Flood damage assessment in agricultural area in Selangor river basin

Flooding is the major natural hazard that occurs in Malaysia. Flooding causes loss of lives, injuries, property damage and leave economic damage to the country, especially when it occurs in an agricultural area. There is still a lack of information available on floodplain especially on the impact of flooding in the agricultural area. This study focused on flooding and flooding impact in oil palm plantations, fruits and vegetables area. A river modeling is required to study the impact of flooding and to mitigate the floods using one mitigation option. A flood model was developed using InfoWorks Integrated Catchment Model (ICM) to carry out the analysis for flood damage assessment. With the aim of creating a flood damage map, Geographical Information System (GIS) was combined with the flood model to provide an ideal tool for the analysis of the flood damage and the effect of mitigation to flood damage. The estimated total damage for three different flood event; 10 ARI, 50 ARI and 100 ARI involved millions of ringgits. In order to reduce the flood impact along the Selangor River, a flood mitigation structure which is a retention pond was suggested, modeled and simulated. The effects of the retention ponds were analyzed and evaluated for 10 ARI, 50 ARI and 100 ARI. With this retention pond, flood extents of the flood events in agricultural area were shown capable of reduction significantly by 65.57% for 10 ARI, 76.18% for 50 ARI and 72.51% for 100 ARI

Improvement of Digital Elevation Model (DEM) using data fusion technique for oil palm replanting phase

Digital elevation models (DEMs) play an important role in producing terrain-related applications such as curvature and contour maps for planning and management of oil palm plantation. Data fusion of DEMs derived from terrestrial laser scanning (TLS) and interferometric aperture radar (IfSAR) was developed with the intention to increase the accuracy of IfSAR-derived DEM at a lower cost thus, provide a high-quality data for plantation management. In this research, fusion by weights was carried out after applying regression analysis to integrate both TLS and IfSAR data. The results showed a significant reduction in root mean square error (RMSEs) after fusion. RMSEs of both stations reduced from 1.83 m to 0.35 m and from 3.13 m to 0.41 m for Station 1 and Station 2, respectively. In addition, data fusion technique for an area with no TLS data were tested around the stations at 200 m distance. The RMSEs decreased from 2.52 m to 2.33 m for Station 1 but the value increased from 2.09 m to 2.13 m for Station 2. It was concluded that the proposed fusion technique in the extension area could be done in a relatively flat area but not be used in a steep-slope area

Deep learning semantic segmentation for water level estimation using surveillance camera

The interest in visual-based surveillance systems, especially in natural disaster applications, such as flood detection and monitoring, has increased due to the blooming of surveillance technology. In this work, semantic segmentation based on convolutional neural networks (CNN) was proposed to identify water regions from the surveillance images. This work presented two well-established deep learning algorithms, DeepLabv3+ and SegNet networks, and evaluated their performances using several evaluation metrics. Overall, both networks attained high accuracy when compared to the measurement data but the DeepLabv3+ network performed better than the SegNet network, achieving over 90% for overall accuracy and IoU metrics, and around 80% for boundary F1 score (BF score), respectively. When predicting new images using both trained networks, the results show that both networks successfully distinguished water regions from the background but the outputs from DeepLabv3+ were more accurate than the results from the SegNet network. Therefore, the DeepLabv3+ network was used for practical application using a set of images captured at five consecutive days in the study area. The segmentation result and water level markers extracted from light detection and ranging (LiDAR) data were overlaid to estimate river water levels and observe the water fluctuation. River water levels were predicted based on the elevation from the predefined markers. The proposed water level framework was evaluated according to Spearman’s rank-order correlation coefficient. The correlation coefficient was 0.91, which indicates a strong relationship between the estimated water level and observed water level. Based on these findings, it can be concluded that the proposed approach has high potential as an alternative monitoring system that offers water region information and water level estimation for flood management and related activities

Development of river water level estimation from surveillance cameras for flood monitoring system using deep learning techniques

Around 70% of global disasters are related to hydro-meteorological events such as drought, floods, and cyclones. Therefore, researchers and experts carried out many studies on flood hazards in order to reduce the impact of flood magnitude and flood frequency. In Malaysia, a telemetric forecasting system is currently been used in flood monitoring systems. However, data information obtained from this system is one spatial dimension and one point-based station, thus it cannot represent the dynamics of the surface water extent. Therefore, this study introduces a visual surveillance concept to monitor the flood event in a specific area, based on surveillance cameras and computer vision approaches to obtain instant flood inundation information during flood events. A deep learning approach was proposed for water segmentation so that it can be applied to various water scenarios and backgrounds. However, conventional image segmentation techniques were also carried out to ensure the usage of deep learning is worth it. The conventional segmentation methods used in this work are thresholding, region growing, and hybrid technique known as GeoRegion. The findings demonstrated that these methods are handcrafted and the algorithms need to be changed when applying to different images, which is not practical to be used during flood disasters. Hence, deep learning technique was chosen for water segmentation procedure in this work. Two different networks were applied in this study, namely DeepLabv3+ and SegNet, for detecting water regions before estimating water levels from surveillance images. Water level estimation was predicted based on the elevations from LiDAR data. Based on the experimental results, it was found that the DeepLabv3+ network performed better than the SegNet network by achieving above 93% for overall accuracy and IoU metrics, and approximately 82% for boundary F1 score (BF score). The Spearman’s rank correlation obtained between water level measured by the sensor and water level estimated from the proposed framework was 0.92 which indicates a strong relationship. By integrating the estimated water level with a 3D model developed from LiDAR data, flood simulation was performed. Besides, volume of water was also computed from the 3D model. The findings demonstrate that the water volume increased as water level increased. Lastly, a graphical user interface was developed for water segmentation and water level estimation analysis that could be applied during the flood events. Hence, the proposed work can help in improving the current monitoring and emergency warning abilities against flood events, serving as a complement to the currently used quantitative precipitation forecasts and in-situ water-level measurements

Improvement of vertical height accuracy using data fusion technique for terrain mapping in oil palm plantation

Digital elevation models (DEMs) play an important role in producing terrainrelated applications such as curvature and contour maps for planning and management of oil palm plantation. Compared to Light Detection and Ranging (LiDAR) data, Interferometric Synthetic Aperture Radar (IfSAR) has lower accuracy but the cost is much cheaper. In order to increase the accuracy of IfSAR data, fusion of IfSAR and terrestrial LiDAR (TLS) datasets was proposed in this study. The TLS data collection was carried out in TH Plantation in Muadzam Shah, Pahang using Faro 3D Laser Scanner. Two different stations were selected with different terrain characteristics. Station 1 was located in a relatively flat area while station 2 was located in a rolling and hilly area. Raw data of TLS were filtered using TerraScan software to extract the ground points from object points. In this study, the efficiency of filtering technique for TLS data was assessed and determined before being used for data fusion with IfSAR. The performance of data filtering was tested by using double filtering technique. Using this technique, 20,977594 points were correctly identified as object points while 10804 object points were mistakenly classified as ground points. Statistically, 0.05% of type II errors (accept object points as ground points) were obtained in the study area. The result indicates that filtering algorithm in TerraScan was good enough to be used for TLS data in oil palm plantation. When the filtering was completed, data fusion of TLS and IfSAR-derived DEM was developed to increase the accuracy of IfSAR-derived elevation models and provide high quality data for plantation management especially for slope risk management. This study used fusion by weights based on the spatial errors after applying regression equation. The results show a significant reduction in RMSEs after fusion. RMSEs of both stations reduced from 1.83 m to 0.35 m and from 3.13 m to 0.41 m for station 1 and station 2 respectively. In addition, data fusion technique for area with no TLS data that located nearby the station was tested. Data fusion of these areas was carried out by using regression equation of their relative station but the weighted values were computed differently from the previous fusion technique. The weighted value was computed using mean error of the elevation of its relative station, the mean error of the elevation based on classified elevation range and the error pattern based on its relative station. All results proved that the proposed fusion technique could be done in relatively flat area but it could not be used in steep-slope area. A mobile application was also developed for field data collection and verification. The application has been successfully developed and tested in the field. On the whole, it is concluded that data fusion is a promising technique for increasing the accuracy of IfSAR-derived DEM in oil palm plantation

The Use of LiDAR-Derived DEM in Flood Applications: A Review

Flood occurrence is increasing due to escalated urbanization and extreme climate change; hence, various studies on this issue and methods of flood monitoring and mapping are also increasing to reduce the severe impacts of flood disasters. The advancement of current technologies such as light detection and ranging (LiDAR) systems facilitated and improved flood applications. In a LiDAR system, a laser emits light that travels to the ground and reflects off objects like buildings and trees. The reflected light energy returns to the sensor, whereby the time interval is recorded. Since the conventional methods cannot produce high-resolution digital elevation model (DEM) data, which results in low accuracy of flood simulation results, LiDAR data are extensively used as an alternative. This review aims to study the potential and the applications of LiDAR-derived DEM in flood studies. It also provides insight into the operating principles of different LiDAR systems, system components, and advantages and disadvantages of each system. This paper discusses several topics relevant to flood studies from a LiDAR-derived DEM perspective. Furthermore, the challenges and future perspectives regarding DEM LiDAR data for flood mapping and assessment are also reviewed. This study demonstrates that LiDAR-derived data are useful in flood risk management, especially in the future assessment of flood-related problems

Estimating agricultural losses using flood modeling for rural area

Flooding is the most significant natural hazard in Malaysia in terms of population affected, frequency, flood extent, flood duration and social economic damage. Flooding causes loss of lives, injuries, property damage and leave some economic damage to the country especially when it occurs in a rural area where the main income is dependent on agricultural area. This study focused on flooding in oil palm plantations, rubber plantations and fruits and vegetables area. InfoWorks ICM was used to develop a flood model to study the impact of flooding and to mitigate the floods using a retention pond. Later, Geographical Information System (GIS) together with the flood model were used for the analysis on flood damage assessment and management of flood risk. The estimated total damage for three different flood event; 10 ARI, 50 ARI and 100 ARI involved millions of ringgits. In reducing the flood impact along the Selangor River, retention pond was suggested, modeled and tested. By constructing retention pond, flood extents in agricultural area were reduced significantly by 60.49% for 10 ARI, 45.39% for 50 ARI and 46.54% for 100 ARI