Irrigation pipe network design using drone data

Irrigation is the process of applying controlled amounts of water to plants at needed intervals.There are several methods of irrigation. Out of which, the pipe distribution network (PDN) for irrigation purposes is one of the best solutions of water distribution for substantially improving the design and actual overall project efficiency.

In general, in order to attain the best Pipe Distribution Network, the following criteria has to be maintained:

  • Efficient and Effective Pipe Alignment
  • Calculation of Discharge and Head Losses at different Nodes
  • Defining the Optimal Material of Pipe
  • Designing the Pipe (Hydraulic & Structural) as per demand and Residual Head by maintaining standards
  • Proper management and maintenance of Pipe distribution network
  • There are many ways of deriving the terrain (X, Y & Z coordinates). As per the recent studies, many organisations have been using the total station as the main tool of survey to derive the position values of the terrain. But there are many challenges and setbacks are appended while using the surveyed data, which includes lack of information about the terrain, submission of false datasets, no transparency in the field work, huge manpower requirement and time consuming process etc.

    In order to overcome these hurdles, usage of Drone or Lidar for extracting the terrain data is essential and many reports exclaimed the same about the importance of these two types of Surveys.

    As lidar is costlier and with the upgrade in the technologies, nowadays drone surveys have become an efficient way in providing the necessary information. Also, it has been proved in many case studies conducted by different organisations, that the accuracy obtained from the drone is on par with the data obtained from the lidar (except in certain cases like canopy covered regions etc).

    On the same note, in order to check the efficiency of drones in irrigation projects, a case study has been conducted on the alignment and design of the pipe distribution network using drone data and compared with the alignment and design of the pipe distribution network done using total station in conglomeration with satellite data by other organisations and the final outcomes have been explained here.

    The entire work has been subdivided into 5 stages:

  • Establishing the Alignment of the Pipe
  • Hydraulic Design of the Pipe by maintaining the Demand and Pressure
  • Performing Transient Analysis
  • Structural Design of the Pipe
  • Cut and Fill volume estimation with BOQ of the Material and Machinery
  • The Pipe Alignment, cut and fill Volume estimation have been done using relevant GIS tools and the analysis and design has been done using Excel and Bentley Hammer. After conducting the detailed study and analysis, the following major observations have been identified:

    1. While conducting the reconnaissance survey, the pipeline established using TS and satellite data has been passing through more than 40 conflict zones (buildings, temples, crematories etc.) and the same has been rectified using drone data.

    2. During the reconnaissance survey, it has been identified that one of the major river drain of width 15m has been missed by the TS survey and the pipe layout established by TS has been passing through it and no other precautionary measures has been considered which has later been rectified using drone data.

    3. To irrigate a land of 3000Ha, the total main and sub mains pipe length derived from the TS data has come around 43km whereas it was rectified to 18km using drone data, which leads to a reduction in the total tonnage requirement for steel and ductile iron material by 40%.

    4. The Position of Hydro pneumatic tank and Relief Valves have been optimised.

    5. With respect to this, BOQ has also been quantified using Drone data, which leads the project execution much Simpler and Faster.

    6. The total time taken by the drone survey and the subsequent stages involved in pipe alignment and design has been reduced by 38% when compared with total station survey.