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3. Prediction of short and long-term droughts using artificial neural networks and hydro-meteorological variables

Author

Hassanzadeh, Yousef, Ghazvinian, Mohammadvaghef, Abdi, Amin, Baharvand, Saman, and Jozaghi, Ali

Subjects
Physics - Atmospheric and Oceanic Physics, Statistics - Machine Learning
Abstract

Drought is a natural creeping threat with numerous damaging effects in various aspects of human life. Accurate drought prediction is a promising step in helping policy makers to set drought risk management strategies. To fulfill this purpose, choosing appropriate models plays an important role in predicting approach. In this study, different models of Artificial Neural Network (ANN) are employed to predict short and long-term of droughts by using Standardized Precipitation Index (SPI) at different time scales, including 3, 6, 12, 24 and 48 months in Tabriz city, Iran. To this end, different combination of calculated SPI and time series of various hydro-meteorological variables, such as precipitation, wind velocity, relative humidity and sunshine hours for years 1992 to 2010 are used to train the ANN models. In order to compare the models performances, some well-known measures, namely RMSE, Mean Absolute Error (MAE) and Correlation Coefficient (CC) are utilized in the present study. The results illustrate that the application of all hydro-meteorological variables significantly improves the prediction of SPI at different time scales.

Published
2020

10. A Comparative Study of the AHP and TOPSIS Techniques for Dam Site Selection Using GIS: A Case Study of Sistan and Baluchestan Province, Iran.

Author

Jozaghi, Ali, Alizadeh, Babak, Hatami, Mohsen, Flood, Ian, Khorrami, Mohammad, Khodaei, Nastaran, and Ghasemi Tousi, Erfan

Subjects
*DAMS, *ARTIFICIAL intelligence, *GEOGRAPHIC information systems
Abstract

The application of multiple criteria decision-making (MCDM) techniques in real-life problems has increased in recent years. The need to build advanced decision models with higher capabilities that can support decision-making in a broad spectrum of applications, promotes the integration of MCDM techniques with applicable systems, including artificial intelligence, and Geographic Information Systems (GIS). The Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are among the most widely adopted MCDM techniques capable of resolving water resources challenges. A critical problem associated with water resource management is dam site selection. This paper presents a comparative analysis of TOPSIS and AHP in the context of decision-making using GIS for dam site selection. The comparison was made based on geographic and water quality criteria. The geographical criteria are geology, land use, sediment, erosion, slope, groundwater, and discharge. The water quality criteria include Soluble Sodium Percentage, Total Dissolved Solid, Potential of Hydrogen, and Electrical Conductivity of water. A ratio estimation procedure was used to determine the weights of these criteria. Both methods were applied for selection of optimal sites for dams in the Sistan and Baluchestan province, Iran. The results show that the TOPSIS method is better suited to the problem of dam site selection for this study area. Actual locations of dams constructed in the area were used to verify the results of both methods. [ABSTRACT FROM AUTHOR]

Published
2018
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