AHP and TOPSIS based flood risk assessment- a case study of the Navsari City, Gujarat, India

In the face of climate change, mapping flood vulnerability has become important. This is because it helps to identify vulnerable people in particular communities and provides a better understanding of the causes of their vulnerability. The objective of this scoping literature review paper is to describe the components, elements, criteria selection methods, scale and settings, data, analytical approaches, the purpose of the maps, sensitivity analysis and accuracy assessment techniques used for mapping flood vulnerability in developing countries. A search over the last decade was conducted in Web of Science and Scopus using the keywords: “flood vulnerability” OR “spatial modelling” AND “developing country”. The results reveal that flood vulnerability mapping in developing countries was integrated and multi-disciplinary and the mapping was mainly done at a community level or local level. Indicator-based multi-criteria analysis approaches and Geographical Information Systems (GIS) were commonly used in mapping flood vulnerability. The selection of criteria or indicators was mostly based on the existing literature and expert knowledge. Stakeholder participation and use of indigenous knowledge in producing the flood vulnerability maps was limited in the reviewed studies. Sensitivity analysis was hardly done and the lack of reliable, updated and high-resolution data adversely affected the validation and usability of the flood vulnerability maps. Since a lot of people especially in urban areas are expected to be vulnerable to flood hazards due to climate change, validated and accurate flood vulnerability maps are crucial in supporting decision making and strategic interventions aimed are reducing flood risks. 

Examining flood vulnerability mapping approaches in developing countries: A scoping review

Global natural and manmade events are exposing the fragility of the tourism industry and its impact on the global economy. Prior to the COVID-19 pandemic, tourism contributed 10.3% to the global GDP and employed 333 million people but saw a significant decline due to the pandemic. Sustainable and smart tourism requires collaboration from all stakeholders and a comprehensive understanding of global and local issues to drive responsible and innovative growth in the sector. This paper presents an approach for leveraging big data and deep learning to discover holistic, multi-perspective (e.g., local, cultural, national, and international), and objective information on a subject. Specifically, we develop a machine learning pipeline to extract parameters from the academic literature and public opinions on Twitter, providing a unique and comprehensive view of the industry from both academic and public perspectives. The academic-view dataset was created from the Scopus database and contains 156,759 research articles from 2000 to 2022, which were modelled to identify 33 distinct parameters in 4 categories: Tourism Types, Planning, Challenges, and Media and Technologies. A Twitter dataset of 485,813 tweets was collected over 18 months from March 2021 to August 2022 to showcase the public perception of tourism in Saudi Arabia, which was modelled to reveal 13 parameters categorized into two broader sets: Tourist Attractions and Tourism Services. The paper also presents a comprehensive knowledge structure and literature review of the tourism sector based on over 250 research articles. Discovering system parameters are required to embed autonomous capabilities in systems and for decision-making and problem-solving during system design and operations. The work presented in this paper has significant theoretical and practical implications in that it improves AI-based information discovery by extending the use of scientific literature, Twitter, and other sources for autonomous, holistic, dynamic optimizations of systems, promoting novel research in the tourism sector and contributing to the development of smart and sustainable societies.

https://www.mdpi.com/2071-1050/15/5/4166/pdf?version=1678260836

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

Flood hydrodynamic study is an important technique to assess the flood hazards in a flood-prone area. Flood inundation, flood arrival time, flood velocity, and water surface elevation are important parameters to quantify the performance of the hydrodynamic model for flood assessment; however, the sensitivity of these parameters is purely dependent on flood boundary condition, Digital Elevation Model (DEM) resolution, roughness coefficient, and 2D cell size. Therefore, to identify the efficacy of boundary conditions on flood hydrodynamic studies, a present case is performed on the Purna River, Gujarat, India. The Purna River (urban coastal) flood is considered for modeling. The downstream boundary condition is replaced with tidal waves and identify the efficacy in flood parameters in the 2D hydrodynamic condition. The HEC-RAS model is simulated with and without tidal boundary conditions. The model is validated with observed flood heights. The calibration and validation have been performed to check the adequacy and consistency of the model. The model without tide effect unveiled a high R2 value (0.9707) and low RMSE and STD values (0.7045, 0.7251) respectively, demonstrating that the model without tide effect is more efficacious and performs better. The results are elaborated with the significance of tide a flood plain. The critical flood inundation assessment with tidal boundary is an important case study to understand the role of the tidal waves on 2D floods for coastal urban areas. This study could be useful for the decision maker to prepare an efficient flood assessment and resiliency plan for coastal urban flooding.

Identifying the efficacy of tidal waves on flood assessment study — a case of coastal urban flooding

El Nino dan La Nina adalah merupakan dinamika atmosfer dan laut yang mempengaruhi cuaca di sekitar laut Pasifik. selain fenomena menghangatnya suhu permukaan laut, terjadi pula fenomena sebaliknya yaitu mendinginnya suhu permukaan laut akibat menguatnya upwelling. El Nino dan La Nina adalah merupakan dinamika atmosfer dan laut yang mempengaruhi cuaca di sekitar laut Pasifik. Ketika El nino berlangsung, musim kemarau menjadi sangat kering serta permulaan musim hujan yang terlambat. Sedangkan ketika La nina, musim penghujan akan tiba lebih awal dari biasanya. Kata Kunci: El nino, La nina, Dampak Kehidupan, Indonesia

El Nino, La Nina dan Dampaknya Terhadap Kehidupan di Indonesia

The drivers for coastal flooding may vary from extremely high intensity and persistent rainfall, morphological factors of the coastal area, to extreme waves from the ocean. This means that the flood vulnerability of a coastal area does not solely depend on a single driver but can be a combination with others. A national standard for coastal flooding based on rainfall drivers has been developed. As an evaluation, this study aimed to develop a method for coastal flood-prone mapping by combining rainfall with tidal waves. The steps included the assessment of the coastal flood-prone areas driven by rainfall (CFR) and the coastal flood-prone areas by combined drivers (CFC), which was developed by employing the analytic hierarchy process (AHP), spatial-overlaid, weighted-scored, and logical tests. The coastal area of Mataram City on the Island of Lombok in Indonesia was selected as the study area, since it is frequently affected by flooding. The findings determined the essentiality of the CFC method for identifying flood vulnerability areas. Thus, the minimum standard for CFC parameters can be defined with climatic and land characteristic factors. Further, the findings also identified the need for expert judgment in the development of the CFC weighted score-based method.

https://www.mdpi.com/2220-9964/9/9/525/pdf

Urban Coastal Flood-Prone Mapping under the Combined Impact of Tidal Wave and Heavy Rainfall: A Proposal to the Existing National Standard

Sayung Subdistrict, Demak Regency is a low-lying coastal area prone to coastal flooding. This coastal area’s flood susceptibility is not influenced by a single factor, but by the combined effect of excessive rainfall, the morphological characteristics of coastal areas, and tidal waves. As a reason, the objectives of this study were to (1) map coastal flood-prone areas using a combination of heavy rainfall and tidal waves; (2) determined the extent of inundation; and (3) provide feedback on potential flood-affected area management. The findings indicate inundation has increased by 2.4% per year. Therefore, the evaluation of the mitigation management has to be considered either on local communities’ perspective, regional and national government planning and non-government implementation. Local communities, with or without the assistance of NGO and regional governments, adapt their mitigation strategies to the changing environment, whether through physical, economical, or social approaches. Meanwhile, the regional and national governments’ management will be incorporated into the detailed spatial planning.

Management of coastal flood impact in rural area (Case study in Sayung District)

Benthic Habitat Mapping using Sentinel 2A: A preliminary Study in Image Classification Approach in An Absence of Training Data

The Impact of Flooding on Settlement Along the Jangkok River Mataram, Indonesia

The availability of agricultural land, especially paddy fields, is an inseparable part of efforts to maintain national food security and engage SDGs’ second goal. Changes in land cover/land use can occur due to social, political, economic, cultural, natural, and technological factors. Geographic Information Systems (GIS) operate as a tool to comprehend the changes related to the driver’s factors. The purpose of this study was to examine the spatial dynamics of paddy land cover in the Banyuasin Regency for more than 2 decades using the Markov Chain approach and to analyze the inhibiting and supporting factors in developing paddy farming in the region. The inhibiting factor discussed in this study is the potentiality of floods in paddy fields. The results showed that paddy fields in Banyuasin Regency experienced the widest decrease of 2,863.80 hectares and encountered the largest increase of 14,463.45 hectares in 1990-2003. The threat factor also influenced changes in the area of agricultural land in the form of inundation. The flood inundation crisis peaked in 2010, where 832.58 hectares out of 164,562.37 hectares of paddy field were flooded. However, the potency in developing lowland rice farming on swamps in Banyuasin Regency is considerably beneficial. This can be examined from the value of Revenue-Cost Ratio (R/C Ratio), averaging 3.65, and BenefitCost Ratio (B/C Ratio) of 2.65 with an average production of 5-7 tons/hectares/season.

/pdf/spatial-dynamics-of-agricultural-land-in-banyuasin-regency-2mqgmcrx2i.pdf

Spatial dynamics of agricultural land in Banyuasin Regency, South Sumatera: Its opportunities and threats

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