Urbanization and Increasing Flood Risk in the Northern Coast of Central Java—Indonesia: An Assessment towards Better Land Use Policy and Flood Management

Handayani, Wiwandari; Chigbu, Uchendu Eugene; Rudiarto, Iwan; Putri, Intan Hapsari Surya

doi:10.3390/land9100343

Cited by 74 publications

(58 citation statements)

References 44 publications

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“…A Plan for Growing Sydney aims to provide about 39,000 new homes and 37,000 new jobs in the Hawkesbury-Nepean floodplain region. This is approximately 90,000 people in addition to the current 138,000 people who will be living and working in the Hawkesbury-Nepean floodplain [33][34][35][36][37].…”

Section: Impacts Of Flood Events and The Population At Riskmentioning

confidence: 99%

An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective

et al. 2022

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The Hawkesbury-Nepean Valley, Australia’s longest coastal catchment, is spanned by a river system of more than 470 km, that runs from Goulburn to Broken Bay, covering a total area of over 2.2 million hectares. This region has remained prone to flood events, with considerable mortalities, economic impacts and infrastructural losses occurring quite regularly. The topography, naturally variable climatic conditions and the ‘bathtub’ effect in the region are responsible for the frequent flood events. In response, the Government at the national/federal, state and local level has focused on the design of efficient flood risk management strategies with appropriate evacuation plans for vulnerable communities from hospitals, schools, childcare and aged care facilities during a flood event. Despite these overarching plans, specialized response and evacuation plans for aged care facilities are critical to reducing the loss incurred by flood events in the region. This is the focus of this present paper, which reviews the history of flood events and responses to them, before examining the utilization of artificial intelligence (AI) techniques during flood events to overcome the flood risks. An early flood warning system, based on AI/Machine Learning (ML) strategy is being suggested for a timely decision, enhanced disaster prediction, assessment and response necessary to overcome the flood risks associated with aged care facilities within the Hawkesbury-Nepean region. A framework entailing AI/ML methods for identifying the safest route to the destination using UAV and path planning has been proposed for timely disaster response and evacuation of the residents of aged care facilities.

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Section: Impacts Of Flood Events and The Population At Riskmentioning

confidence: 99%

An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective

et al. 2022

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“…A notable example would be the recent Trans Java Expressway development in Java's North Cost (Hudalah et al, 2020). Having the intention to spur interregional connectivity and economic growth in Indonesia's most populated island, this state-led megaproject may increase the vulnerability of cities located along the coastline to environmental pressures, most notably flooding and land subsidence (Sarah and Soebowo, 2018;Handayani et al, 2020). Such land development has also often sacrificed the everyday life of communities at the margin even though it is carried out under the environmental protection banner.…”

Section: Policy and Governance Contextmentioning

confidence: 99%

Revisiting the Green City Concept in the Tropical and Global South Cities Context: The Case of Indonesia

Zain

Pribadi

Indraprahasta

2022

Front. Environ. Sci.

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Urban areas play a key role in reaching global sustainability as they produce a high amount of waste and emissions, consume a lot of resources, and perform as the prime mover of the global economy. Unsustainable urbanization will generate multidimensional impacts on the earth’s socio-ecological system that is nearly impossible to be managed. As a preemptive action, urban sustainability has been considered as one of the most important targets in the Sustainable Development Goals. Within this context, the green city has emerged as a widely adopted concept around the globe. In the Global North, the concept of a green city has been incrementally developed as efforts to mainstream green infrastructure and nature-based solutions approaches in supporting ecosystem services. Quite the contrary, in the Global South cities, due to their rapid and vast urbanization process, the green city has been fragmentally adopted. Previous studies have proposed three factors, i.e., urbanization, biophysics, and governance, underlying the different approaches of green city development between cities in the Global North and South. Still, more studies are needed to explicate these factors and how these will in turn shape a particular green city trajectory in Global South cities. This study aims to respond to these questions based on green city experimentation in Indonesia. An exploratory study was done based on a literature review and participant observation. The results exhibit that the green city program in Indonesia largely focused on the development of green open spaces which has ultimately failed to improve the urban environment and sustainability. Incorporating local socio-ecological aspects coupled with integrated multi-level and multi-actors’ governance is recommended to increase the green city performances.

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“…However, high rainfall conditions do not always cause flood. The flood can cause by many factors, such as improper land use management [2,3].…”

Section: Introductionmentioning

confidence: 99%

Linear Regression Analysis Using Log Transformation Model for Rainfall Data in Water Resources Management Krueng Pase, Aceh, Indonesia

Ramli¹,

Basri²,

Achmad³

et al. 2022

IJDNE

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Climate changes are one crucial factor that influenced water availability at one location since they affected the environmental, social, and agricultural systems. The study observed the agent factors that influenced the rainfall changes at Krueng Pasee Aceh watershed, Indonesia. The method used in this research is a linear regression with a log transformation approach on predictor variables. The data used in this study consisted of rainfall, a total of rainy days, temperature, humidity, duration of irradiation, and wind speed in the period ranging from 1992 to 2020. Results showed that the agent factors had not distributed normally. The regression model produced after log transformation had met the classical assumptions and can be used to predict the rainfall at R-quare 24.61% with an RMSE value of 57.676. From all factors studied, the wind speed should be excluded. Further study is recommended to use the nonlinear method to improve the model for rainfall prediction.

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Urbanization and Increasing Flood Risk in the Northern Coast of Central Java—Indonesia: An Assessment towards Better Land Use Policy and Flood Management

Cited by 74 publications

References 44 publications

An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective

An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective

Revisiting the Green City Concept in the Tropical and Global South Cities Context: The Case of Indonesia

Linear Regression Analysis Using Log Transformation Model for Rainfall Data in Water Resources Management Krueng Pase, Aceh, Indonesia

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