A mathematical model for the development of talus slopes was constructed. The model is characterized by the following three points: (1) the balance of the erosional volume of the cliff and the depositional volume of the talus; (2) changing ratio of volumes by the parallel recession of the cliff to volumes by the recession due to the decline slope angle of the cliff; (3) variable angles of the slopes bordering the cliff at the top (slope above the cliff) and at the foot (slope below the cliff). The above second and third points are not clearly included in the previous mathematical models of the slope development. These features are particularly indispensable to apply the model to the development of actual talus slopes. The present model consists of four initial landform factors (slope angle above the cliff, slope angle below the cliff, angle of the initial cliff, and height of the initial cliff) and four variable topographic factors (angle of the talus slope, horizontal retreat distance of the cliff owing to the parallel recession, reduction angle of the cliff owing to the recession due to the slope angle declination, and volume change ratio owing to the cliff-talus transformation). The computational results show the developmental process of the talus slope, and are comparable with field data. The model has, therefore, potential to apply to the development of actual talus slopes, although there is a problem that the retreat rates of the cliff set constant with time.

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Geothermal is a clean and abundant energy resources. For an assessment of geothermal resources, clarification of the temperature distribution and hydrothermal systems in deep parts around hot springs and fumarole manifestations is indispensable. The most reliable data for such an assessment can be obtained by temperature logging. In general, the distribution of geothermal wells is biased, and their temperature data are limited in depth ranges. Therefore, the estimation of temperature from the surface to deep zones is difficult from a well-logging data set. To overcome this problem, we examined a combination of neural networks and geostatistics. A feedforward neural network was used to extrapolate temperature logging data of each geothermal well, and the temperatures estimated at the wells were interpolated using geostatistics. The 22 km × 18 km region in the Hohi geothermal area in central Kyushu, southwest Japan, was chosen as the test site, and temperature data from 20 wells were used in the analyses. The wells were classified into two types, conductive (13 wells) and convective (7), based on their patterns of temperature change with depth. Two networks for conductive and convective type data were trained separately. Binary values, 0 and 1, were assigned to the well locations depending on conductive or convective type. A semivariogram was constructed from the binary data and used in ordinary kriging so that the spatial correlation structure concerning the pattern of vertical changes of temperature can be reflected in the interpolation. It was clarified that the extrapolation of temperature data from the lowest level in a well down to -2,000 m can be appropriately performed at each location in spite of any shortness of the measured depth range. Accordingly, three-dimensional temperature distributions in the study area could be characterized through the proposed method.

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Water bodies are often induced with high concentrations of pollutants due to increased aquaculture and other development activities in marine zones. Remote sensing is an effective technique for monitoring marine environment. This study focuses on determining the appropriate spectral band for monitoring nutrients received from shrimp farms and other aquaculture activities and also from decomposed materials from mangrove forests. The study area considered was Ranong marine zone of western seaboard of Thailand.
Appropriate spectral bands for monitoring major aquatic nutrients like Ammonia Nitrogen, Phosphate Phosphorous, Total Suspended Solids (TSS) and Total Dissolved Solids (TDS) were investigated using band suitability analysis. Spectral characterization of these parameters was done using image acquired by Landsat-5 TM and IRS 1C sensors for two different years: 1993 and 2000. The digital numbers of both images were analyzed for different pollution status. All these pollutant parameters were exponentially related to digital numbers whereas TSS was linearly related. Only TDS had negative and exponential correlation with the digital number. It was found that red, and SWIR are best suitable bands for tracking nutrients and TDS load by remote sensing.

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The method that GIS vector data in DLG option 3 format, open format specified by U.S. Geological Survey, are converted to the lattice data was presented and implemented as a software module for the IRIS Explorer®, the commercial scientific visualization software. This module reads digital geologic map released from the Geological Survey of Japan and enables smooth functionality without losing the merit of quick interactive user interface of the visualization software.

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