DAN MOTIVASI KERJA TERHADAP KINERJA PEGAWAI PADA DEPUTI BIDANG REHABILITASI DAN REKONSTRUKSI, BADAN NASIONAL PENANGGULANGAN BENCANA

This research was conducted at the Deputy for Rehabilitation and Reconstruction, National Disaster Management Agency. This research aims to examine and analyze the influence of performance allowance, training and work motivation on employee performance at the Deputy for Rehabilitation and Reconstruction, National Disaster Management Authority. t statistic value for performance allowances is 2.026 > t table 1.997, so the conclusion is that performance allowances have an effect on improving employee performance. t statistic value for training is 1.059 < t table 1.997, the conclusion is that training has no effect on improving employee performance. t statistic value for work motivation is 5.240 > t table 1.997, so the conclusion is that work motivation has an effect on increasing employee performance. F statistic value is 24.050 > F table 2.742, the conclusion is performance benefits, training and work motivation simultaneously (together) have an effect on improving employee performance, these three variables can be used to predict employee performance. R Square (coefficient of determination) is 0.522, this condition shows that the effect of performance allowances, training and work motivation in improving employee performance is 52.2 percent, the remaining 47.8 percent is influenced by other factors not tested in this research.

Disaster management ontology- an ontological approach to disaster management automation

The geographical location of any region, as well as large-scale environmental changes caused by a variety of factors, invite a wide range of disasters. Floods, droughts, earthquakes, cyclones, landslides, tornadoes, and cloudbursts are all common natural disasters that destroy property and kill people. On average, 0.1% of the total deaths globally in the past decade have been due to natural disasters. The National Disaster Management Authority (NDMA), a branch of the Ministry of Home Affairs, plays an important role in disaster management in India by taking responsibility for risk mitigation, response, and recovery from all natural and man-made disasters. This article presents an ontology-based disaster management framework based on the NDMA’s responsibility matrix. This ontological base framework is named as Disaster Management Ontology (DMO). It aids in task distribution among necessary authorities at various stages of a disaster, as well as a knowledge-driven decision support system for financial assistance to victims. In the proposed DMO, ontology has been used to integrate knowledge as well as a working platform for reasoners, and the Decision Support System (DSS) ruleset is written in Semantic Web Rule Language (SWRL), which is based on the First Order Logic (FOL) concept. In addition, OntoGraph, a class view of taxonomy, is used to make taxonomy more interactive for users.

Detection of flood inundation in serayu watershed using landsat-8 and sentinel-2 satellite imagery

Floods are natural disasters that occur routinely almost every year in various regions in Indonesia, especially on the island of Java. According to the National Disaster Management Authority (BNPB), in 2001-2021 there were 5542 floods in Java. Determining the area of flood inundation is very important to know the potential and risk of the impact of flooding in an area. The purpose of this study is to develop a method of detecting flood inundation areas based on Landsat-8 and Sentinel-2 satellite imagery data using the Normalized Difference Water Index (NDWI) parameter. The Maximum Likelihood Classification (MLC) method was chosen to classify inundated areas and non-flooded areas. The Landsat-8 and Sentinel-2 satellite images were selected during the wet months that usually rain, January, February, March, November and December in 2020. The results show that the Landsat-8 satellite can detect water inundation better than the Sentinel-2 satellite. The results are influenced by the difference in recording time between the two satellites, the presence of clouds that cover the research location, and Landsat-8 imagery data that does not cover the entire location area.

A Review of Spatial Variations of Multiple Natural Hazards and Risk Management Strategies in Pakistan

Natural hazards are dynamic and unpredictable events that are a continuous threat to global socio-economic development. Humans’ reactions to these catastrophes are influenced by their proximity to the hazards and their ability to anticipate, resist, cope with, and recover from their consequences. Due to climatic changes, the risk of multiple natural hazards is expected to increase in several regions of Pakistan. There is a pressing need to understand the spatial discrepancies of natural hazards due to climate change and identifying the regions that require special measures to increase resilience, achieve adaptation, and sustainable development goals. This paper synthesizes the related literature to understand spatial variations of natural hazards due to climate changes across Pakistan. The Emergency Events Database (EM-DAT), National Aeronautics and Space Administration Global Landslide Catalog (NASA-GLC), National Disaster Management Authority (NDMA), and Pakistan Meteorological Department (PMD) are utilized to analyze spatial discrepancies and vulnerabilities to natural hazards. This study unveils that Pakistan’s current risk analysis and management strategies seem to be obsolete compared to global trends. Because of spatial variations of hazards, most research work on hazard risk assessments and risk management focuses on a single hazard, neglecting the co-occurrence impact of different natural hazards. Very limited studies are included in comprehensive multi-hazard risk strategies. Therefore, in Pakistan, risk management would require integrated multi-hazard risk assessment approaches to detect, analyze, measure, and evaluate various natural hazards, their effects, and interconnections. Moreover, the Pakistan governmental institutes dealing with natural hazards should focus on pre-disaster mitigation and resilience techniques instead of investing only in post-disaster relief activities.

Brewing epidemic of communicable diseases after massive flooding in Pakistan.

Brewing epidemic of communicable diseases after massive flooding in Pakistan.

The Use of Sentinel-1 in Response to Sequences of Natural Disasters in Indonesia: The South Kalimantan Floods and The Mamuju-Majene Earthquake

A series of floods occurred in the early year of 2021 are the first major South Kalimantan flood in the past fifty years. The major regions which were affected by them are Banjarmasin, Central Hulu Sungai Regency, and Tanah Laut. There are 211,670 people severely affected by flooding and some of them need to be evacuated to higher zones. Not only floods as a concern of Indonesia National Disaster Management Authority (BNPB) but also earthquake that struck the Mamuju and Majene Regency, in the province of West Sulawesi on 14 and 15 January 2021. Damage was reported in 4 regencies which more than 6,000 buildings were destroyed, more than 3,300 were injured, and 105 people were confirmed dead due to this earthquake. Quick monitoring of two events must be conducted in order to support spatial information for BNPB mitigating the disasters. A geodetic ground measurement could be the best result in terms of accuracy and a large-scale map. However, a lack of pre-event measurement and a difficulty to access the primary roads and bridges in the affected regions makes ground survey complicated to be conducted. An alternative solution is using a space-borne remote sensing which its sensor regularly passes the affected regions. We used Synthetic Aperture Radar (SAR) data from Sentinel-1 to regain spatial information before and after disasters in the provinces of South Kalimantan and West Sulawesi such as inundation and deformation areas, respectively. Using an interval of 12 days and the SAR backscatter analysis, we found inundated areas mostly located near to the Barito River and in sub-districts along its tributaries for a case study of South Kalimantan floods. Meanwhile, we detected deformation about -5 cm in the southern part of West Sulawesi using the Differential Interferometric SAR (DInSAR) technique. Due to high decorrelation with the range of coherence values between 0 and 0.2 in the epicenter of earthquake area, the generated interferograms can not confirm the surface displacement caused by the sequence series of foreshocks, mainshocks, and aftershocks in this area.

FLOODS HAVE POSED SERIOUS HEALTH AND ECONOMIC CHALLENGES IN AFFECTED AREAS OF PAKISTAN

Climate changes can contribute to flooding. Adverse effects of flooding include loss of life, property, and infrastructure damage, road closures, erosion and landslide risks, crop destruction and livestock loss, health risks due to water contamination, housing displacement and economic impacts. Exposure to contaminated floodwater can cause wound infection, skin rash, gastrointestinal disorders, and tetanus etc. Around 33 million people in Pakistan were affected by devastating floods that killed over 1,700 people. One-third of Pakistan was inundated and 116 out of 154 districts of Pakistan have been affected by floods. Frail, hungry, children are fighting a losing battle against severe acute malnutrition, diarrhoea, malaria, dengue fever, typhoid, acute respiratory infections, and painful skin conditions. The longer the crisis continues, the greater the risk to children’s mental health. According to National Disaster Management Authority (NDMA), 2,000 health facilities, representing 10% of all health facilities in the country have been damaged. Over 8 million people in flood-affected districts are in urgent need of health assistance. Less than 50% of all people needing health services are currently able to access health facilities. Cases of Severe Acute Malnutrition (SAM) with complications are on the rise. Floods have disrupted immunization campaigns. United Nation’s revised Floods Response Plan was released on 4 Oct and seeks $816 million to provide humanitarian assistance to 9.5 million people. As of 16 Dec 2022, donors had funded only 26.7% of the $816 million requested. Lack of resources will definitely worsen health crisis during winter season. Pak J Physiol 2022;18(4):1–2

The floods of 2022: Economic and health crisis hits Pakistan.

Pakistan is currently going through the worst-ever recorded flood crisis. The 2022 floods have created unprecedented destruction, especially in the coastal regions of the country. Almost one-third of Pakistan has drowned, merely as a consequence of climate change which is impacting many countries around the globe, with the South Asian countries at the greatest risk due to their proximity to the equator. The National Disaster Management Authority (NDMA) has reported millions of people to be affected by the catastrophic floods. The country is also going through a major economic crisis due to livestock and agriculture being destroyed, which has led to severe food shortages resulting in the flood victims being vulnerable to malnutrition as well. Health care has also been severely affected, with a serious outbreak of waterborne diseases including diarrhea, dengue fever, malaria. Thousands of pregnant women are unable to access maternity services and menstrual hygiene products are also unavailable in flood-stricken areas. The Government of Pakistan should address this urgent matter as a priority and necessary steps should be taken to try and contain this catastrophe.

Addressing the human costs and consequences of the Pakistan flood disaster

Addressing the human costs and consequences of the Pakistan flood disaster

Socio-Cultural Intervention As Disaster Mitigation In Indonesia

The Republic of Indonesia needs a mitigation system due to considering how vulnerable the archipelago to disasters. Compiled from The National Disaster Management Authority (BNPB) data in 2021, Indonesia will have to deal with 1,441 natural disasters in the period from January to June. This situation gets worse because until now Covid-19 is still high in various corners of the country. Various efforts in the context of handling the disasters have encountered obstacles that could slow down the recovery of the community. Indonesia's disaster management is still absent in playing its role. Therefore, it is necessary to optimize various aspects, including social and cultural capital as an important force. Natural disaster mitigation needs to be done by basing the wealth of cultural heritage, traditions and local wisdom. In addition to saving budget, mitigation models that use social power like this are more responsive so that prevention and recovery can be carried out earlier. This study aims to analyze and identify socio- cultural intervention as a disaster mitigation. This study uses a qualitative method with an explanatory descriptive approach. The data collection was done by using observation, interview, and documentation techniques. The result of this study indicates that disaster management in Indonesia is still spontaneous and does not optimize mitigation interventions in social and cultural aspects such as social networks and local wisdom values. The socio-cultural intervention system is very realistic because local wisdom exists in every community in the archipelago. This is good for the community to respond to disaster problems.

Finance Commissions and Disaster Risk Funding: A Paradigm Shift in Methodology by Fifteenth Finance Commission

Disaster risk financing has been in practice since the Second Finance Commission in the form of a Margin Money Scheme. Later, it was replaced with a Calamity Relief Fund and a National Calamity Contingency Fund (NCCF)/National Disaster Relief Fund (NDRF). These funds were based on expenditure-based funding, from which a state is provided relief funds based on its past expenditure. There have been a lot of discrepancies as states like Uttarakhand, with high hazard risk vulnerability, received ₹1,158 crore, and Haryana, comparatively a less hazard risk vulnerability state, received ₹1,699 crore for 2015–2020 from the State Disaster Response Fund (SDRF). States and other agencies like National Disaster Management Authority (NDMA) have been demanding, for replacing this expenditure-based funding with a state-specific hazard/disaster risk vulnerability for a long time. The Fifteenth Finance Commission addressed this long-standing demand by incorporating an innovative methodology for disaster risk funding. It made a slight departure from the past method and included area, population and disaster risk index for calculating a state’s share in disaster risk funding. This article analytically examines the past and present methodologies of disaster risk funding by applying quantitative and qualitative research methods.

MODEL SUPPORT VEKTOR MACHINE (SVM) BERDASARKAN PARAMETER WINDOWS UNTUK PREDIKSI KEKUATAN GEMPA BUMI

Earthquakes are a type of natural disaster that currently cannot be predicted. Predicting the value of earthquake magnitude for related parties such as government and National Disaster Management Authority is very important. Furthermore, the results of earthquake predictions by several parties are used as indicators in post-earthquake response in minimizing the risks that will occur. Several studies have applied machine learning methods to predict earthquakes such as deep neural networks and parallel Support Vector Regression. In this article, we propose a data mining method using the Support Vector Machine (SVM) algorithm accompanied by the optimization of the windowing parameter value in the model that is applied to predict the value of the earthquake magnitude. Based on its advantages, the SVM model was chosen because it has been applicable in time series data processing. In the experimental stage process, parameter settings are first carried out, namely setting the kernel type, sampling type, and number of windowing to optimize the level of accuracy of the resulting model. The results showed that the best model with the smallest Root Mean Square Error (RMSE) was 0.712.

Efficacy of Flood relief measures - 2010: A case study of district Layyah, Punjab-Pakistan

In 2010, Pakistan experienced a massive flood that took the lives of 1985 individuals, in addition to causing huge damage to livestock, shelters, and domestic goods. Multiple local and international organizations extended support to the victims of the 2010 Pakistan flood. Beside relief support, media highly criticized their relief activities. The study was conducted in the district of Layyah, in the Punjab province of Pakistan. The study primarily aims at determining aspects of the flood relating to: ground situation and extent of damages, quality of services provided by the government and non-government organizations (NGOs). The study gathers data and analysis of data was carried out with simple statistical techniques. Ground situation in the country appeared alarming: flood affected 160,000 square kilometer of land, damaged to crop approached US$ 0ne billion, and affected around 20 million people. In the study area 40 % of livestock could not survive, 94.5 % houses were completed abolished and 38.7 % of domestic goods were heavily damaged. District government role was appreciated by 66.4 % of the respondents. Around 50 % of the respondents reported against the performance of the Provincial Disaster Management Authority and National Disaster Management Authority. 96.2 % of the respondents recognized the role of NGOs while respondents suggested working of NGOs through district governments.