Background: Monitoring the phenomenon of flood routing is of great techno-economic interest and a preventive factor for agricultural land and crops protection. Particularly, modelling the phenomenon constitute an essential component. This work presents the development and evaluation of a numerical model for simulating flood routing by optimising the temporal variation of downstream flow in river reaches. The proposed approach employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) as a stochastic, data-driven tool. 
 
Methods: Two model structures were tested; A three-input version ANFIS (3 Var) based on inflow, outflow and the discharge derivative (&part;Q/&part;t) at time t. And an extended four-input version ANFIS (4 Var) that additionally incorporates the derivative at t+&Delta;t. The models were applied to two real-world cases. The first case involved a non-smooth hydrograph from the River Wye in the United Kingdom and the second case concerned the Karun River hydrograph in Iran. 
 
Result: In the first case, the four-input model reduced maximum and mean outflow errors at 7.7% and 1.6%, outperforming both the three-input version and the Grey Wolf Optimizer (GWO) benchmark. For the second case, the four-input model achieved mean outflow error of 3%, again exhibiting a distinct and consistent error-reduction pattern. In both cases, simulated and observed hydrographs were nearly superimposed with an average deviation less than 1.8%, confirming the model&rsquo;s accuracy and robustness. These findings demonstrate that ANFIS(4var) can offers strong predictive capability, high reliability and practical applicability for forecasting and decision-making even for other rivers.

A New Approach for Flood Routing Modelling using a Neuro-fuzzy System &lsquo;For a Contribution to the Protection of Agricultural Land and Crops&rsquo;

Swine live weight is an important aspect in the production of pork products and also to Stockmen, with reference to market costs, feed conversion, and animal health. The objective of this study was to develop a contactless, stress-free method of swine live weight estimation by machine vision technology. This novel approach was based on image processing for features extraction and Adaptive Neuro-Fuzzy Inference System (ANFIS) for modelling. Firstly, the model determines which input combination holds the highest predictive ability, secondly, used the feature combination with the best predictive power to correlate to live-weight. The test results showed that the average relative error of our proposed system was about 3% and a standard deviation of 0.7%. Thus, development of a practical imaging system for swine live weight estimation by the proposed method is feasible.

A New Approach for Flood Routing Modelling using a Neuro-fuzzy System ‘For a Contribution to the Protection of Agricultural Land and Crops’

Swine live weight estimation by adaptive neuro-fuzzy inference system