Assessment the instability and land degradation using fuzzy methods, hierarchical analysis and weighted linear combination (Case study: Bakhtegan watershed, Fars)
Volume 73, Issue 2, Summer 2020, Pages 321-335
https://doi.org/10.22059/jrwm.2018.252394.1233
Mahroo Dehbozorgi, Mohammad Jafari, Arash Malekian, Gholamreza Zehtabian, seyed rashid fallah shamsi
Abstract In Iran, due to arid and semi-arid weather conditions the optimal use of limited water and soil resources has a particular importance. Land degradation is the result of incorrect policies in land management, which is a prominent example in the Bakhtegan watershed. Human factors, as well as climate changes and the phenomenon of drought in recent years, have caused the area to be severely degraded in terms of water and soil resources and the life of Bakhtegan wetland has been subject to destruction. In this study, it was tried to identify natural and ecological factors as well as human parameters affecting the destruction and vulnerability of the area using fuzzy classification method and hierarchical analysis method (AHP) as well as the capabilities of GIS modeling, the data from the criteria and effective layers have been used and the level of vulnerability and land degradation has been determined. According to the results of the research, the level of land degradation and instability in the area has a large extent (48% of the area), However, the level of vulnerability in the central and downstream regions of the watershed and often in areas affected by human factors such as high population density, consumption of water in the agricultural sector, improper management of land use in the region, as well as dams and dike construction, has been increased. It was also concluded that human parameters have a more significant role in the degradation and vulnerability of the region compared to ecological and environmental factors.
Application of Artificial Neural Networks in Simulating and Forecasting of Meteorological Drought Decile Percentage Index (Case study: Sistan & Balouchestan Province)
Volume 67, Issue 1, Spring 2014, Pages 127-139
https://doi.org/10.22059/jrwm.2014.50834
Arash Malekian, Mahrou Dehbozorgi, Amir Houshang Ehsani, Amir Reza Keshtkar
Abstract Consecutive droughts in Sistan and Baloochestan province cause water resources restriction and this is
a very significant problem for this region. In this study, in order to forecast the drought cycle in 9
climatological stations in the province, we used Artificial Neural Networks. The input data were
average of annual rainfall data in all stations and also deciles precipitation index, which the first 30
years from 1971 to 2000 used for training the network and the last 8 years from 2001 to 2008 for
simulating it. The network consists of Multilayer Perceptron (MLP) and Back Propagation Algorithm
(BP) and also sigmoid transfer function. Number of Neurons in hidden layer was 10 with 1-10-1
structure and was calculated based on the lowest RMSE. Then drought prediction was done in neural
network with the trained algorithm and without using actual and observed data in 2009 to 2012.
Results showed that, the network was able to simulate and forecast DPI index with 97% regression
and average RMSE error less than 5%. According to drought indices, results showed that the drought
will have an increasing trend in all stations in this region in 2009 to 2011. Therefore, by using this
method, drought can be predicted in later years without any need to have actual meteorological data
and also can be used in water resources management, drought management and climate changes.