Abstract:
Soil moisture is important for the survival of plants and living organisms in the soil. It
plays an important role in the movements of minerals and nutrients in the soil surface.
Additionally, it is important in understanding the land surface processes such as soil
erosion, evaporation, soil topography and runoff generation [2]. However, the
availability of water has been greatly affected by prolonged and often occurring
drought events caused by rapid changes in the climatic conditions, Land Use and Land
Cover (LULC) and socio-economic systems in ecosystems. This has led to loss of crop
106
harvest and vegetation, causing loss of human lives, death of livestock and wildlife. This
study seeks to develop a novel soil moisture content index under the influence of
droughts. The novel index could be used to detect and monitor soil moisture and form
an early warning system in arid and semi-arid ecosystems. Muringato basin located in
the Upper Tana River basin in Nyeri County in Kenya will be used as the test site.
Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) deep learning
algorithm will be utilised to develop the index through linear and non-linear regression,
fitting, iterations and adjustments. The algorithm is preferred due to its ability to handle
both non-linear and linear data depicting seasonality and cyclic nature, multiple variable
consideration and provide reliable results [1]. The input variables will be Biophysical
Composition Index (BCI), Topographic Wetness Index (TWI), Solar Incidence Angle
Index (SIAI), Bareness Index (BI), Normalized Difference Vegetation Index (NDVI), and
a multiple combined drought index made up of the Standardized Precipitation Index
(SPI), Streamflow Drought Index (SDI), Evaporative Stress Index (ESI), and Waters
Supply Capacity Index (WSCI) [1], [3]. The expected results will be a comprehensive
soil moisture index capable of giving reliable soil moisture simulations especially in dry
environments. The novel soil moisture content index will be of benefit to the local
community and the scientific world as it will foster droughts monitoring, inform policies
and measures towards food security and irrigation. Additionally, it presents a perfect
application of the artificial intelligence in environment monitoring and conservation.
