When scattering intensity is plotted against the dimensionless quantity qR, where q is the magnitude of the scattering wave vector and R is the radius of the particle, in log-log scale the scattering curve shows a power-law structure which defines characteristic crossovers. This work reveals some new relationships between the power-law structure and the particle properties. In this work, computer simulation results from T-matrix, Mie theory, and discrete dipole approximation methods are used to study the far field intensity and the internal field of the particles. Scattering by both weakly and strongly refractive particles are studied. For weakly refractive randomly oriented spheroidal particles, how the phasor cancellation-based tip volume method can be applied to predict the scattering envelope is demonstrated. The relationship between backscattering enhancement and the curvature of the weakly refractive particles is explained. In strongly-refractive particles when the phase shift parameter is high, regions with higher field amplitudes start to appear. These regions are recognized as the hot spot regions. In this work, a proper definition is given to the hot spot region. The relationships between the hot spot region and the power-law structure, between the hot spot region and the particle morphology, and between the power-law structure and the particle morphology are extensively studied for scattering by spherical particles. A new semi-quantitative phasor analysis method is introduced, and the new method is used with color-coded phasor plots to identify how different regions of the particle contribute to the scattering pattern to get an insight into the physics behind the scattering. How different regions of the particle contribute to the second crossover (SC) and the backscattering enhancement is presented. Relationships between the SC, particle size, and relative refractive index of the particle are derived. It was identified that the scattering angle at the SC depends only on the relative refractive index of the particle. How the findings of this work can be applied to solve the inverse electromagnetic scattering problem for a single non-absorbing spherical particle is also discussed.

Yes / The transfer of suspended sediment can range widely from being diluted to being hyperconcentrated, depending on the local flow and ground conditions. Using the Rouse model and the Kundu and Ghoshal (2017) model, it is possible to look at the sediment distribution for a range of hyper-concentrated and diluted flows. According to the Kundu and Ghoshal model, the sediment flow follows a linear profile for the hyper-concentrated flow regime and a power law applies for the dilute concentrated flow regime. This paper describes these models and how the Kundu and Ghoshal parameters (linear-law coefficients and power-law coefficients) are dependent on sediment flow parameters using machine-learning techniques. The machine-learning models used are XGboost Classifier, Linear Regressor (Ridge), Linear Regressor (Bayesian), K Nearest Neighbours, Decision Tree Regressor, and Support Vector Machines (Regressor). The models were implemented on Google Colab and the models have been applied to determine the relationship between every Kundu and Ghoshal parameter with each sediment flow parameter (mean concentration, Rouse number, and size parameter) for both a linear profile and a power-law profile. The models correctly calculated the suspended sediment profile for a range of flow conditions ( 0.268 𝑚𝑚𝑚𝑚 ≤ 𝑑𝑑50 ≤ 2.29 𝑚𝑚𝑚𝑚, 0.00105 𝑔𝑔 𝑚𝑚𝑚𝑚3 ≤ particle density ≤ 2.65 𝑔𝑔 𝑚𝑚𝑚𝑚3 , 0.197 𝑚𝑚𝑚𝑚 𝑠𝑠 ≤ 𝑣𝑣𝑠𝑠 ≤ 96 𝑚𝑚𝑚𝑚 𝑠𝑠 , 7.16 𝑚𝑚𝑚𝑚 𝑠𝑠 ≤ 𝑢𝑢∗ ≤ 63.3 𝑚𝑚𝑚𝑚 𝑠𝑠 , 0.00042 ≤ 𝑐𝑐̅≤ 0.54), including a range of Rouse numbers (0.0076 ≤ 𝑃𝑃 ≤ 23.5). The models showed particularly good accuracy for testing at low and extremely high concentrations for type I to III profiles.

Yes / During flooding, the suspended sediment transport usually experiences a wide-range of dilute to hyper-concentrated suspended sediment transport depending on the local flow and ground con-ditions. This paper assesses the distribution of sediment for a variety of hyper-concentrated and dilute flows. Due to the differences between hyper-concentrated and dilute flows, a linear-power coupled model is proposed to integrate these considerations. A parameterised method combining the sediment size, Rouse number, mean concentration, and flow depth parameters has been used for modelling the sediment profile. The accuracy of the proposed model has been verified against the reported laboratory measurements and comparison with other published analytical methods. The proposed method has been shown to effectively compute the concentration profile for a wide range of suspended sediment conditions from hyper-concentrated to dilute flows. Detailed com-parisons reveal that the proposed model calculates the dilute profile with good correspondence to the measured data and other modelling results from literature. For the hyper-concentrated profile, a clear division of lower (bed-load) to upper layer (suspended-load) transport can be observed in the measured data. Using the proposed model, the transitional point from this lower to upper layer transport can be calculated precisely.