Abstract:
Groundwater is one of the most precious and significant sources of water in
the world. Understanding the effects of both natural and human-made factors on
groundwater reserves and exploitation is crucial for developing appropriate
management strategies to deal with unsustainable use. The understanding of
groundwater level variability and trend is crucial for water resource planning in a
region. The groundwater level fluctuation is a non-linear phenomenon. Hence,
Artificial Neural Networks (ANN) proves to be one of the best tools for modelling
non-linear relationship between input and output datasets. Once the groundwater
level is modelled, it is easy to assess the groundwater drought conditions of a region
using Standardized Groundwater level Index (SGI). Therefore, systematic
information about likely occurrence and distribution of drought may assist in
preparedness and mitigation of drought disasters.
The present study was conducted in Kalpathypuzha sub-basin of
Bharathapuzha to analyze the variability and trend of groundwater level, to develop
ANN model for groundwater level prediction and to assess the groundwater drought
using Standardised Groundwater level Index (SGI). Twelve observation wells
evenly distributed in the blocks of Kuzhalmannam, Palakkad, Malampuzha and
Chittur were selected. The groundwater level variability was analyzed by various
descriptive statistics such as mean, standard deviation, coefficient of variation,
skewness and kurtosis. The groundwater level trend was estimated using Mann-
Kendall test and Sens slope estimator. ANN models were developed separately for
each well to predict the groundwater level using MATLAB R2016a software. The
input parameters used were precipitation, maximum and minimum temperature and
output data used was groundwater level collected for a period of 15 years from 2007
to 2021. SGI values were estimated for both observed and predicted groundwater
level data to assess the groundwater drought scenario of the study area and to
develop spatio-temporal groundwater drought map. Monthly groundwater level and
drought conditions were predicted for the year 2023 using the developed ANN
model.
Results of trend analysis showed a decreasing pre-monsoon groundwater
level trend in three wells, well 129 of Palakkad block and wells 133 and 142 of
Malampuzha block while decreasing post-monsoon groundwater level trend in well
139 of Chittur block. But there was no trend in all other wells for both pre-monsoon
and post-monsoon. Feed forward ANN models were developed for all the twelve
wells in the study area and the performance indicators correlation coefficient r (0.93
to 0.74), Root Mean Square Error RMSE (0.11 to 0.45 m), and coefficient of
determination R2 (0.87 to 0.69) were found in the acceptable range. The best model
performance for training was for the well PKD S-4 with model configuration 3-10-
1 and r = 0.92 whereas, during testing it was found for the well 129 with model
configuration 3-14-1 and r = 0.93. ANN predicted groundwater level was found in
close agreement with that of the observed groundwater level in this study. Hence
the model developed could be safely and effectively applied in the study area.
The SGI was estimated for pre-monsoon months Jan, Feb, Mar, Apr and May
of the study period from 2007 to 2021 for all the twelve wells as drought was more
severe during these months. SGI values ranged from -3.7 to 1.1 indicated
exceptional to no drought condition in the study area. The computed SGI values
indicated that the years 2013, 2016, 2017 were the severe drought years of the study
area. According to Spatial distribution of SGI values for the years 2013, 2016 and
2017 Chittur and Malampuzha block were the most drought affected areas followed
by Kuzhalmannam and Palakkad block. Hence the study revealed that the majority
of Kapathypuzha sub-basin is drought prone and immediate measures are to be
adopted to prevent the extend of severity in the area.