Environmental hazards: Assessing risk and reducing disaster

This study aims at providing expertise for preparing public-based flood mapping and estimating flood risks in growing urban areas. To model and predict the magnitude of flood risk areas, an integrated Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) analysis techniques are used for the case of Eldoret Municipality in Kenya. The flood risk vulnerability mapping follows a multi-parametric approach and integrates some of the flooding causative factors such as rainfall distribution, elevation and slope, drainage network and density, land-use/land-cover and soil type. From the vulnerability mapping, urban flood risk index (UFRI) for the case study area, which is determined by the degree of vulnerability and exposure is also derived. The results are validated using flood depth measurements, with a minimum average difference of 0.01 m and a maximum average difference of 0.37 m in depth of observed flooding in the different flood prone areas. Similarly with respect to area extents, a maximum error of not more than 8% was observed in the highly vulnerable flood zones. In addition, the Consistency Ratio which shows an acceptable level of 0.09 was calculated and further validated the strength of the proposed approach.

Urban Flood Vulnerability and Risk Mapping Using Integrated Multi-Parametric AHP and GIS: Methodological Overview and Case Study Assessment

In the present study, we applied artificial neural network (ANN) optimized with particle swarm optimization (PSO) for the problem of landslide susceptibility mapping (LSM) prediction. Many studies have revealed that the ANN-based techniques are reliable methods for estimating the LSM. However, most ANN training models facing with major problems such as slow degree of learning system as well as being trapped in their local minima. Optimization algorithms (OA) such as PSO can improve performance results of ANN. Existing applications of PSO model to ANN training have not been used in area of landslide mapping, neither assess the optimal architecture of networks nor the influential factors affecting this problem. Hence, the present study focused on the application of a hybrid PSO-based ANN model (PSO-ANN) to the prediction of landslide susceptibility hazardous mapping. To prepare training and testing datasets for the ANN and PSO-ANN network models, large data collection (i.e., a database consists 168970 training datasets and 42243 testing datasets) were provided from an area of Layleh valley, located in Kermanshah, west of Iran. All the variables of PSO algorithm (e.g., in addition to the network parameter and network weights) were optimized to achieve the most reliable maps of landslide susceptibility. The input dataset includes elevation, slope aspect, slope degree, curvature, soil type, lithology, distance to road, distance to river, distance to fault, land use, stream power index (SPI) and topographic wetness index (TWI), where the output was taken landslide susceptibility value. The predicted results (e.g., from ANN, PSO-ANN) for both of datasets (e.g., training and testing) of the models were assessed based on two statistical indices namely, coefficient of determination (R2) and root-mean-squared error (RMSE). In this study, to evaluate the ability of all methods, color intensity rating (CER) based on the result of above indices was developed. Apart from CER, the total ranking system was also used to rank the obtained statistical indexes. As a result, both models presented good performance, however, according to the introduced ranking system, the PSO-ANN model could perform a better performance compared to ANN. According to R2 and RMSE values of (0.9717 and 0.1040) and (0.99131 and 0.0366) were found for training dataset and values of (0.9733 and 0.111) and (0.9899 and 0.0389) obtained for testing dataset, respectively, for the ANN and PSO-ANN approximation models, it can be resulted that PSO-ANN model showed higher reliability in estimating the LSM compared to the ANN.

Modification of landslide susceptibility mapping using optimized PSO-ANN technique

In the present study, the impact of land use–land cover (LULC) change and urbanization on floods are investigated for an expanding urban catchment of the Oshiwara River in Mumbai, India. For the study area, the land use change was estimated between 1966, 2001 and 2009 by using the topographic map and satellite images. The analysis of LULC change revealed that the change between 1966 and 2001 was slower than that between 2001 and 2009. The LULC analysis revealed a 74.84% increase in the built up area with a 42.8% decrease in open spaces between the years 1966 and 2009, with substantial increase in urbanization. The impact of LULC on flood hydrograph for different return periods was ascertained by using the HEC-GeoHMS and HEC-HMS models. In the past 43 years, the increase in peak runoff and runoff volume is marginally varied by 3.0% and 4.45% for the 100-year return period and 10.4% and 12.2% for the 2-year return period respectively, although the built-up area increased by 74.84%. The flood inundation area is increased by 5.61% for the 100-year return period and 6.04% for the 10-year return period between the same time period. The results showed that lower return periods led to a maximum change in peak discharge/volume of runoff compared to higher return periods for change in land use conditions. Further, a flood hazard analysis has been carried out and it showed that the area in highly hazardous zone is increased by 64.29% as compared to less hazardous zone where it is decreased by 32.14%. Overall, the total flood hazard area is increased by 22.27%. The developed flood plain and flood hazard maps can be used by the local Municipal body to prepare flood mitigation and early evacuation management plans during floods and as a criteria for insurance of any property by insurance organizations.

Impacts of land use–land cover change and urbanization on flooding: A case study of Oshiwara River Basin in Mumbai, India

Malaysia lies in a geographically stable region, relatively free from natural disasters, but is affected by flooding, landslides, haze and other man-made disasters. Disaster management in Malaysia is traditionally based almost entirely on a government-centric top-down approach. Because Malaysia’s main risk comes from flooding, national disaster mitigation institutions largely target monsoon flooding. However, this mechanism is less than effective and should be re-modeled into something more pro-active. The Drainage and Irrigation Department (DID) is responsible for flood management. However, as an engineering-based organization, DID’s approach is largely focused on structural measures in controlling floods and lacks a holistic approach towards flood management. There is also a need for greater stakeholder participation, especially from NGOs at all levels in the disaster cycle. Malaysia should build capacity for NGOs, local communities and disaster victims. Disaster management mechanisms should also adopt more non-structural measures, bring in state-of-the-art technology and cooperate internationally with other countries for addressing transboundary disasters.

Impacts of Disasters and Disaster Risk Management in Malaysia: The Case of Floods

Land use change in urbanizing watersheds can have a significant impact on hydrologic and hydraulic process as well a degradation of water quality on receiving waters. The Bio‐Ecological Drainage System (BIOECODS) consists of elements of storage, flow retarding and infiltration engineering. Swales, dry ponds, detention ponds and wetland are the main components of BIOECODS that function as flow attenuation and water quality treatment devices. The BIOECODS is a pilot project that meets the requirements of the Stormwater Management Manual for Malaysia and has been constructed at the Engineering Campus of the University Science Malaysia, Nibong Tebal, Penang. BIOECODS represents an alternative to the traditional hard engineering‐based drainage system to manage stormwater quantity and quality for urban areas. This article discusses how the BIOECODS could be implemented to control stormwater quantity from an urbanized area and reduce the water quality impact on the receiving water.

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Bio‐ecological drainage system (BIOECODS) for water quantity and quality control

In the past decades, thousands of lives have been lost, directly or indirectly, by flooding. In fact, of all natural hazards, floods pose the most widely distributed natural hazard to life today. Sungai Kayu Ara river basin which is located in the west part of the Kuala Lumpur in Malaysia was the case study of this research. In order to perform river flood hazard mapping HEC-HMS and HEC-RAS were utilized as hydrologic and hydraulic models, respectively. The generated river flood hazard was based on water depth and flow velocity maps which were prepared according to hydraulic model results in GIS environment. The results show that, magnitude of rainfall event (ARI) and river basin land-use development condition have significant influences on the river flood hazard maps pattern. Moreover, magnitude of rainfall event caused more influences on the river flood hazard map in comparison with land-use development condition for Sungai Kayu Ara river basin.

GIS-based river flood hazard mapping in urban area (a case study in Kayu Ara river basin, Malaysia)

The 2003 flood of the Muda River reached 1,340 m 3 /s at Ladang Victoria and adversely impacted 45,000 people in Malaysia. A flood control remediation plan proposed a levee height based on a 50-year discharge of 1,815 m 3 /s obtained from hydrologic models. This design discharge falls outside the 95% confidence intervals of the flood frequency analysis based on field measurements. Instream sand and gravel mining operations also caused excessive riverbed degradation, which largely off sets apparent benefits for flood control. Pumping stations have been systematically required at irrigation canal intakes. Several bridge piers have also been severely undermined and emergency abutment protection works were needed in several places. Instream sand and gravel mining activities should be replaced with offstream mining in the future. DOI: 10.1061/ASCEHY.1943-7900.0000163 CE Database subject headings: Floods; Hydrologic models; Hydraulic engineering; Gravel; Mining; Malaysia. Author keywords: Flood mitigation; Flood control; Hydrologic models; Hydraulic engineering; Gravel mining.

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Case Study: Flood Mitigation of the Muda River, Malaysia

River flood risk map prediction is a combination of hydrological modelling, hydraulic modelling, river flood visualisation and river flood risk mapping. Two hydraulic models were applied in this research regarding their capabilities in river flood risk studies. These are MIKE11 developed by Danish Hydraulic Institute (DHI) and HEC-RAS4.0 by US Army Corps of Engineers. These two hydraulic models are compared in four aspects including credibility, available outcomes, usability of the models and the availability. Sungai Kayu Ara River basin is the case study in this research which is located in Kuala Lumpur, Malaysia. The results of the models were compared against observed water level at the outlet of the river basin. The results of this research show that HEC-RAS has more capabilities for river flood risk mapping in comparison with MIKE11 in this case study.

Comparison between capabilities of HEC-RAS and MIKE11 hydraulic models in river flood risk modelling (a case study of Sungai Kayu Ara River basin, Malaysia)

Rozi Abdullah

Papers

Bio‐ecological drainage system (BIOECODS) for water quantity and quality control

GIS-based river flood hazard mapping in urban area (a case study in Kayu Ara river basin, Malaysia)

Case Study: Flood Mitigation of the Muda River, Malaysia

Comparison between capabilities of HEC-RAS and MIKE11 hydraulic models in river flood risk modelling (a case study of Sungai Kayu Ara River basin, Malaysia)

Sediment transport modeling for Kulim River – A case study