Leads Connect Services will make indicative flood prone area maps so that the situation in future doesn’t get worse, said, CMD Navneet Ravikar
NOIDA, Uttar Pradesh, Apr 22 (The CONNECT) - Noida based Agri-tech company Leads Connect Services has bagged a prestigious project from West Bengal Irrigation & Waterways Department for assessment and demarcation of flood prone areas of the state.
The project will be executed by using temporal satellite imageries to develop year wise flood inundation maps from historical available data and images. In addition, the objective of the project is also to determine district-wise flood prone area maps corresponding to different flood return periods. It will be conducted in 23 districts of the state for a duration of six months, starting from April 2022.
Navneet Ravikar, Chairman & Managing Director, Leads Connect Services said, West Bengal, being the lowermost riparian State of Ganga-Brahmaputra basins, has both Inter-State catchment areas of 57,624 Sq. Kms. And international catchment areas of 34,252 Sq. Kms., thus has no control over influx from the upper catchment areas. The outfall tidal conditions often makes the situation worse and river bank erosion, coastal erosion, water logging, tidal inundation, cyclones/depressions are common phenomena of the State. As the monsoon rainfall normally starts from June and ends in October, the districts in the southern parts of the State experience heavy precipitation particularly in the months of September and October. Consequently, upon development of low pressure/depressions in the Bay of Bengal during this period, major floods have been noticed to have occurred during this crucial period 1978, 1984, 1991, 2000 etc.
Leads Connect is an analytics company with core focus on Agri-technology driven Data Analysis and Modelling, Risk Management & Financial Services.
“Through this assessment project, we intend to make indicative flood prone area maps so that the situation in future doesn’t get worse and proactive measures can be taken,” Ravikar said.
The company is not only focused on agritech but has been deeply involved in rigorous research and development activities on climate change and disaster management.
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He emphasized on the need for a full-fledged and core research and development activities on various aspects of the earth system spanning from agriculture, climate change, hazard analytics to disaster management. Such a comprehensive assessment of the earth system may be helpful in understanding the impact of one component to another. This understanding may help in developing sustainable frameworks for the future.
Prior bagging this project, the organization has worked with nodal agencies such as Mahalanobis National Crop Forecast Centre (MNCFC), Agriculture Insurance Company (AIC), and NABARD Consultancy Services (NABCONS) for research-based project related to yield estimation, crop cutting experiments (CCE) and mapping agricultural infrastructures, respectively.
Currently, Leads Connect is engaged in delivering projects in more than 100 districts of India and has a pan-India presence backed by a comprehensive presence of a field team and well equipped research lab. The research lab of the company comprises interdisciplinary skill sets, expertise spanning from Agriculture, Remote Sensing–GIS, Numerical Modeling, Data Analytics and Artificial Intelligence.
Leads Connect has already been engaged in research and development activities pertaining to climate change and hazard analytics, disaster management, biodiversity analytics, landscape and urban analytics and health analytics, using cutting edge technologies such as satellite Remote Sensing Analytics and Machine Learning / Deep-tech Intelligence since the year 2018. The company is in collaboration with the AIR Institute, Spain for studying biophysical impacts of climate change in the North-East Region of India. It is also associated with Birla Institute of Technology (BIT), Mesra-Jharkhand, a premiere institute of the country for performing study on assessing environmental dynamics of Jharkhand and subsequently for developing a machine learning (ML) model for construing intrinsic characteristics of environmental dynamics.
