Rob Flood as impact of sea level rise around Kenjeran Beach Tourism Surabaya

Abstract

Kenjeran Beach is located in the coastal area of the East Coast of Surabaya (Pamurbaya) where most of the ecosystems are mangroves, so this area has the potential to be inundated with water (flood). This study aims to analyze tidal flooding as the impact of sea level rise and formulate appropriate adaptation & mitigation strategies so that tourism utilization can be carried out in a sustainable manner. The method used in this study was the tidal calculation which produces sea level rise predictions and the Geographic Information System method which produces a map of the vulnerability of the tidal flood area. The map results were analyzed to determine adaptation and mitigation strategies from the tidal flood. Based on the analysis of sea level rise, the area inundated due to tidal flooding was around 42 cm in the next 50 years. In an effort to adapt and mitigate sea level rise disasters, it was carried out with structural (physical or technical) mitigation and non-structural (non-technical) mitigation.

Water Level Time Series Forecasting Using TCN Study Case in Surabaya

“…Although this rise happens slowly, it is happening faster now and has serious impacts [2], [3], and [4]. Due to their typical flat terrain and low elevations close to the water's surface, coastal areas are particularly vulnerable to the rise in water levels [5]. Surabaya, the capital city of East Java, predominantly lies in a low-lying area with altitudes ranging from 3-6 meters above water level, while its southern part exhibits higher elevations, reaching 20-50 meters above water level [6].…”

“…Surabaya, the capital city of East Java, predominantly lies in a low-lying area with altitudes ranging from 3-6 meters above water level, while its southern part exhibits higher elevations, reaching 20-50 meters above water level [6]. According to the Meteorological, Climatological, and Geophysical Agency (BMKG), Surabaya's coastal areas experience tidal flooding every year due to rising water levels [5]. In some years, the risk of flooding is four to five times higher than usual.…”

“…Specifically, we employ Temporal Convolutional Networks (TCN) for this purpose and compare its performance with other contemporary machine learning models, including transformers and RNN. The selected case study is Surabaya in Indonesia, characterized by its low-lying terrain, where the coastal areas are prone to tidal flooding due to rising water levels [5]. The contribution of this research is to find a better algorithm to predict the water level that could be implemented in other locations in Indonesia due to the many cities with similar conditions.…”