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The increasing population in an area often triggers heightened development, leading to diminished land availability and subsequent land conversion. This transformation, predominantly observed in agricultural and plantation lands, is driven by the need for residential areas. However, continuous land use changes contribute to erosion, a natural process wherein soil is transported, leading to sedimentation and eventual flooding. This study aims to identify critical areas prone to erosion and land degradation. To address this, we used the Universal Soil Loss Equation (USLE) method for erosion prediction, leveraging its simplicity and accuracy. This study focuses on the Mayang watershed in East Java, utilizing a combination of USLE, spectral index modeling, and Geographic Information System (GIS) techniques to estimate soil erosion and land criticality. By integrating methodologies and analyzing data spanning from 2011 to 2021, the study reveals shifts in erosion danger levels and land use patterns. While regions with low erosion remained stable, areas with medium to high erosion declined, yet those with very high erosion exhibited a worrisome increase. Changes in land use, including forest loss and urban expansion, underscore the ecological shifts exacerbating erosion hazards. The study emphasizing the necessity for targeted conservation strategies and comprehensive land management plans to mitigate erosion risks and preserve environmental sustainability. [ABSTRACT FROM AUTHOR]

Puger Beach located in Puger Village, Jember Regency which is often passed by fishing boats to Puger Fish Market. The problem of high waves is often experienced by fishermen. In 2014, a breakwater was built in the estuary of Puger Beach to overcome the problem. However, the construction of the breakwater is considered not optimal because the layout of the building only dampens waves from the west. Based on this background, this study aims to model the sea waves that occur in the estuary of Puger Beach due to the influence of the breakwater layout. Waves were modeled based on the dominant wind direction at the study site: southwest, west, and south. Modeling was made using Delft3D software by creating five breakwater placement schemes. The modeling results obtained southwest, west, and south respectively are: the first scheme reduced the wave height by 31.01%, 27.33%, and 37%; the second scheme reduced the wave height by 28.95%, 31.59%, and 36.63%; the third scheme reduced the wave height by 78.12%, 82.08%, and 65.32%; the fourth scheme reduced the wave height by 79.91%, 47.28%, and 76.54%; the fifth scheme reduced the wave height by 94.53%, 99.78%, and 96.78%.

In December 2021, Mount Semeru experienced an eruption accompanied by extreme rainfall, which resulted in lava floods, known as lahars or debris flows. The lava flood destroyed infrastructure, resulting in loss of life. Various rivers surrounding Mount Semeru, including the Rejali River, experienced the effects of this phenomenon. To address this, a study is needed to analyze the occurrence and frequency of lava floods over specific time intervals through the creation of a hazard map. This study aims to map the hazard of lava floods for various return periods using a coupled HEC-HMS and HEC-RAS software alongside a lava flood discharge approach. The HEC-HMS software is used to simulate hydrological processes, to obtain the lava flood discharge, while the HEC-RAS is used to model a two-dimensional (2D) lava flood hazard map. The input parameters of the modeling in this study are rainfall intensity, soil type, land cover, river distance, slope, and elevation. The results show that the flood area covers 9.55% of the total study area by 2 year return period (Q2), 11.80% by Q10, 14.10% by Q50, and 15.72% by Q200 with an overall validation Root Mean Square Error (RMSE) of 0.16. These changes are determined by the discharge volume from each return phase and the river's shallow depth, which causes overflow beyond the river's ability to accommodate the flow. Thus, this study suggests that the models successfully generated a reliable model for mapping the risk of lava floods on the Rejali River. These findings can help the government reduce disaster losses through adequate adaptation and mitigation initiatives. [ABSTRACT FROM AUTHOR]

Floods that frequently hit the Jompo Watershed (DAS) area cause damage to irrigation networks. The magnitude of the damage certainly requires much money, while the maintenance funds are minimal and not comparable to the area of the irrigation network. This research aims to select the most appropriate priority for network rehabilitation to produce the maximum benefits. Compiling priorities uses the Analytic Hierarchy Process (AHP) method, but it contains subjectivity in the preparation of AHP. Therefore, to reduce its subjectivity, this study integrates AHP with the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method, Weight Aggregated Sum Product Assessment (WASPAS), and Multi-Objective Optimization by Ratio Analysis (MOORA). Rehabilitation criteria assess the performance of irrigation networks by using the condition of damage to buildings (A1), availability of water (A2), area of irrigation (A3), and crop productivity (A4). The results of AHP-TOPSIS [0.1], AHP-WASPAS [0.0.8], and AHP-MOORA are effectively used in decision-making in rehabilitating irrigation networks. In addition, the results can represent the performance of irrigation networks. The priority to be rehabilitated is the Pangarengan (B3) irrigation network, with preference values of 0.72, 0.81, and 0.33. [ABSTRACT FROM AUTHOR]

Volcanic mudflow floods occur when rainfall runoff combines with volcanic material and flows downstream. These devastating events cause significant damage to infrastructure, disrupt economies, and result in injuries and casualties. One area where the flow of volcanic material greatly affects the situation is the Rejali River, which receives a substantial amount of volcanic debris from Mount Semeru. To address this issue and begin mitigating the associated risks, it is crucial to start by mapping the potential distribution of volcanic mudflow floods. Therefore, this study aimed to assess factors impacting volcanic mudflow flood susceptibility and to create a corresponding susceptibility map. The study employed the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the influence of various factors and classify the areas, respectively. These methods were integrated with the Geographic Information System (GIS) to enhance the analysis. The weighted analysis results showed that the most impactful factors conditioning volcanic mudflow floods, in descending order, were rainfall (42.40%), land cover (13.89%), elevation (13.39%), slope (12.51%), distance from the river (7.09%), soil type (6.58%), and rock distribution (4.13%). The TOPSIS calculation further highlighted that rainfall intensity between 104.03 and 109.65 mm day-1 had the greatest influence on susceptibility. The successful integration of AHP and TOPSIS methods with GIS helped develop a volcanic mudflow flood susceptibility model with an outstanding accuracy of 0.969. The model showed that approximately 46.40% of the areas along the Rejali River exhibited very high susceptibility to volcanic mudflow floods, while an additional 16.21% indicated high susceptibility and substantial risk in most regions. Therefore, the generated susceptibility map offered important insights for shaping future mitigation strategies and influencing policy decisions. [ABSTRACT FROM AUTHOR]

The availability of a flood hazard map is beneficial during flood risk reduction. However, there is a lack of high-resolution topographic data, which is a map realization obstacle. Therefore, this study aims to construct flood hazard maps based on hazard levels for various flood return periods. The 2D-hydrodynamic from the Hydrologic Engineering Center's River Analysis System (HEC-RAS) and Digital Surface Models (DSM) from Unmanned Aerial Vehicle (UAV) imagery were used for generating inundation maps. The results showed that simulation of return periods for 2 and 10 years illustrated a 37% increase in flood hazard levels. Furthermore, there was an increased danger level for locations that were exposed to flood inundation. This occurred in housing, some roads, and rice fields. Ultimately, this study mitigates flood hazards through the determination of evacuation directions, urban spatial planning, and informed flood mitigation measures. [ABSTRACT FROM AUTHOR]

In Indonesia, bamboo pile mattress is commonly utilized for reinforced embankments over deep soft soil, especially for shallow ground water table or even over the ground surface as the water functions as timeless preservation for the bamboo. However hardly any articles have been published for design references. This work intends to evaluate bamboo pile mattress for embankment reinforcement numerically by means of Plaxis 2D. A parametric analysis was conducted to assess the effect of both piles spacing s and length L on embankment settlement, stability, and pile load ratio αp. A ratio s/L is introduced to evaluate the combination of s and L effects. Furthermore, normalized settlement S/S∞ and normalized factor of safety SF/SF∞ are proposed to facilitate the assessment, in which S∞ and SF∞ are ones at s/L = ∞ (bamboo mattress with no pile condition). The results show that S/S∞ increases with increasing s/L. Inversely, SF/SF∞ and αp decrease with decreasing s/L. When s and L are increased independently, S/S∞ and SF/SF∞ curves initially follow different paths. When s/L > 0.1 the curves merge into a line. Meanwhile, αp curves blend into a line and separate with a different slope when s/L > 0.1. s/L = 0.1 becomes an onset point to show that L provides more significant effects than s for S/S∞ and SF/SF∞ at s/L < 0.1. However, L produces less influence on αp than s at s/L > 0.1. Furthermore, the findings may provide insight and direction for reinforced embankment design using bamboo pile mattress. [ABSTRACT FROM AUTHOR]

Floods in coastal areas occur yearly in Indonesia, resulting in socio-economic losses. The availability of flood susceptibility maps is essential for flood mitigation. This study aimed to explore four different types of models, namely, frequency ratio (FR), weight of evidence (WofE), random forest (RF), and multi-layer perceptron (MLP), for coastal flood susceptibility assessment in Pasuruan and Probolinggo in the East Java region. Factors were selected based on multi-collinearity and the information gain ratio to build flood susceptibility maps in small watersheds. The comprehensive exploration result showed that seven of the eleven factors, namely, elevation, geology, soil type, land use, rainfall, RD, and TWI, influenced the coastal flood susceptibility. The MLP outperformed the other three models, with an accuracy of 0.977. Assessing flood susceptibility with those four methods can guide flood mitigation management. [ABSTRACT FROM AUTHOR]