Assessing the role of SuDS in resilience enhancement of urban drainage system: A case study of Gurugram City, India

Guptha, Guru Chythanya; Swain, Sarat Chandra; Al‐Ansari, Nadhir; Taloor, Ajay Kumar; Dayal, Deen

doi:10.1016/j.uclim.2021.101075

Cited by 65 publications

(14 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Government of India's census department provided the state and district boundaries of Uttar Pradesh and Maharajganj District. For validating the acreage generated from SVM and RF, we conducted extensive ground-truthing and performed the accuracy assessment using the user's accuracy, producer's accuracy, and the kappa coefficient [36,37], and took about 120 ground-truthing points (randomly distributed, covering almost whole of the district) for this purpose. We used Garmin GPS explorer to record the GPS locations [38] (Figure 2).…”

Section: Datasetsmentioning

confidence: 99%

Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling

et al. 2022

View full text Add to dashboard Cite

Globally, estimating crop acreage and yield is one of the most critical issues that policy and decision makers need for assessing annual crop productivity and food supply. Nowadays, satellite remote sensing and geographic information system (GIS) can enable the estimation of these crop production parameters over large geographic areas. The present work aims to estimate the wheat (Triticum aestivum) acreage and yield of Maharajganj, Uttar Pradesh, India, using satellite-based data products and the Carnegie-Ames-Stanford Approach (CASA) model. Uttar Pradesh is the largest wheat-producing state in India, and this district is well known for its quality organic wheat. India is the leader in wheat grain export, and, hence, its monitoring of growth and yield is one of the top economic priorities of the country. For the calculation of wheat acreage, we performed supervised classification using the Random Forest (RF) and Support Vector Machine classifiers and compared their classification accuracy based on ground-truthing. We found that RF performed a significantly accurate acreage assessment (kappa coefficient 0.84) compared to SVM (0.68). The CASA model was then used to calculate the winter crop (Rabi, winter-sown, and summer harvested) wheat net primary productivity (NPP) in the study area for the 2020–2021 growth season using the RF-based acreage product. The model used for wheat NPP-yield conversion (CASA) showed 3100.27 to 5000.44 kg/ha over 148,866 ha of the total wheat area. The results showed that in the 2020–2021 growing season, all the districts of Uttar Pradesh had similar wheat growth trends. A total of 30 observational data points were used to verify the CASA model-based estimates of wheat yield. Field-based verification shows that the estimated yield correlates well with the observed yield (R2 = 0.554, RMSE = 3.36 Q/ha, MAE −0.56 t ha−1, and MRE = −4.61%). Such an accuracy for assessing regional wheat yield can prove to be one of the promising methods for calculating the whole region’s agricultural yield. The study concludes that RF classifier-based yield estimation has shown more accurate results and can meet the requirements of a regional-scale wheat grain yield estimation and, thus, can prove highly beneficial in policy and decision making.

show abstract

Section: Datasetsmentioning

confidence: 99%

Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Anticipating an increased frequency of precipitation extremes and associated flooding in Asia, Africa, and Southeast Asia in the coming decades, this challenge has prompted a debate on the necessary adaptations in flood management policies to address this evolving reality 8 . India, facing the highest flood-related fatalities among Asian countries 9 , 10 , encounters heightened vulnerability to disaster threats. This susceptibility is further exacerbated by the country's extensive geographic variability, making the development and implementation of a climate response strategy considerably more challenging 11 .…”

Section: Introductionmentioning

confidence: 99%

Understanding flash flooding in the Himalayan Region: a case study

Nagamani,

Mishra,

Meer

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The Himalayan region, characterized by its substantial topographical scale and elevation, exhibits vulnerability to flash floods and landslides induced by natural and anthropogenic influences. The study focuses on the Himalayan region, emphasizing the pivotal role of geographical and atmospheric parameters in flash flood occurrences. Specifically, the investigation delves into the intricate interactions between atmospheric and surface parameters to elucidate their collective contribution to flash flooding within the Nainital region of Uttarakhand in the Himalayan terrain. Pre-flood parameters, including total aerosol optical depth, cloud cover thickness, and total precipitable water vapor, were systematically analyzed, revealing a noteworthy correlation with flash flooding event transpiring on October 17th, 18th, and 19th, 2021. Which resulted in a huge loss of life and property in the study area. Contrasting the October 2021 heavy rainfall with the time series data (2000–2021), the historical pattern indicates flash flooding predominantly during June to September. The rare occurrence of October flash flooding suggests a potential shift in the area's precipitation pattern, possibly influenced by climate change. Robust statistical analyses, specifically employing non-parametric tests including the Autocorrelation function (ACF), Mann–Kendall (MK) test, Modified Mann–Kendall, and Sen's slope (q) estimator, were applied to discern extreme precipitation characteristics from 2000 to 201. The findings revealed a general non-significant increasing trend, except for July, which exhibited a non-significant decreasing trend. Moreover, the results elucidate the application of Meteosat-8 data and remote sensing applications to analyze flash flood dynamics. Furthermore, the research extensively explores the substantial roles played by pre and post-atmospheric parameters with geographic parameters in heavy rainfall events that resulted flash flooding, presenting a comprehensive discussion. The findings describe the role of real time remote sensing and satellite and underscore the need for comprehensive approaches to tackle flash flooding, including mitigation. The study also highlights the significance of monitoring weather patterns and rainfall trends to improve disaster preparedness and minimize the impact of flash floods in the Himalayan region.

show abstract

“…Additionally, urban drainage systems often face institutional and governance challenges, including fragmented responsibilities among multiple agencies, inadequate funding for maintenance and upgrades, and regulatory frameworks that may not prioritize holistic water management strategies (Mohammadiun et al, 2020). Furthermore, socio-economic disparities can exacerbate the impacts of urban flooding, as marginalized communities often bear the brunt of inadequate drainage infrastructure due to historical inequities in infrastructure investment and land-use planning (Guptha et al, 2022). Addressing these challenges requires a multifaceted approach that integrates innovative technologies, sustainable design principles, community engagement, and robust governance frameworks to build more resilient and equitable urban drainage systems capable of withstanding the complexities of the urban landscape in the 21st century (Bartos & Kerkez, 2021).…”

Section: Introductionmentioning

confidence: 99%

Innovative Approaches to Urban Drainage System : A Review

Muhammad Ade Kurnia Harahap,

Loso Judijanto,

Bucky Wibawa Karya Guna

et al. 2024

IJEET

View full text Add to dashboard Cite

Conventional urban drainage systems are often unable to cope with extreme rainfall and rapid urbanization, resulting in flooding and water pollution. Innovative approaches are needed to improve the performance and sustainability of urban drainage systems. This research aims to explore and analyze innovative approaches in urban drainage systems to improve efficiency, sustainability and resilience to climate change. This type of research is literature research. Data collection techniques are direct observation through journal databases and documentation. Data analysis in the research is qualitative data analysis. The results concluded a promising innovative approach to improve the performance and sustainability of urban drainage systems. Integrated application of this innovative approach can help create more resilient, sustainable and environmentally friendly urban drainage systems, contributing to healthier and climate change resilient cities. Further research is needed to evaluate the long-term effectiveness and scalability of this approach in various urban contexts.

show abstract

Assessing the role of SuDS in resilience enhancement of urban drainage system: A case study of Gurugram City, India

Cited by 65 publications

References 67 publications

Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling

Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling

Understanding flash flooding in the Himalayan Region: a case study

Innovative Approaches to Urban Drainage System : A Review

Contact Info

Product

Resources

About