Sediment Prediction and Aid to Navigation for the Inland Water Transport in the Indian Ocean Region (IOR)
The National Waterway No. 2 (NW2), is being pursued very aggressively by the Government of India, as an infrastructure project for the development of the North-East, particularly for the State of Assam. It is an extremely ambitious project with plans for multi-modal transport systems and multiple other diplomatic agendas of taking our neighbouring nations on-board the development agenda for the region. Such mega projects raise the stakes for all the stakeholders and demand high-end measures to ensure reliable operational availability at all times.
The NW1 and NW2 are also being connected as part of the trans-boundary Inland Water Transport (IWT) between India and Bangladesh. The figure below provides the details of the Bharat-Bangladesh Protocol Route being planned. Such IWT network will provide an efficient system for multi-modal connectivity in the entire region comprising of India and its neighbours.
The NW2, over the Brahmaputra River has some very unique challenges in terms of being the river that carries the world’s highest sediment load. Both physical and chemical erosion rates are high in the Brahmaputra Basin compared with the world average. The Namche Barwa or Eastern Syntaxis Zone is the major source of sediments and supplies about 45% of the bulk sediment flux from only 20% of mountain area. The sediment deposited in the Brahmaputra varies across its length. At Tsela Dzong in Tibet, it is about 150 tonnes per square km, but as the river crosses the Himalayas and reaches Pasighat at the foothills of Arunachal Pradesh in India, the deposit increases tenfold to 1,495 tonnes per square km. This shows that the river gathers sediments from soft rocks and landslide-affected areas of the Himalayas.
Sediments carried like this, substantially affect the environmental, economic and social aspects of the region. The Inland Water Transport (IWT), project is highly sensitive to such siltation as the navigability of these waterways are severely impacted by such high levels of sediment loads. Thus, effective aid for navigation has to factor this rapid rate of siltation and present way ahead to safe guard the vessels from any damage due to the sediment carry.
The mass of sediment being transported is referred to as ‘total load’ comprising of bed load representing the sediment rolling, sliding or jumping along the bed, where the grains remain in contact with the bed for majority of their transport, the suspended load representing the portion that is not in continuous contact with bed due to turbulent fluctuation of the flow keeping the particles in suspension and finally the wash load representing the very fine particles that are not included in the total mass of sediment transported. Figure-2, pictorially represents the sediment distribution across the three types, accounting for the total transport load.
Typically, the sediment rate is directly proportional to the channel geometry, however for a river like the Brahmaputra the channel geometry itself keeps on changing, making it extremely challenging for effective estimation of the sediment load. The Brahmaputra displays a wide range of morphological variations ranging between steep gorges and wide channels with gentle slopes, probably due to its tectonics driven gradient changes. To have a better understanding of water discharge and sediment load, we need to know the channel geometry. The measurements can be made only at specific points. Also, there exists a limitation on the frequency of performing the aforementioned task. Thus, there is a case to estimate the river channel geometry, which can be undertaken using an Artificial Intelligence (AI) based technique involving an Artificial Neural Network (ANN). The ANN requires sediment grain size and water discharge rate as inputs, and it gives outputs as the channel width (B), the channel depth (H) and the channel slope (S). Figure-3, presents the ANN flow used for estimating river channel geometry. Figure-4, presents the details of the river channel geometry required for computation of the sediment load. The estimation of the river channel geometry is a critical component in this entire formulation and using the AI techniques give a significant advantage in terms of real-time computation and also accuracy of the estimation.
The existing models for estimation of the total sediment load are based on direct and indirect methods. The indirect methods, determine the total sediment load using transport functions based on Einstein’s bed-load function, in which the total sediment load is obtained through sum of the bed load and the suspended load functions. Whereas, the direct method, they make no distinction between the two modes of transports and directly compute the total load. Engelund and Hansen’s approach that depends on the power concept and similarity principle to obtain the sediment transport function.
The estimation of the total sediment load then brings us to the task of aid to navigation for the IWT vessels. The Automatic Identification System (AIS) in the maritime sector has been an extremely critical finding by the Technical Committee of the IMO in the late 90s. Today the AIS data is freely available and multiple researchers have built algorithms for varied applications. The AIS data gives complete static and dynamic inputs on the vessel and its voyage. One critical input required for our application here is the draught of the vessel. Now based on the present draught of the vessel and the sediment load estimation discussed earlier, we have developed a unique Aid to Navigation that will serve as a tool for real-time assessment of the siltation in a specific area and provide early warning to the navigator of the vessel. This is a state-of-the-art, AI based tool that takes real-time inputs on the ground situation and performs extensive computations to derive the real-time sediment load and then maps it on to the draught of the ship for evaluating the navigational hazards.
Multiple aspects of this study were undertaken at MRC, Pune as part of a summer internship project by Mr. Ayush Sharma from BITS Pilani, during his six weeks attachment. He was ably supported by Mr. Shridhar Prabhuraman, research coordinator at MRC. The details of his findings are available at (Webpage| Research Note) and there are two components to his deliverables. One is the research note that summarizes the state-of-the-art in the domain to establish his specific novel contribution and the second is the detailed report that provides his findings with results and analysis. There is scope to take forward this unique effort by him and translate into a tool for defence application, environment monitoring, effective UDA framework realization, blue economic policy formulation, oceanographic studies in the Brahmaputra river and beyond in the IOR.
Dr. (Cdr.) Arnab Das
Director, Maritime Research Center, Pune