Impacts of Climate Change on Flood-Prone Areas in Davao Oriental, Philippines

Cabrera, Jonathan Salar; Lee, Han Soo

doi:10.3390/w10070893

Cited by 36 publications

(21 citation statements)

References 27 publications

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“…The changes will continue until every pixel undergoes the changing process. The ANN-CA model might be more reliable in terms of reduced subjectivity and uncertainty when compared to other methods such as analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) for weighting criteria [43,44]. Moreover, this study illustrates that the ANN-CA model is successfully applied to conduct multiple LULC change simulations within one CA model by considering the complicated interactions and competition among different land use categories.…”

Section: Discussionmentioning

confidence: 89%

Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton

2019

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Land use and land cover (LULC) form a baseline thematic map for monitoring, resource management, and planning activities and facilitate the development of strategies to balance conservation, conflicting uses, and development pressures. In this study, changes in LULC in North Sumatra, Indonesia, are simulated and predicted using an artificial-neural-network-based cellular automaton (ANN-CA) model. Five criteria (altitude, slope, aspect, distance from the road, and soil type) are used as exploratory data in the learning process of the ANN-CA model to determine their impacts on LULC changes between 1990 and 2000; among the criteria, altitude and distance from the road have strong impacts. Comparison between the predicted and the real LULC maps for 2010 illustrates high agreement, with a Kappa index of 0.83 and a percentage of correctness of 87.28%. Then, the ANN-CA model is applied to predict LULC changes in 2050 and 2070. The LULC predictions for 2050 and 2070 demonstrate high increases in plantation area of more than 4%. Meanwhile, forest and crop area are projected to decrease by approximately 1.2% and 1.6%, respectively, by 2050. By 2070, forest and crop areas will decrease by 1.2% and 1.7%, respectively, indicating human influences on LULC changes from forest and cropland to plantations. This study illustrates that the simulation of LULC changes using the ANN-CA model can produce reliable predictions for future LULC.Simulation of LULC changes provides the baseline scenario for use when predicting future scenarios and patterns in future development. Simulation of LULC changes can indicate anthropogenic impact, identify land use problems, such as the degradation and deforestation, and be used in land use planning [2]. Land use changes have been important aspects for regional and urban planning for centuries. Most of the research predicts land use changes using the independent variable with the highest correlation to those changes. Verburg et al. [3] illustrated that the multiscale characteristics of land use systems require new techniques to quantify neighborhood effects. Furthermore, Verburg et al.[3] also explicitly dealt with temporal dynamics and achieved a higher level of integration among disciplinary approaches and between models studying urban and rural land use changes. Multivariate spatial models [4], Markov analysis [5], cellular automata (CA) [3,6-9], neural-network-based CA [2], empirical-statistical models [10,11], optimization models [12], and agent-based models [13,14] have all been used in the simulation of land use change. More details on each method can be found in [15].The CA is a common method for simulating the LULC change spatial evolution by estimating the state of a pixel according to its initial state, surrounding neighborhood effects and transition rules. A CA model can generate rich patterns and effectively represent nonlinear spatially stochastic LULC change processes [16,17]. The applications of CA models have been growing in urban development studies, with strong capabilities for si...

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Section: Discussionmentioning

confidence: 89%

Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton

2019

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“…Climate change will aggravate the regional hydrological cycle [57]. Studies have shown that the rainfall will increase with increases in temperatures, leading to increased flood intensity [58]. Due to the well-developed water system in the lower reaches of the Huai River Basin, there is little difference in river network density among different regions.…”

Section: Index System Of Flood Drainage Rights Allocationmentioning

confidence: 99%

Allocation of Flood Drainage Rights Based on the PSR Model and Pythagoras Fuzzy TOPSIS Method

Zhang

Shen

Liu

et al. 2020

IJERPH

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To minimize losses caused by flooding of areas in a river basin, flood risk management may sacrifice the interests of some areas. Because of regional differences in natural and urban conditions, rankings of the urgencies of flood drainage rights allocations in different regions are of great practical significance to the realization of optimal allocations and reduction of damages. Based on the “pressure–state–response” (PSR) framework, this study designed an index system of flood drainage rights allocations in river basins for the comprehensive consideration of the different attributes of regional societies, environments, and technologies, as well as the differences in the quality of technical management and in the levels of social and economic development. A Pythagoras fuzzy TOPSIS method was used to evaluate the urgencies and determine the management of allocations in different areas. Eight cities in Jiangsu Province in the Huai River Basin were selected as the research objects. The results showed that pressure factors played dominant roles in the degrees of urgency. Among the cities, Nantong had the highest degree, followed by Taizhou, whereas Lianyungang had the lowest. The degrees in the central region of Jiangsu were higher than in the northern region.

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“…However, the AHP method shows some limitations due to the subjectivity in choosing the value of the indicator weighting from the arbitrary judgments of experts [1,6,13]. The CR is the crucial element when generating HI for the multi-criteria analysis using AHP method.…”

Section: Effects Of Varying Consistency Ration (Cr) On Flood Hazard Mapmentioning

confidence: 99%

“…AHP is a popular and widely used method developed by Saaty [34] to solve a wide range of multi-criteria decision-making problems, combined with the use of a pairwise comparison matrix that calculates the weights of every criterion considered. Recently, researchers have given considerable attention to using AHP in flood risk assessment [1,4,13,35,36]. In those case studies, it has been shown that AHP can assess and map flood risk areas with reliable accuracy.…”

Section: Introductionmentioning

confidence: 99%

Flood-Prone Area Assessment Using GIS-Based Multi-Criteria Analysis: A Case Study in Davao Oriental, Philippines

Cabrera

Lee

2019

Water

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Flooding is one of the major destructive natural disasters in Davao Oriental, Philippines, and results primarily from a high incidence of typhoons and heavy rainfalls. The main objective of this study was to identify flood-prone risk areas by mapping them based on the integration of multiple indicators, including rainfall, slope, elevation, drainage density, soil type, distance to the main channel and population density. For this purpose, a GIS-based flood risk spatial assessment was conducted by using analytic hierarchy process (AHP), weights by rank (WR) and ratio weighting (RW) frameworks to determine the relative importance of each indicator against another in the province of Davao Oriental. The resulting flood-prone areas by the three methods are validated by comparing with the estimated flood map based on ground truthing points from a field survey. The comparison results show that AHP is the most appropriate method among them to assess flood hazard. The result of the AHP flood risk map shows that 95.99% (5451.27 km2) of Davao Oriental is under low and moderate flood risk. The high and very high flood risk area covers approximately 3.39% (192.52 km2) of the province, primarily in the coastal areas. Thirty-one out of the one hundred eighty-three (31/183) barangays (towns) are at a high to very high risk of flooding at current climate, calling for the immediate attention of decision-makers to develop mitigation strategies for the future occurrence of flooding in Davao Oriental.

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Impacts of Climate Change on Flood-Prone Areas in Davao Oriental, Philippines

Cited by 36 publications

References 27 publications

Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton

Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton

Allocation of Flood Drainage Rights Based on the PSR Model and Pythagoras Fuzzy TOPSIS Method

Flood-Prone Area Assessment Using GIS-Based Multi-Criteria Analysis: A Case Study in Davao Oriental, Philippines

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