A composite fall-slippage model for cliff recession in the sedimentary coastal cliffs

A composite fall-slippage model is proposed in this study for the Tertiary sedimentary coastal cliffs of Varkala in the western coastal tract of Peninsular India which are retreating landwards due to the combination of several factors.The fall model in the present study accounts both spring seepage and wave action,resulting in undercutting and this fall affects only the topmost laterite and the just below sandstone in the cliff.Slippage in this area affects all the litho-units and hence the geologic characteristics of all the litho-units are considered for developing the slippage model.This mathematically derived model can be used in other cliffs exhibiting the same morphology as well as the one controlled by the same influencing factors.This model differs from other models in incorporating multi-lithounits as well as multi-notches.Varkala cliffs form a part of the aspiring geopark in the Global Geopark Network and hence a study on the cliff recession is a pressing requirement.

Prediction of Seaward Slope Recession in Berm Breakwaters Using M5＇ Machine Learning Approach

In the design process of berm breakwaters, their front slope recession has an inevitable rule in large number of model tests, and this parameter being studied. This research draws its data from Moghim＇s and Shekari＇s experiment results. These experiments consist of two different 2D model tests in two wave flumes, in which the berm recession to different sea state and structural parameters have been studied. Irregular waves with a JONSWAP spectrum were used in both test series. A total of 412 test results were used to cover the impact of sea state conditions such as wave height, wave period, storm duration and water depth at the toe of the structure, and structural parameters such as berm elevation from still water level, berm width and stone diameter on berm recession parameters. In this paper, a new set of equations for berm recession is derived using the M5＇ model tree as a machine learning approach. A comparison is made between the estimations by the new formula and the formulae recently given by other researchers to show the preference of new M5＇ approach.

A Physicoempirical Model for Soil Water Simulation in Crop Root Zone

To predict soil water variation in the crop root zone, a general exponential recession （GER） model was developed to depict the recession process of soil water storage. Incorporating the GER model into the mass balance model for soil water, a GER-based physicoempirical （PE-GER） model was proposed for simulating soil water variation in the crop root zone. The PE-GER model was calibrated and validated with experimental data of winter wheat in North China. Simulation results agreed well with the field experiment results, as well as were consistent with the simulation results from a more thoroughly developed soil water balance model which required more detailed parameters and inputs. Compared with a previously developed simple exponential recession （SER） based physicoempirical （PF^SER） model, PE-GER was more suitable f0r application in a broad range of soil texture, from light soil to heavy soil. Practical application of PE-GER showed that PE-GER could provide a convenient way to simulate and predict the variation of soil water storage in the crop root zone, especially in case of insufficient data for conceptual or hydrodynamic models.