---

In the present paper, ANN is used to predict the tidal level fluctuations, which is an important parameter in maritime areas. A time lagged recurrent network (TLRN) was used to train the ANN model. In this kind of networks, the problem is representation of the information in time instead of the information among the input patterns, as in the regular ANN models. Two sets of data were used to test the proposed model. San Francisco Bay tidal levels were used to test the performance of the model as a predictive tool. The second set of data was collected in Gouatr Bay in southeast of Iran. This data set was used to show the ability of the ANN model in predicting and completing of data in a station, which has a short period of records. Different model structures were used and compared with each other. In addition, an ARMA model was used to simulate time series data to compare the results with the ANN forecasts. Results proved that ANN can be used effectively in this field and satisfactory accuracy was found for the two examples. Based on this study, an operational real time environment could be achieved when using a trained forecasting neural network.

---

The correlations and relationships between electrical resistivity and geotechnical parameters of soils have become very important for site investigation. However, there is a lack of understanding about the relationships between electrical resistivity and geotechnical parameter values. The resistivity piezocone penetration tests and laboratory tests have been conducted for geotechnical investigations of marine clay in Jiangsu province of China to establish quantitative relationships between electrical and geotechnical data. The geotechnical investigation reveals that electrical resistivity values are very low for marine clay in Jiangsu, ranging from 5 to 10 Ω m. The correlations between electrical resistivity and geotechnical parameters are examined using Spearman’s rank correlation test that is a rank-based test for correlation between two variables without any assumption about the data distribution. It was shown that the electrical resistivity has strong bonds with the moisture content, void ratio, salt content and plasticity index. In terms of quantitative relationships, good fitting relationships between electrical resistivity and selected geotechnical parameters are observed. The statistical analysis indicates that the electrical resistivity is a good indirect predictor of selected geotechnical parameters. The data studied demonstrates the usefulness of the in situ resistivity method in geotechnical investigations, which have an advantage over other geotechnical methods in cost performance.