Flooding is one of the most destructive natural hazards that cause damage to both life and property every year, and therefore the development of flood model to determine inundation area in watersheds is important for decision makers. In recent years, data mining approaches such as artificial neural network (ANN) techniques are being increasingly used for flood modeling. Previously, this ANN method was frequently used for hydrological and flood modeling by taking rainfall as input and runoff data as output, usually without taking into consideration of other flood causative factors. The specific objective of this study is to develop a flood model using various flood causative factors using ANN techniques and geographic information system (GIS) to modeling and simulate flood-prone areas in the southern part of Peninsular Malaysia. The ANN model for this study was developed in MATLAB using seven flood causative factors. Relevant thematic layers (including rainfall, slope, elevation, flow accumulation, soil, land use, and geology) are generated using GIS, remote sensing data, and field surveys. In the context of objective weight assignments, the ANN is used to directly produce water levels and then the flood map is constructed in GIS. To measure the performance of the model, four criteria performances, including a coefficient of determination (R
 2), the sum squared error, the mean square error, and the root mean square error are used. The verification results showed satisfactory agreement between the predicted and the real hydrological records. The results of this study could be used to help local and national government plan for the future and develop appropriate (to the local environmental conditions) new infrastructure to protect the lives and property of the people of Johor.

An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia

Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey

Assessing the potential of thermal infrared satellite surveys for monitoring seismically active areas: The case of Kocaeli (İzmit) earthquake, August 17, 1999

https://hal.archives-ouvertes.fr/hal-00602355/document

Analysis of TerraSAR-X data sensitivity to bare soil moisture, roughness, composition and soil crust

In the Pacific Northwest of North America, significant flooding can occur during mid-winter rain-on-snow events. Warm, wet Pacific storms caused significant floods in the Pacific Northwest in February 1996, January 1997 and January 1998. Rapid melting of the mountain snow cover substantially augmented discharge during these flood events. An energy-balance snowmelt model is used to simulate snowmelt processes during the January 1997 event over a small headwater basin within the Reynolds Creek Experimental Watershed located in the Owyhee Mountains of southwestern Idaho, U.S.A. This sub-basin is 34% forested (12% fir, 22% aspen and 66% mixed sagebrush (primarily mountain big sagebrush)). Data from paired open and forested experimental sites were used to drive the model. Model-forcing data were corrected for topographic and vegetation canopy effects. The event was preceded by cold, stormy conditions that developed a significant snow cover over the sub-basin. The snow cover at sites protected by forest cover was slightly reduced, while at open sites significant snowmelt occurred. The warm, moist, windy conditions during the flooding event produced substantially higher melt rates in exposed areas, where sensible- and latent-heat exchanges contributed 60–90% of the energy for snowmelt. Simulated snow-cover development and ablation during the model run closely matched measured conditions at the two experimental sites. This experiment shows the sensitivity of snowmelt processes to both climate and land cover, and illustrates how the forest canopy is coupled to the hydrologic cycle in mountainous areas.

/pdf/simulating-snowmelt-processes-during-rain-on-snow-over-a-5169usug6b.pdf

Manfred Owe

Papers

Remote Sensing and Hydrology 2000