Integrated ANN Modelling for assesment of Runoff due to Land-Use change using Remote Sensing and GIS
Dr. Madhav Narayan Shrestha
Assistant Manager, Nepal Water Supply Corporation,
Kathmandu, Nepal
Tel: +977-1-271429,
Email: madhavnarayan@yahoo.com
Abstract
An integrated artificial neural network (ANN) model considering spatial variability using remote
sensing, and GIS is developed to assess the changes in runoff value due to land use change in a
hydrological basin. Kathmandu Valley basin, Nepal, is chosen as a basin of case study. It is found that the
average daily monsoon flow is increased by 12% for 9% deforestation and 17% urbanization. Peak flow
value in the basin during monsoon season is found increased by 14%. It is found that the percentage
change in runoff due to land use change is almost constant for different land use irrespective of the rainfall
pattern and time of occurrence. The performance of the network in training and validation using Feed
forward back propagation network (FFBPN) model to predict the runoff from the land-use, soil moisture and
rainfall is found to be quite satisfactory when compared to Recurrent Neural Network (RNN). The total runoff
values for different percentage of urbanization predicted by Distributed Hydrological Model (DHM) and
FFBPN model are found to be close than that of RNN model. If the land-use change and climatic data of a
basin are available for sufficient periods covering all extreme conditions , the FFBPN can be used for
estimation of flows for ungaged periods. During low flow period, RNN models underestimated the runoff
more compared to FFBPN. RNN model will be more appropriate for high flow condition. The study clearly
demonstrated that integration ANN model with remote sensing and GIS, and spatial distributed model
provides a powerful tool for assessment of the hydrological effect due to land use changes.
Introduction
There has been a growing need to quantify the impacts of land use changes on hydrology from the point of
minimising potential environmental impacts. The conventional methods of detecting land use changes are
costly, low in accuracy. Remote sensing because of its capability of synoptic viewing and repetitive
coverage, provides us eful information on land use dynamics. With the development of GIS and remote
sensing techniques, the hydrological catchment models have been more physically based and distributed
to enumerate various interactive hydrological processes considering spatial heterogeneity. The purpose of
this study is to integrate ANN model for assessment of runoff due to land-use change. The non-linear
response of a watershed (in terms of runoff) to rainfall events makes the problem very complicated. In
addition, spatial heterogeneity of various physical and geomorphologic properties of a watershed cannot be
easily represented in physical models. The rainfall-runoff relationship is one of the most complex hydrologic
phenomena due to the tremendous spatial and temporal variability of the watershed characteristics and
unpredictable rainfall pattern. ANN models are capable of mapping this non-linearity.
System for Study
The system considered for the study is Kathmandu Valley basin. The valley is a roughly circular bowl
shaped intramontane basin, of 651 km 2 and lies between 27° 32' N to 27° 49' N and 85° 11' E to 85° 32' E.
Bagmati river is the main river originates from north hill and flows towards south-west and forms a typical
centripetal drainage system. It passes through Chovar gorge, which is the only outlet of the basin.. The
maximum and minimum temperatures are 35 0 C and -2.5 0 C respectively. The rainfall occurs about 80% of
the total annual rainfall during the months of June to September. The average annual rainfall in the basin is
1600 mm. The basin is divided into 14 subbasins considering topography and is shown in Fig.1.
Fig.1. Subbasin M ap of Kathmandu Valley.Fig.Ar ea ( %)
The land use map for the year 1978 is derived from topomaps using Arc/Info. Digital images for 1984
(Landsat TM), 1990 (Landsat TM) and 1996 (Landsat TM) are used to derive the land use maps by digital
image process. Visual image interpretation of satellite data is carried out using an interpretation key
generated through field survey and verifications. The ground checks are made for confirming the land use
units. The spatial database containing information on land use, soil type, topography, hydraulic
characteristics and meteorological information is created using Arc/Info. The hydrological soil group (HSG).map is derived from the soil map whereas subabsin boundary map is derived from the drainage map. The
Thiessen Polygon map is derived using available rain gauge stations. In the study area, the forest
(mountainous) area is about 30% of the total basin area having slop range from 20 to 30%, and remaining
area (70%) is having average slope of 0 to 4%. The map of newly proposed plan with 27 new settlements,
outer ring road and connecting radial roads (KVTDPIC, 1998) is derived as future plan scenario. The future
development consists of 18-km 2 area of settlements, 66 km outer ring road around the foothills, which
covers mostly agricultural land and 20.25 km connecting radial road. Considering the development of
existing built-up pattern on the either side of ring road, 110m widths the settlement area along the outer ring
road and 95 m width along connecting road are considered as future development. The daily and monthly
rainfall record of 9 raingauge stations for period 1965 to 1996 are used. The daily data for five stream
gauging stations, namely Chovar, Gaurighat, Buddhanilkantha, Sundarijal and Tika Bhairab are collected.
Some missing records are filled in considering the correlation structure with other stations. The correlation
coefficients are found in between 0.87 and 0.97.
