Fire Outbreaks Research Articles

AI Enabled Fire-Detecting Drone Using Deep Learning Model

Wildfires pose significant threats to both human lives and the environment, necessitating advanced technological solutions for early detection and rapid response. This project aims to design, develop, and implement an autonomous drone system equipped with cutting-edge fire detection technology leveraging deep learning concepts. The system's primary objective is to swiftly identify and locate wildfires, enabling timely intervention and mitigation efforts to enhance public safety and environmental protection. The proposed drone system integrates state-of-the-art deep learning algorithms, allowing it to analyze real-time aerial imagery for signs of fire outbreaks with high accuracy and efficiency. Through the utilization of convolutional neural networks(CNNs) trained on vast datasets of wildfire images, the system can recognize distinct patterns and characteristics associated with flames, smoke, and heat sources amidst varying environmental conditions. Key components of the autonomous drone system include advanced sensors such as infrared cameras and multispectral imaging devices, facilitating enhanced detection capabilities across different spectrums. The integration of these sensors enables the system to detect wildfires even in challenging scenarios such as dense foliage or low visibility conditions. Upon detection of potential wildfire activity, the autonomous drone employs intelligent navigation algorithms to swiftly navigate to the identified location for closer inspection and assessment. Real-time data transmission capabilities enable seamless communication with ground-based emergency response teams, providing critical information for prompt decision-making and deployment of firefighting resources.

Autonomous Vehicle for Fire detecting and Extinguishing

— Our project presents an innovative solution for fire detection and extinguishing through an autonomous rover system. Leveraging fire sensors integrated with a node MCU, the system swiftly identifies fire outbreaks and transmits alerts to the rover via the ThingSpeak cloud platform. Upon receipt of the alert, the rover activates a buzzer for immediate local notification and dispatches alerts through a dedicated mobile application, ensuring rapid response from relevant personnel. Employing a line follower mechanism, the rover autonomously navigates towards the fire-affected area. Equipped with a water spraying system, the rover efficiently extinguishes flames, minimizing water wastage and enhancing firefighting effectiveness. This approach significantly reduces response time, enhancing safety across diverse environments. The system's ability to optimize resource utilization and automate firefighting procedures makes it adaptable to various settings, including industrial facilities, urban areas, and remote locations. Overall, our autonomous rover system represents a substantial advancement in firefighting technology, offering swift and efficient fire detection and extinguishing capabilities to mitigate fire hazards effectively. Keywords— NodeMCU, Rover, Flame Sensors, Servomotor, L293d motor drivers, Li-ion batteries

Development of an IoT-based firefighting drone for enhanced safety and efficiency in fire suppression

An uncontrolled fire poses severe threats to both humans and the environment, making firefighting a perilous and complex task. Traditional fire suppression methods are inefficient, costly, and without thorough testing, leading to delays in gaining control over fire outbreaks. This paper presents a novel firefighting drone aimed at mitigating risks to firefighters by extinguishing fires and providing real-time imaging, gas concentration and fire location data monitoring. The proposed intelligent quadcopter utilizes the Pixhawk PX4 microcontroller for precise control and the Pixhawk Telemetry system for data processing. The proposed device is constructed from an ultra-strength S500 Quadcopter frame, NodeMCU, Arduino Nano, various gas sensors, a servo motor to extinguish the fire and a camera to detect fire events in real time. Equipped with an FPV camera and a video transmitter, it transmits live video feed to the ground, enabling efficient navigation using the Flysky I6X controller. The intended position and height of the drone are controlled using an adaptive optimization technique known as fuzzy-based backstepping control. This article demonstrates the effectiveness of the device by collecting and analyzing gas emissions data from controlled burns of various materials. The drone successfully measured concentrations of CO, CO2, O3, SO2, and NO2 in affected areas, providing valuable insights for firefighting operations. Different levels of gases have been measured depending on the concentration from burning alcohol, clothes, plastic materials, paper, leaves, and so on. The novelty of this work lies in the development and comprehensive analysis of an IoT-based firefighting drone conducting extensive real-time experiments.

Fire Fighting Robot with SMS and Call Alert

In emergency situations such as fire outbreaks, timely intervention is crucial to minimize damage and ensure safety. Traditional fire-fighting methods often face challenges in reaching remote or hazardous locations promptly. To address this gap, we present a pioneering solution in the form of a "Fire Fighting Robot with SMS Alert." Our project integrates advanced hardware and software components to develop a robust system capable of autonomously detecting smoke and fire, and alerting the relevant authorities through SMS. The hardware design involves equipping a mobile platform with hit-sensing sensors for detecting impacts and smoke. Software development utilizes the Arduino IDE to program the Arduino UNO microcontroller, enabling efficient control and communication. Through meticulous implementation and rigorous testing, our project demonstrates promising results. The robot effectively senses fire and smoke, triggering immediate SMS alerts to designated recipients. Additionally, upon completion of its tasks, the robot initiates a call to the owner for further instructions, ensuring comprehensive response capabilities. This paper highlights the significance of our contribution to the field of emergency response systems, offering a practical and innovative solution to enhance safety and mitigate risks in fire incidents. The outcomes of our project provide valuable insights for future developments in Electronics and Telecommunication Engineering, with potential applications in various domains beyond fire-fighting scenarios. Key Words: Fire Fighting Robot, SMS Alert, Emergency Response, Robotics, Arduino, Hardware Design, Software Development, etc.

Improved the accuracy of IOT based LPG leakage detection system with early fire prediction and smart alert

Safety is of utmost importance in the realm of smart cities, smart homes, and industries. Among the various safety measures implemented in a smart city, the detection of gas leakage and the prevention of mass fires are crucial for safeguarding lives and valuable assets. This research paper introduces a novel system that not only detects gas leakage but also provides early predictions of fire outbreaks. Unlike the conventional gas leakage detectors available in the market, which only alert when the gas concentration reaches a high level, our system utilizes fuzzy logic, environmental temperature, and humidity to accurately detect gas concentration levels and predict early fire symptoms. By employing the Internet of Things (IoT) approach, this system promptly sends notifications and sensor readings to the relevant individuals, enabling them to take immediate action and prevent extensive damage.

Industrial Safety Application Using Ardunio (UNO R3)

Abstract: The aim of this work is to mitigate the destruction engendered due to fire outbreaks in industries like petroleum, chemicals, oil which leads to loss of life and property. It is very important to have some system that can keep the industry premises secure and also to notify the authorized people within the stipulated time in case of an emergency. The IOT industry The IOT industry protection system using Arduino is a system designed to protect industries from losses due to accidents using Internet of things. Gas leakages may lead to fires leading to huge industrial losses, also instant fire detection is needed in case of furnace blasts or other conditions. Also low lighting in industries may create improper work conditions increasing the probability of accidents.

Estimation of Forest Canopy Fuel Moisture Content in Dali Prefecture by Combining Vegetation Indices and Canopy Radiative Transfer Models from MODIS Data

Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated data from look-up tables. This study proposes a novel approach for estimating FMC by combining enhanced vegetation index (EVI) and normalized difference moisture index (NDMI). The method employs the PROSAIL + PROGeoSAIL two-layer coupled radiation transfer model to simulate the vegetation index, the water index, and the FMC value, targeting the prevalent double-layer structure in the study area’s vegetation distribution. Additionally, a look-up table is constructed through numerical analysis to investigate the relationships among vegetation indices, water indices, and FMC. The results reveal that the polynomial equations incorporating vegetation and water indices as independent variables exhibit a strong correlation with FMC. Utilizing the EVI–NDMI joint FMC estimation method enables the direct estimation of FMC. The collected samples from Dali were compared with the estimated values, revealing that the proposed method exhibits superior accuracy (R2 = 0.79) in comparison with conventional FMC estimation methods. In addition, we applied this method to estimate the FMC in the Chongqing region one week before the 2022 forest fire event, revealing a significant decreasing trend in regional FMC leading up to the fire outbreak, highlighting its effectiveness in facilitating pre-disaster warnings.

Factors influencing fire accidents in urban complexes: a combined DEMATEL and ISM study.

Fire outbreaks in urban complexes are a major safety concern worldwide. Therefore, this study aims to examine the critical factors that influence fire accidents and their interaction mechanisms in urban settings. A (software factors, hardware factors, environmental factors, parties and other factors, SHEL) model is developed to identify 15 risk factors in four categories affecting fire incidents in urban complexes. The Decision-making Trial and Evaluation Laboratory method (DEMATEL) and Interpretive Structural Modeling (ISM) are employed to identify the key factors and their interrelationships, using the evaluation metrics of degree of influence, affected degree, centrality, and hierarchical structure. The results show that lack of safety management rules and regulations (S13), poor security awareness (S1), and uncorrected hidden dangers (S11) are the top three critical factors. Based on the hierarchical structure and centrality values, eight critical paths with the highest impact on fires are identified; for instance, Path 39 (comprising, lack of safety management rules and regulations (S13) → lack of fire training and drills (S12) → insufficient security knowledge (S2) → poor security awareness (S1) → poor sense of security responsibility (S3) → uncorrected hidden danger (S11) → inadequate maintenance of fire-fighting facilities (S14) → Accident), which, among all disaster impact paths, has the highest centrality value of 21.8796 (out of a total of 15 factors and total centrality value of 42.9226; Path 39 involves seven factors, but its centrality value accounts for 50.97% of the total). Finally, based on the factor analysis results, suggestions for fire control measures are provided to prevent fire accidents and ensure the safety of people and property.

Automated Gas Line Shut Off Mechanism Using Solenoid Operated Valve and Gas Leak Detector (At the Event of Gas Leakage in PNG Line)

Gas leaks that cause deadly flames have become a major issue in homes and other places where domestic gas is handled and utilized. Gas leaks cause a variety of mishaps that result in financial damage as well as personal injuries and/or loss. The project’s goal is to create a system that detects gas leaks and notifies the subscriber through alarm and status display, as well as shutting off the gas supply valve as the main safety precaution. The turning off of the supply valve prevents further gas flow to the cooker, preventing a fire breakout caused by an attempt to ignite the cooker. The device, which functions more like a first- aid kit, utilizes a usually closed solenoid valve to shut off the gas valve before asking for assistance via visual display and loud alert to anyone in the vicinity. The system is clever in that it does not cause a loud nuisance by constantly sounding the alarm, but rather the siren stops beeping once the concentration of the gas in the atmosphere after leaking falls below the predetermined value and the valve is opened again for regular operations. This effort will reduce injuries and losses caused by explosions caused by gas leaks, as well as enhance the safety of people and property while utilizing home cooking gas.

DSRO based data annotation with improved EfficientNet for forest fire detection using image processing in IoT environment

<p>The increasing risk of forest fires demands sophisticated detection systems in order to mitigate the environment effectively. The technology under consideration enhances real-time monitoring and reaction by functioning inside an Internet of Things (IoT) architecture. Even though Artificial Intelligence (AI) algorithms have improved fire detection systems, they are quite expensive and energy-intensive due to their high computing needs. With the use of creative methods for data augmentation and optimization as well as a shared feature extraction module, this research study offers a thorough fire detection model using an improved EfficientNet that tackles these issues. Three technical components are creatively combined in the realm of forest fire detection by this study. The first stage is the use of diagonal swap of random (DSRO) data annotation, which makes use of spatial connections in the data to improve the model’s understanding of complex aspects that are essential for precisely identifying possible fire breakouts. By adding a shared feature extraction module across three functions, the second stage solves difficulties in feature extraction and target identification. This greatly increases the model’s performance in complicated forest scenes while reducing false positives and false negatives. The third and final stage focuses on improving the EfficientNet model’s capacity for accurate forest fire categorization. When taken as a whole, these technical components upon creative combination improve the existing technology in forest fire detection and provide a thorough and practical strategy for reducing environmental hazards. For the purpose of hyperparameter tuning in the EfficientNet for the classification of forest fires, an improved Harris Hawks optimization (HHO) is used. By using the Cauchy mutation approach with adaptive weight, HHO expands the search space, boosts population diversity, and improves overall exploration. By including the sine-cosine algorithm (SCA) into the optimization process, the likelihood of local extremum occurrences is decreased. The proposed strategy is successful compared to other existing models, as shown by the experimental findings that show an improvement of 5% in accuracy compared to the standard existing model, and an improvement of 2% compared to EfficientNet model in detecting forest fire.</p>

Development of a lightweight carbon fiber reinforced plastic water tank for fire trucks

In the event of a fire outbreak, it is very important to extinguish it before it becomes a blaze. Since the increase in firefighting water capacity for this purpose leads to an increase in the vehicle's load, it is necessary to reduce the weight of the tank. In this study, by changing the material from stainless steel to CFRP, the weight of the firefighting tank was reduced by 47.9% while increasing the capacity by 13.3%. The CFRP tank was manufactured by a vacuum infusion process using 12k filament carbon fiber and vinyl ester resin. After driving a firetruck equipped with a firefighting water tank for 5000 km, the dissolution test and the test to withstand the internal pressure performed on the tank corroborated that it can be equipped on firetrucks and used as a drinking water tank.

Development of a two-level parameter access control system

The necessity for safety and security-based systems has grown tremendously in our daily lives, and their significance has also increased notably. These systems are now essential for safeguarding people's homes, workplaces, valuable assets like gold stores, and countless other institutions. However, defense against the possibility of catastrophic incidents such as gas leaks, fire outbreaks, or burglary attempts emphasizes the significance of the same. The development of intelligent electronics and smart microcontrollers has led to a notable surge in technological advancements in recent times. This paper outlines the design of a security system used in managing a multi-level home using the versatile Atmega328 microcontroller board. This management system is designed to operate seamlessly accepting both remote and local control. The results achieved from the proposed system commendably display an admirable level of performance reliability, hence making it suffice for integration within Internet of Things (IoT) applications. This system stands out due to its ability to function independently of various points of failure, which makes it more dynamic and able to hold sustained service. The suggested system's affordability makes it a viable application for a cross-operation of upstream, midstream, and downstream settings, enhancing security and strengthening defenses against floods in different forms that could compromise the security architecture. Hence, the research described in this paper not only portrays a solution with strong security and safety aspects, but also establishes doors for its practical implementation across various circumstances.

Fire Risk Transfer and the Nigerian Economy (1999-2019)

This study was conducted to assess fire risk transfer and its impact on the Nigerian economy over the period 1999 to 2019. It was borne out of the incessant fire outbreaks in the country in recent times which has led to an increase in fire risk premiums in the Nigerian insurance industry. The specific objectives were to investigate the trend of fire risk premium and real gross domestic product (RGDP) and assess the impact of fire risk premium on RGDP in Nigeria. The ex-post facto research design was adopted and the population consisted of all insurance companies covering fire risk as of 2019. Data were sourced from Nigeria Insurance Digest, the Central Bank of Nigeria statistical bulletin and the National Bureau of Statistics covering the selected years. The dependent variable for the study was RGDP while fire risk premium, exchange rate and money supply were independent variables. The trend analysis revealed that fire risk premiums increased at a faster rate than RGDP. while the regression analysis indicated a positive significant relationship between fire risk premiums and RGDP, and negative relationships between exchange rate and RGDP, as well as between money supply and RGDP. It was recommended that fire risk transfer should be encouraged for individuals and organizations to enhance its continued contribution to economic growth.

Assessing The Risks of Oil Spills and Other Environmental Hazards in Bonny and Ogba/Egbema/Ndoni Local Government Area of Rivers State, Nigeria

This study assessed the emergency and risk drivers in oil exploration activities in Bonny Local Government Area, Rivers State, Nigeria. The descriptive research survey design was adopted for the study. Five research questions were raised to guide the study. The data for the study were collected using a five-point likert scale questionnaire containing a total of 25-items and utilized as a major instrument for the study. In all, three hundred and sixty (360) respondents made up the sample for the study. Arithmetic mean and standard deviation were the major statistical tools used for the data analysis. The results of the study showed that fire outbreak, destruction of crops and farmland, pollution of water bodies and marine lives, destruction of mangroves and increase in the cases of health threatening are the risks and environmental hazards associated with oil exploration in the study areas. The findings of the study also revealed that oil exploration greatly impact the economic, social and cultural wellbeing of the people as well as the health of the people and the environment of the study areas. Based on the above findings, the study recommended among other things, that oil exploration should be operated within stringent regulations designed to safeguard the welfare of employees, the environment, and nearby communities, oil companies operating in the areas should ensure regular monitoring and maintenance of their equipment and facilities so as to reduce the occurrence of pollution due to faulty oil and gas facilities, that oil companies should formulate and practice clearly defined emergency response strategies to manage potential incidents like oil spills or fires effectively.

Development of a Facenet Enhanced Secured Smart Office System

A secured smart office system is the one that is capable of recognizing and granting access to authorized persons only and manage the office appliances autonomously. The goals are access control, security and automation. Over the years, several studies have been carried out to meet these needs using RFID cards, access codes and biometrics resulting in weak security with long computational period. Switching of electrical appliances and smoke detection in case of fire outbreak were used but real time electrical appliances management that could prevent fire outbreak is yet to be achieved. This research focused its attention on the design and implementation of a smart office system that meet these needs. The system was developed using a raspberry pi 4 board. Ultrasonic sensor, camera, servo motor, relay, current and voltage sensors were interfaced with the raspberry pi for image capturing, opening the door, switching and power monitoring respectively. The system captures the image of an approaching person and process it for recognition using FaceNet; an open source model for face recognition. Information was transmitted via SIM800L GSM module as SMS to the administrator. The system shuts down the office electrical network once the supply voltage exceeds 220v ac or less than 161v ac, thus preventing any chance of fire outbreak due to irregular power supply. The accuracy of image recognition model was 93.13%. This research has shown a simple way of implementing an autonomous smart office system that is capable of providing adequate security, efficiency and convenience in offices.

Connecting an Offshore Dry Air Stratospheric Intrusion with the Outbreak of Soberanes Fire 2016

Abstract This study investigates the connection between the arrival of dry stratospheric air and the Soberanes Fire (2016). The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) and Goddard Earth Observing System Forward Processing model (GEOS-FP) are used for back-trajectories and offshore deep stratospheric intrusion in conjunction with the ignition and outbreak of the fire. The back-trajectory analysis indicates that most air reaching the vertical column was critically dry, exhibiting relative humidity values below 10%. As the fire ignited, dry air arrived from due west at heights of 1–3 km about 24 h prior. During the overnight fire growth, dry air arrived from the northwest to north-northwest at heights of 3.5–5.5 km 48–72 h prior. The synoptic and GEOS-FP analyses demonstrate offshore mid-to-low stratospheric intrusion. On 21 July 2016, an enclosed upper-level low approached the California–Oregon border along the northwesterly subtropical jet stream hours before the fire outbreak. The GEOS-FP results of potential vorticity, specific humidity, and ozone along the back-trajectories to the west and northwest of the fire suggest a stratospheric intrusion event into the mid-to-low troposphere at the back-trajectory start points, and vertical velocity indicates sinking motion. The specific humidity analyzed at the arrival time shows the transport of the abnormally dry air to the Soberanes Fire. Results suggest a connection between dry stratospheric air transported to the Soberanes Fire at ignition and overnight accelerated growth, supported by a dark bank in satellite water vapor imagery. The prediction of low-level transport of dry stratospheric air to the coastal communities could help to predict the occurrence of wildfire outbreaks, or periods of accelerated fire growth.

Exploring CO2 anomalies in Brazilian biomes combining OCO-2 & 3 data: Linkages to wildfires patterns

Climate change is a challenge to the global community and one of the causes is the increase of greenhouse gases (GHG) in the atmosphere, especially carbon dioxide (CO2). The major emission source of this gas into the atmosphere comes from the burning of fossil fuels and biomass burning, on the other hand, the main sink comes from biochemical processes such as photosynthesis. Thus, the observation of CO2 is a key point to understanding sources and sinks. In this context, The Orbiting Carbon Observatory 2 (OCO-2) and 3 (OCO-3), are a NASA dedicated mission to monitor the column-averaged dry-air mole fraction of carbon dioxide (XCO2) on a global scale. We combined the OCO-2 and OCO-3 observations to study the spatial distribution of XCO2 anomalies and how some of these anomalies are related to fire occurrence in the Brazilian Biomes during 2020 and 2021 considering two different seasons, Dry and Wet. The fire occurrence was obtained from Fire Information for Resource Management System (FIRMS) that provides the data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) product of active fires and thermal anomalies at Near-Real Time (MCD14DL, collection 6). The OCO-2/3 observations are affected by cloud formations in wet seasons, we observe that the dry period has more observations. The XCO2 anomaly values range from ∼ 7.0 ppm to −7.0 ppm and mostly positive anomalies occur in Amazon Biome, and this ecosystem has higher average values for all periods (∼0.9 ppm), compared to the other biomes. The fire occurrence was higher in dry periods, especially in 2020 when unprecedented fire outbreaks were registered in Brazil. The most affected biomes were Pantanal, Cerrado, and Amazon. XCO2 positive anomalies spatially agree with fire foci over some areas, and the correlation values between them ranged from 0.2 to 0.5 depending on the biome and season, and when considering observations with clouds the correlation is slightly higher. We point out for the first time the possibility of using OCO-2 and 3 combined, also, how positive XCO2 anomalies are related to fire occurrence in different ecosystems and periods, and the role of cloud detection in this relationship.

Examining the Role of Attitudinal Factors in Shaping Fire Prevention Intentions: A Study of Response Efficacy in Public Markets of Tanzania

The need of this study was to examine the moderating effects of attitude on the relationship between response efficacy and prevention intention of fire outbreaks in public markets. This research utilized quantitative research methods, surveying 384 participants consisting of traders and market management in the Dar es Salaam Region. A simple random sampling technique was employed to select respondents from 10 markets, and data analysis was conducted using a structural equation model. Findings show that attitudes moderate the relationship between perceived response efficacies towards prevention intention of fire outbreaks in public markets. The result shows that the effects of perceived response efficacy to attitudes (CR = 0.114, P = 0.035 and SRW = 0.480) and path leading from attitudes to prevention intention on outbreaks of fire in public markets was (CR = 0.2.653, P = 0.008 and SRW = 0.931). The study recommends that building attitudes on fire prevention should be a continuous process in public market in Tanzania.

EFFICIENT PLANT GAS LEAKAGE DETECTION AND MONITORING SYSTEM BASED ON IOT INTEGRATION

In industrial areas, fire breakouts often stem from gas leaks within the surrounding environment. These leaks could be caused by faulty apparatus, inadequate maintenance, or human mistake either during handling or storage of flammable gases. The presence of combustible gases poses a significant risk, as they can ignite easily and escalate into large-scale fires, endangering both personnel and property within the industrial site. This research employs a compact detection device capable of identifying combustible gases like propane, LPG Gas, Carbon Monoxide, butane, and other hazardous gases. The device is designed to identify these gases quickly and accurately, aiding in ensuring safety in various environments such as industrial sites, homes, and workplaces. The leakage detector includes NODEMCU, gas sensors, buzzer, and LCD system. When harmful gases surpass acceptable thresholds, the micro-controller initiates a alerting SMS using Blynk server. The prototype emits a sound alert and illuminates LCDs to indicate the leaking gas. Gas concentration datais sent to Thinkspeak server for easy monitoring by plant personnel.

Rural Household Perception of Drought Occurrence and Its Influence on Livelihood Strategy in Northeast Ethiopia

Globally, drought is occurring more frequently today, which is considerably affecting rural households’ agricultural productivity and socioeconomic development. Understanding households’ perceptions of drought is thus important for resilience‐building work because people act based on their views, and the resilience of people is tied to their views, knowledge, culture, and attitudes. This study analyses the rural households’ perceptions of drought occurrence and its influence on livelihood strategies in northeast Ethiopia. This was achieved through a mixed‐methods approach with a concurrent research design. The quantitative data were collected from 354 randomly selected household heads, whereas the qualitative data were collected from purposefully selected household head focus group discussions (FGDs) and key informants. The quantitative data were analyzed using descriptive statistics, whereas the qualitative data were analyzed using thematic data analysis techniques. The results show that climate variability and drought occurrence were perceived by the households as decreasing rainfall, increasing temperature, variations in rainfall onset and cessation, variations in heat waves and cold waves, heavy rainfall events, changes in sporadic rain, a lesser coverage of clouds, and adverse weather events. Within the last 25 years, households have perceived the number of extreme, severe, or moderate droughts to be increasing. Household perceptions of temperature changes match meteorological records, but their perceptions of rainfall changes do not. The drought hampered income sources, brought food shortages, and threatened family well‐being. It increased water stress, livestock morbidity and mortality, insect invasions, fire outbreaks, grazing resource depletion, abnormal migration, school dropout rates, and human health problems. The findings have important policy implications to mitigate drought risk, enhance drought adaptation, and develop pathways out of drought vulnerability, so it is worthwhile to harmonize the household perceptions with climate change policy.