Geospatial Assessment of Flood and Drought on the Chure Region of Western Nepal

2021 ◽  
Author(s):  
Swagat Attreya ◽  
Shankar Tripathi ◽  
Puja Sharma ◽  
Yojana Adhikari
Keyword(s):  
Relevant Information ◽  
Present Condition ◽  
Precipitation Trend ◽  
Climate Data ◽  
Drought Vulnerability ◽  
Temperature And Precipitation ◽  
Planning Decision ◽  
Major Floods ◽  
Socioeconomic Data ◽  
The City

Abstract Flood troubles people of central chure region and its vicinity almost every year. Besides, the area is also becoming susceptible to the drought currently. In that perspective, it is necessary to assess the vulnerability of floods and drought in the chure region. So this research focused on the assessment of flood and drought geospatially in Butwal city which was the major city and experience flood and drought every year. This study mainly records floods, calculates drought, finds their present condition, studies their trend, and determines drought and flood vulnerable areas within Butwal. Climate data, imagery, soil data, and socioeconomic data, and other relevant information were analyzed and extracted past drought conditions through Temperature and precipitation trend analysis and SPEI model. NDVI, NDDI, NDWI were used to find drought vulnerability status, and AHP and MCDA were used to find flood vulnerability. The city faced extreme drought in 2005, 2011, 2012, and 2013. And, Flood of 1970 and 1981 are the major floods. The area was frequently affected by floods in the past but no flood has been recorded after 2017. About 43% of the area was found high to very highly vulnerable to drought and 68% of the land was found vulnerable to extremely vulnerable to flood. So geospatial vulnerability assessment can enhance the planning decision and effectiveness of activities of the preventive measures by the stakeholders.

scholarly journals An Efficient Management Platform for Developing Smart Cities: Solution for Real-Time and Future Crowd Detection

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 765
Author(s):  
David Garcia-Retuerta ◽  
Pablo Chamoso ◽  
Guillermo Hernández ◽  
Agustín San Román Guzmán ◽  
Tan Yigitcanlar ◽  
...  
Keyword(s):  
Smart Cities ◽  
Open Data ◽  
Climate Data ◽  
Smart Meters ◽  
Security Systems ◽  
Crowd Management ◽  
Management Platform ◽  
Efficient Management ◽  
Pedestrian Traffic ◽  
The City

A smart city is an environment that uses innovative technologies to make networks and services more flexible, effective, and sustainable with the use of information, digital, and telecommunication technologies, improving the city’s operations for the benefit of its citizens. Most cities incorporate data acquisition elements from their own systems or those managed by subcontracted companies that can be used to optimise their resources: energy consumption, smart meters, lighting, irrigation water consumption, traffic data, camera images, waste collection, security systems, pollution meters, climate data, etc. The city-as-a-platform concept is becoming popular and it is increasingly evident that cities must have efficient management systems capable of deploying, for instance, IoT platforms, open data, etc., and of using artificial intelligence intensively. For many cities, data collection is not a problem, but managing and analysing data with the aim of optimising resources and improving the lives of citizens is. This article presents deepint.net, a platform for capturing, integrating, analysing, and creating dashboards, alert systems, optimisation models, etc. This article shows how deepint.net has been used to estimate pedestrian traffic on the streets of Melbourne (Australia) using the XGBoost algorithm. Given the current situation, it is advisable not to transit urban roads when overcrowded, thus, the model proposed in this paper (and implemented with deepint.net) facilitates the identification of areas with less pedestrian traffic. This use case is an example of an efficient crowd management system, implemented and operated via a platform that offers many possibilities for the management of the data collected in smart territories and cities.

scholarly journals Prediction of Maize Yield at the City Level in China Using Multi-Source Data

2021 ◽  
Vol 13 (1) ◽  
pp. 146
Author(s):  
Xinxin Chen ◽  
Lan Feng ◽  
Rui Yao ◽  
Xiaojun Wu ◽  
Jia Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Vegetation Index ◽  
Maize Yield ◽  
Machine Learning Algorithms ◽  
Shortwave Radiation ◽  
Growth Stages ◽  
Climate Data ◽  
Early Peak ◽  
The City

Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

Development of a flow-based planning support system based on open data for the City of Atlanta

2017 ◽  
Vol 46 (2) ◽  
pp. 207-224
Author(s):  
Ge Zhang ◽  
Wenwen Zhang ◽  
Subhrajit Guhathakurta ◽  
Nisha Botchwey
Keyword(s):  
Support System ◽  
Open Data ◽  
Community Planning ◽  
Relevant Information ◽  
Easy Access ◽  
Data Movement ◽  
Planning Support ◽  
Planning Support System ◽  
Analytical Tools ◽  
The City

Open data have come of age with many cities, states, and other jurisdictions joining the open data movement by offering relevant information about their communities for free and easy access to the public. Despite the growing volume of open data, their use has been limited in planning scholarship and practice. The bottleneck is often the format in which the data are available and the organization of such data, which may be difficult to incorporate in existing analytical tools. The overall goal of this research is to develop an open data-based community planning support system that can collect related open data, analyze the data for specific objectives, and visualize the results to improve usability. To accomplish this goal, this study undertakes three research tasks. First, it describes the current state of open data analysis efforts in the community planning field. Second, it examines the challenges analysts experience when using open data in planning analysis. Third, it develops a new flow-based planning support system for examining neighborhood quality of life and health for the City of Atlanta as a prototype, which addresses many of these open data challenges.

scholarly journals Exposure to Floods, Climate Change, and Poverty in Vietnam

2017 ◽  
Author(s):  
Mook Bangalore ◽  
Andrew Smith ◽  
Ted Veldkamp
Keyword(s):  
Climate Change ◽  
Flood Hazard ◽  
National Level ◽  
Climate Change Impacts ◽  
Ho Chi Minh City ◽  
Poor People ◽  
Socioeconomic Conditions ◽  
Socioeconomic Data ◽  
The City ◽  
Level Analysis

Abstract. With 70 percent of its population living in coastal areas and low-lying deltas, Vietnam is highly exposed to riverine and coastal flooding. This paper examines the exposure of the population and poor people in particular to current and future flooding in Vietnam and specifically in Ho Chi Minh City, using new high-resolution flood hazard maps and spatial socioeconomic data. The national-level analysis finds that a third of today’s population is already exposed to a flood, which occurs once every 25 years, assuming no protection. For the same return period flood under current socioeconomic conditions, climate change may increase the number exposed to 38 to 46 percent of the population. Climate change impacts can make frequent events as important as rare ones: the estimates suggest a 25-year flood under future conditions can expose more people than a 200-year flood under current conditions. Although poor districts are not found to be more exposed to floods at the national level, the city-level analysis of Ho Chi Minh City provides evidence that slum areas are highly exposed. The results of this paper show the benefits of investing today in flood risk management, and can provide guidance as to where future investments may be targeted.

scholarly journals Homogeneity Test and Correction of Daily Temperature and Precipitation Data (1978–2015) in North China

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Lingling Shen ◽  
Li Lu ◽  
Tianjie Hu ◽  
Runsheng Lin ◽  
Ji Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
North China ◽  
Maximum Temperature ◽  
Homogeneity Test ◽  
Climate Data ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
Daily Data ◽  
Before And After ◽  
Annual Average Temperature

Homogeneity of climate data is the basis for quantitative assessment of climate change. By using the MASH method, this work examined and corrected the homogeneity of the daily data including average, minimum, and maximum temperature and precipitation during 1978–2015 from 404/397 national meteorological stations in North China. Based on the meteorological station metadata, the results are analyzed and the differences before and after homogenization are compared. The results show that breakpoints are present pervasively in these temperature data. Most of them appeared after 2000. The stations with a host of breakpoints are mainly located in Beijing, Tianjin, and Hebei Province, where meteorological stations are densely distributed. The numbers of breakpoints in the daily precipitation series in North China during 1978–2015 also culminated in 2000. The reason for these breakpoints, called inhomogeneity, may be the large-scale replacement of meteorological instruments after 2000. After correction by the MASH method, the annual average temperature and minimum temperature decrease by 0.04°C and 0.06°C, respectively, while the maximum temperature increases by 0.01°C. The annual precipitation declines by 0.96 mm. The overall trends of temperature change before and after the correction are largely consistent, while the homogeneity of individual stations is significantly improved. Besides, due to the correction, the majority series of the precipitation are reduced and the correction amplitude is relatively large. During 1978–2015, the temperature in North China shows a rise trend, while the precipitation tends to decrease.

scholarly journals Temporal analysis of the relationship between dengue and meteorological variables in the city of Rio de Janeiro, Brazil, 2001-2009

2012 ◽  
Vol 28 (11) ◽  
pp. 2189-2197 ◽  
Author(s):  
Adriana Fagundes Gomes ◽  
Aline Araújo Nobre ◽  
Oswaldo Gonçalves Cruz
Keyword(s):  
Minimum Temperature ◽  
Rio De Janeiro ◽  
Linear Models ◽  
Negative Binomial ◽  
Critical Factor ◽  
Temporal Analysis ◽  
Temporal And Spatial Distribution ◽  
Temperature And Precipitation ◽  
The Relationship ◽  
The City

Dengue, a reemerging disease, is one of the most important viral diseases transmitted by mosquitoes. Climate is considered an important factor in the temporal and spatial distribution of vector-transmitted diseases. This study examined the effect of seasonal factors and the relationship between climatic variables and dengue risk in the city of Rio de Janeiro, Brazil, from 2001 to 2009. Generalized linear models were used, with Poisson and negative binomial distributions. The best fitted model was the one with "minimum temperature" and "precipitation", both lagged by one month, controlled for "year". In that model, a 1°C increase in a month's minimum temperature led to a 45% increase in dengue cases in the following month, while a 10-millimeter rise in precipitation led to a 6% increase in dengue cases in the following month. Dengue transmission involves many factors: although still not fully understood, climate is a critical factor, since it facilitates analysis of the risk of epidemics.

scholarly journals Determinants of SARS-CoV-2 transmission to guide vaccination strategy in a city

2020 ◽  
Author(s):  
Sarah C. Brüningk ◽  
Juliane Klatt ◽  
Madlen Stange ◽  
Alfredo Mari ◽  
Myrta Brunner ◽  
...  
Keyword(s):  
Vaccination Strategy ◽  
Population Level ◽  
Data Sets ◽  
Epidemic Dynamics ◽  
Modelling Analysis ◽  
City Population ◽  
Senior Population ◽  
Spatio Temporal ◽  
Socioeconomic Data ◽  
The City

Transmission chains within cities provide an important contribution to case burden and economic impact during the ongoing COVID-19 pandemic, and should be a major focus for preventive measures to achieve containment. Here, at very high spatio-temporal resolution, we analysed determinants of SARS-CoV-2 transmission in a medium-sized European city. We combined detailed epidemiological, mobility, and socioeconomic data-sets with whole genome sequencing during the first SARS-CoV-2 wave. Both phylogenetic clustering and compartmental modelling analysis were performed based on the dominating viral variant (B.1-C15324T; 60% of all cases). Here we show that transmissions on the city population level are driven by the socioeconomically weaker and highly mobile groups. Simulated vaccination scenarios showed that vaccination of a third of the population at 90% efficacy prioritising the latter groups would induce a stronger preventive effect compared to vaccinating exclusively senior population groups first. Our analysis accounts for both social interaction and mobility on the basis of molecularly related cases, thereby providing high confidence estimates of the underlying epidemic dynamics that may readily be translatable to other municipal areas.

scholarly journals Climate Change Influences Basidiome Emergence of Leaf-Cutting Ant Cultivars

2021 ◽  
Vol 7 (11) ◽  
pp. 912
Author(s):  
Rodolfo Bizarria ◽  
Pepijn W. Kooij ◽  
Andre Rodrigues
Keyword(s):  
Climate Change ◽  
Climate Change Scenarios ◽  
Nest Architecture ◽  
Historical Records ◽  
Climate Data ◽  
Temperature And Precipitation ◽  
Data Analyses ◽  
Symbiotic Mutualism ◽  
Leaf Cutting

Maintaining symbiosis homeostasis is essential for mutualistic partners. Leaf-cutting ants evolved a long-term symbiotic mutualism with fungal cultivars for nourishment while using vertical asexual transmission across generations. Despite the ants’ efforts to suppress fungal sexual reproduction, scattered occurrences of cultivar basidiomes have been reported. Here, we review the literature for basidiome occurrences and associated climate data. We hypothesized that more basidiome events could be expected in scenarios with an increase in temperature and precipitation. Our field observations and climate data analyses indeed suggest that Acromyrmex coronatus colonies are prone to basidiome occurrences in warmer and wetter seasons. Even though our study partly depended on historical records, occurrences have increased, correlating with climate change. A nest architecture with low (or even the lack of) insulation might be the cause of this phenomenon. The nature of basidiome occurrences in the A. coronatus–fungus mutualism can be useful to elucidate how resilient mutualistic symbioses are in light of climate change scenarios.

scholarly journals A FRAMEWORK IN DEVELOPING A CITIZEN-CENTERED SMART CITY MOBILE APPLICATION AS A PLATFORM FOR DIGITAL PARTICIPATION IN ILOILO CITY

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 153-160
Author(s):  
J. Domingo ◽  
K. A. Cabello ◽  
G. A. Rufino ◽  
L. Hilario ◽  
M. J. Villanueva-Jerez ◽  
...  
Keyword(s):  
Smart City ◽  
Mobile Application ◽  
Relevant Information ◽  
City Development ◽  
Collaborative Processes ◽  
Digital Services ◽  
Smart Mobility ◽  
The Government ◽  
Smart Governance ◽  
The City

Abstract. ICT is one of the technological enablers of a smart city which facilitates the developments in various sectors of the community such as in governance, transportation, education, safety, tourism, and communication. Development of smartphone applications have directly contributed to areas of smart living, smart people, smart governance, and smart mobility as it provides several features catering digital services in the city and flexible utilization of the city services. However, smart city development is not merely the creation of digital services for the citizens but instead involves a two-way communication between the government and citizen’s collaborative processes and digital participation. The purpose of this paper is to provide a framework for a mobile tool wherein people can easily access the most essential everyday city services and in the same manner provide the city authorities to gather relevant information from the application through review of literature and other relevant documents.

COOPERATIVE INFORMATION AGENTS FOR DIGITAL CITIES

2001 ◽  
Vol 10 (01n02) ◽  
pp. 197-215 ◽  
Author(s):  
SATOSHI OYAMA ◽  
KAORU HIRAMATSU ◽  
TORU ISHIDA
Keyword(s):  
Social Interaction ◽  
Relevant Information ◽  
Urban Life ◽  
The Internet ◽  
Digital City ◽  
Social Agents ◽  
Research Guidelines ◽  
Front End ◽  
Information Agents ◽  
The City

A digital city is a social information infrastructure for urban life (including shopping, business, transportation, education, welfare and so on). We started a project to develop a digital city for Kyoto based on the newest technologies including cooperative information agents. This paper presents an architecture for digital cities and shows the roles of agent interfaces in it. We propose two types of cooperative information agents as follows: (a) the front-end agents determine and refine users' uncertain goals, (b) the back-end agents extract and organize relevant information from the Internet, (c) Both types of agents opportunistically cooperate through a blackboard. We also show the research guidelines towards social agents in digital cities; the agent will foster social interaction among people who are living in/visiting the city.

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