javad mahjoobi

It is common to use different data mining methods in drought prediction. However, the selection of the best model is mainly based on the accuracy of the simulation, while most of the studies do not mention the features of the models. In this paper, the performance of the most common data mining models, including Multilayer Perceptron Artificial Neural Network (ANN-MLP), Radial Base Function Neural Network (ANN-RBF), Regression Decision Tree (CART), Model Tree (M5P), and Support Vector Machine (SVM) is evaluated in order to predict monthly one year ahead rainfall at Bandar Abbas synoptic station and then the characteristics of each of them are described. Calibration and validation of the models were done using raw data and a three-year moving average of climatic parameters from 1347 to 1396. The performance of the models has been evaluated using different statistical indices and comparative diagrams. The results showed that the SVM and M5P models have good prediction performance with RMSE of 7.93 and 8.31 mm, the MAE of 3.66 and 4.69 mm, and the CC of 0.83 and 0.82, respectively. Also, with the exception of the CART, the change in the data mining tool makes an eight to 11 percent difference in the accuracy of the estimates. Therefore, the most appropriate model should be selected based on other characteristics of the methods besides their accuracy. In addition, using the three-year moving average of the input parameters has increased the correlation coefficient by about 78 percent and reduced the RMSE by about 63 percent. The analysis of the long-term drought situation showed that with the increase in the period of the standard precipitation index, the separation of wet and dry years becomes more specific.

Run off resulted from rainfall is the main way of receiving water in most parts of the World. Therefore, prediction of runoff volume resulted from rainfall is getting more and more important in control, harvesting and management of surface water. In this research a number of machine learning and data mining methods including support vector machines, regression trees (CART algorithm), model trees (M5 algorithm) and artificial neural networks have been used to simulate rainfall- runoff process in Zayandeh_rood dam basin in Iran. Data used in this research included 9 years of daily precipitation, minimum temperature, maximum temperature, mean temperature, mean relative humidity of daily times 6:30, 12:30 and 18:30 and run off. A number of 3294 lines of data were totally used, and simulations were carried out in two different conditions: without previous run off data as input vectors (M1 condition), and with previous runoff data as input vectors of the models (M2 condition). Results show that machine learning techniques used in this research are not able to present acceptable predictions of runoff in M1 condition (without previous runoff data). However, predictions are considerably improved when previous runoff data are used as input beside other inputs (M2 condition). Between the models used in this research support vector machines (SVM) presented the most accurate results, as the values of RMSE for results presented by SVM, regression tree, model tree and artificial neural network are 2.4, 6.71, 3.2 and 3.04, respectively.

Although a small portion of the Earth's surface is covered by the mountains, but it has a large impact on watershed hydrological perspective Because of the water crisis in arid and semi-arid regions of Iran, monitoring of the amount of snow in these areas is very important. Usually, access to the spatial distribution of snow water equivalent is limited to small scale using sampled data. However, due to the limitations of the mountainous, snow sampling of area is difficult and sometimes impossible in the large basins. Thus, the development of methods in order to estimate snow water equivalent at the un-sampled locations is essential. In this research, an area of 16 ha area in Yazd province was selected and snow water equivalent was measured at 216 points using a Mt. Rose snow sampler. Then the application of artificial neural network method was evaluated using 31 geomorphometric parameters and the digital map of snow water equivalent was obtained. The results showed that the artificial neural network can estimate the snow water equivalent by a R2=0.83 and RMSE= 3.55.The results of the sensitivity analysis are also showed that among the ANN parameters used in the prediction of snow water equivalent, Plan Curvature, Profile Curvature, Curvature, Wind Effect, Slope, Multiresolution ridge top flatness index (MRRTF), Catchment slope and Multi resolution index of valley bottom flatness (MRIVBF) are the effective parameters to predict snow water equivalent, respectively.