A thesis submitted to the Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka in partial fulfillment of. Flash flooding in the pre-monsoon season (March-May) is one of the major natural disasters of the Upper Meghna basin, which often destroys Boro rice, the main agricultural product of northeastern Bangladesh.
INTRODUCTION
Background and Present State of the Problem
These models usually involve simplified forms of physical laws and are generally non-linear, time-invariant and deterministic, with parameters that are representative of watershed characteristics [4]. A major advantage of this approach is that these models are generally very fast to run and much faster to develop than physics-based conceptual models.
Objectives of the Study and Possible Outcomes The specific objectives of this study are
On the other hand, data-driven methods provide an alternative approach to these conceptual and physically based hydrological models, in that they are not built using knowledge of the underlying physical processes. The comparison of the performance of ANN and SVM was carried out for only four river stage monitoring stations in northeastern Bangladesh.
Organization of the Chapters
Remotely sensed rainfall data were used due to non-availability of observed rainfall data over catchment areas located within India. Modeling was done with 3-hourly time resolution data due to the lack of availability of hourly river stage data.
LITERATURE REVIEW
- Flash Floods in the Northeast Region of Bangladesh
- Physically-based or conceptual models
- Empirical or data-driven models
- Application of Artificial Neural Networks in Flood Forecasting
- Application of Support Vector Machines in Flood Forecasting
- Application of TRMM Rainfall Data in Flood Forecasting
Flash floods are a major threat to the livelihood of the Haor people, especially sharecroppers and landless workers. Akhtar et al predicted river flows of the Ganges using historical flow and rainfall data.
ARTIFICIAL NEURAL NETWORKS
- Introduction to Artificial Neural Networks
- Types of ANN
- Structures of ANN
- Training an ANN
A test dataset: this portion of the data is used to test the network on an unseen or independent dataset that was not used during the training process. If the objective function of the ANN is the sum of squares, then the Hessian matrix can be written as
SUPPORT VECTOR MACHINES
- Introduction to Support Vector Machines
- Nonlinear support vector regression
- Optimization algorithms
- Sequential Minimal Optimization
It can be shown that this regression problem can be expressed as the following convex optimization problem. Once the Lagrange multipliers α𝑖 and α𝑖 have been determined, the parameter vectors w and b can be evaluated under Karush–. SVM can easily handle any increase in the input variables or the number of data in the input vectors, because the dot product of the two vectors can be computed without difficulty.
Similarly, the nonlinear regression problem can be expressed as the optimization problem in Equation (4.9) and the dual form of the nonlinear SVR can be expressed by modifying Equation (4.10) as. Functions that satisfy Mercer's condition [68] can be proved to correspond to dot products in a feature space. Convex programming algorithms can be used directly on medium-sized sample data sets (up to 3000) without any further modification.
A first solution, introduced in [69], relies on the observation that the solution can be reconstructed from the SVs alone. The key point is that for a working set of 2, the optimization subproblem can be solved analytically without explicitly calling a quadratic optimization.
STUDY AREA AND DATA
Topographic Data
The primary data source for HydroSHEDS is the Shuttle Radar Topography Mission (SRTM) DEM. The general focus is to find a compromise between forcing the DEM to provide correct river network topology, especially for the largest rivers, while preserving as much original SRTM information as possible. The power grid and elevation of each of the four subbasins are shown in Figures 5.2 to 5.5.
Rainfall Data
Meanwhile, the TRMM Microwave Imager (TMI) continued to operate with slowly changing capabilities until it was shut down on 8 April 2015 as part of the decommissioning of the satellite. The TRMM mission was succeeded by the Global Precipitation Measurement (GPM) mission, which provides a new rainfall product called the Integrated Multi-satellite Retrievals for GPM (IMERG). In order to continue the global rainfall time series collected by the TRMM satellite, all the data from the 3B42RT rainfall product is currently being transformed to blend smoothly with the new IMERG data collected by the GPM satellite.
For this thesis, three-hour rainfall data were collected from 1999 to 2014 to match the duration of available river stage data. The location of the selected grid points representing precipitation over the selected sub-basins is shown in Figure 5.10. The average precipitation data in the sub-basin of these grids are shown for each sub-basin in Figures 5.11 to 5.14.
The TRMM data were also compared with observed gauge data at Sylhet and Cherrapunji within the Sunamganj sub-basin, as shown in Figures 5.15 and 5.16. It can be seen that the TRMM data has a tendency to overestimate at the relatively low altitude (35 m above mean sea level) station of.
River Stage Data
ANALYSIS AND RESULTS
Performance Indicators
MAE has the same unit as the variable and as an error measure 0 is the optimum value.
Development of ANN and SVM Models
Finally, the data for use in the ANN models were also split with the same ratio as the SVM models for consistency, i.e.
Selection of Input Variables and Lag Time
The other (Type-B) consisted of previous river stage data, and for consistency, the number of input nodes was kept the same as the corresponding Type-A models. The most recent river stage to be included as input was chosen to be t-48 hours, where t denotes the present model. The maximum time required to record the latest river stage data at the gauging station, send it to the central database and then reach the modeler who uses the model to perform real-time forecasting was assumed to be 48 hours.
For this, the latest river phase at t-48 hours was simply added to the rainfall amounts in the Type-A model. Type-B models are a notable improvement over Type-A models, but tend to underestimate the larger values of river stages. Type-C models showed slightly better performance than Type-B models and therefore Type-C models are the best performing models of the three types.
An interesting feature of the Type-A models is that the models for all sub-basins seem to be mostly unable to simulate river phases below a certain threshold, which in the case of the Muslimpur sub-basin is exactly 5 m. For the remainder of this thesis, the Type-C models will be used, which include both past rainfall and river phase data as input.
Effect of Increasing Lead Time
Comparison between ANN and SVM
But for the Muslimpur and Sunamganj stations, the underestimation of annual peaks between ANN and SVM models is almost the same. This can be a good indicator of the model's ability to predict river stages throughout the year. But since the focus of the thesis is to predict pre-monsoon flash floods, it is necessary to look at a specialized indicator of model performance.
Therefore, the model performances were calculated exclusively for the pre-monsoon season (March-April-May). To present the results, Table 6.4 has been redeveloped as Table 6.5 with model performances of pre-monsoon months given instead of annual performance. The results show that the model performances are slightly poor for the pre-monsoon months.
But most interestingly, the results indicate that, unlike annual performance, SVM models perform better in the pre-monsoon months than ANN models. A possible reason for this could be that the hydrological characteristics of the study area differ significantly between pre-monsoon and monsoon, namely that a significant part of the Upper Meghna Basin is submerged during monsoon, causing the flows to become connected.
CONCLUSIONS AND RECOMMENDATIONS
Conclusions
Although these data could somehow be collected and hydrological modeling performed, physically based models first calculate the discharge in the rivers, which must be converted to river stage using some approximate methods, since flood forecasting is done based on the river stage crossing a predefined hazard level. This conversion of discharge to river stage is likely to introduce additional errors in flood forecasting. A possible alternative is to use data-driven modelling, which works by generating empirical/statistical relationships between rainfall amounts and direct river stage output based on the historical data alone.
This thesis experimented with three types of input data sets: rainfall only, river stage only, and a combination of rainfall and river stage. The results showed that when past rainfall and river stage combined are used as inputs, better forecasting can be performed. This shows that flash flood forecasting can be done with large lead times using data-driven models.
Results show that model performances in pre-monsoon season are slightly lower than the overall annual performances. Results also indicate that SVM models perform relatively better than ANN models in the pre-monsoon season.
Recommendations
To understand how these models perform especially for the pre-monsoon season when flash floods cause the most damage to the Boro crops, the model performances were measured separately only for the months of March, April and May.
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