Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm Open Access

More recently, the unit hydrograph concept has been developed to conceptualize the reaction of a catchment to a storm event based on the theory of superposition. The unit hydrograph helps to isolate the runoff of the base flow and storm events from the streamflow (Fowler et al. 2016). Runoff models have become more complex with increased computational capacity and a deeper understanding of hydrological processes (Guo et al. 2017). Modeling runoff helps to understand hydrological processes better and how changes impact the hydrological process (Dakhlaoui et al. 2017). A runoff model is represented as a sequence of equations that help to estimate how much rainfall functions runoff of the different parameters used to characterize the watershed. It can be difficult to model the surface runoff since the measurement is complex and includes several interconnected variables (Sitterson et al. 2018).

A model's overall modules include inputs, control equations, boundary or parameter conditions, model processes, and outputs (Garcia et al. 2017). It requires surface runoff simulation to consider yields of floodplain and reactions, predict water quality, change over time, and forecast. Hydrological Simulation Program-Fortran (HSPF1) is a hydrological model which as part of its management utilizes runoff functions for the prediction of sediment charges, nutrients, pollutants, harmful chemicals, and other concentrations of water quality (Fraga et al. 2019). While there are many ways to classify models, not all models fall into one category as they are designed for a variety of purposes. The water demand has increased as a result of fast population growth, industrialization, and urbanization (Kratzert et al. 2018). It is also one of the prime criteria for farming, domestic, and industrial activities. Nevertheless, the careful preparation and control of water supplies to mitigate property damage and lifelessness due to flooding is becoming very important (Nourani 2017).

According to the Asian Development Bank (ADB), from 1950 to 2011 Pakistan witnessed 21 floods which are mostly attributed to rainfall. Unexpected and strong rainfall contributes to overflowing dams, which are the leading cause of severe flooding (Ledesma & Futter 2017). Streamflow forecasting for a catchment area is one of the well-known and main hydrological variables since hydrological engineering, design, and actions for development and evaluation such as water supply, irrigation, flood control, and hydropower production require information on hydrological catchment stream flows (Song et al. 2019). The modeling of the rainfall-streamflow is considered to be complicated, nonlinear, and time-variable because the streamflow produced from a watershed depends not only on the hydro-meteorological parameters but also on the spatiotemporal irregularities in the features of the watershed and the patterns of rainfall (Xiang et al. 2020). Precisely forecasting streamflow helps to manage different water uses such as irrigation, hydropower development, preparing water delivery policies, and flood risk management (Jaiswal et al. 2020). Hydrologists have developed and used various types of models for modeling hydrological processes such as streamflow forecasting, prediction of rainfall, flood forecasting, and modeling of rainfall–runoffs, etc. (Nourani et al. 2019).

Additionally, the calculation of suspended sediment during a flood caused by heavy rain and cyclone is very difficult. Direct sediment yield measurement is complicated because it takes enough time and energy. Computational hydrology has benchmark work criteria in the sediment modeling area (Knoben et al. 2019). Many variables influence the yield of suspended sediments, and their partnerships with each other are extremely nonlinear and complex. Suspended sediment yields are therefore very difficult to estimate using traditional mathematical models such as multiple linear regression (MLR) and sediment rating curve (SRC) which do not provide satisfactory performance (Weeser et al. 2019). A typical feed-forward neural network has its limitations when it comes to time-series data. Feed-forward neural networks are built on the basis that the examples of training and testing (data points) are independent (Del Giudice & Padulano 2016).

For the efficiency of the spatial-temporal resolution, the entire network state is removed after each example has been processed. When data points are naturally connected, a conclusion is not needed. Besides, a typical artificial neural network (ANN) model (feed-forward) will allow a fixed-sized sliding window over the dataset to handle time-series data (Chlumecký et al. 2017). Tuning the scale of these sliding windows for the highest predictive precision adds additional work to the range of models. In finer time resolution flood assessments, the constraint is increasingly noticeable. Despite some examples of using a recurrent neural network (RNN) for hydrological modeling (Reshma et al. 2018), there is no long short-term memory (LSTM) model that forecasts future river flow solely based on precipitation inputs to address the issue of accumulative uncertainty in the literature. Therefore, to overcome the model errors, the infiltration rate with the soil moisture and steep slopes in the hill is a vital part to calculate in the runoff model for the prediction of daily rainfall. Thus, the research work proposed a novel framework for better prediction of rainfall with a runoff model. The main contribution of the paper is as follows:

• To estimate model errors in the computational runoff, the time-dependent and posterior fire-breathing network is utilized.

• The prophetic multilayer network is directly considering the relationship between infiltration and soil moisture through a collection of parsimonious computational parameters to estimate the infiltration rate.

• By utilizing a well-ordered selective genetic algorithm the velocity of runoff on steep slopes is calculated.

The remainder of the article is structured as follows. In section 2, the literature survey is presented in detail. The proposed rainfall prognostic model-based artificial framework is characterized in Subsection 3. Section 4 follows the result and the output of the proposed work. Finally, the overall work is resolved in Section 5.

Vidyarthi & Chourasiya (2020) introduced the use of the ANN in estimating the runoff of a river. The classical gradient descent (GD) algorithm is the most commonly used algorithm for training the ANN runoff models so far. The achievement of the GD algorithm, however, is affected by changes to get stuck at the local minimum. In the paper, one of the popular evolutionary optimization algorithms, known as particle swarm optimization (PSO), has been explored to train the ANN rainfall–runoff model. However, the accuracy of the models using evolutionary algorithms is low.

Asadi et al. (2019) Proposed Inputs from the hydrogeomorphic and biophysical period series, including the Normalized Vegetation Difference Index (NDVI) and Connectivity Index (IC), were evaluated in addition to climatic and hydrological inputs. They used selected inputs for the creation of ANNs in the catchment of the Haughton River and the catchment of the Calliope River, Queensland. Results show that IC is incorporated as a hydrogeomorphic parameter and NDVI. Besides, the availability of remote sensing land use datasets (i.e., NDVI) and high-resolution feature maps to reflect geomorphologic catchment features may also be a limiting factor.

Kumar et al. (2019) proposed the hydrological implementation of the ANN and the emotional neural network (ENN) for the simulation of rainfall–runoff in some order, Bihar as the area experiences flooding due to heavy rainfall. The ENN is a revised version of the ANN since it contains neural parameters that improve the process of network learning. The selection of inputs for the rainfall–runoff model is a key task. The paper makes use of a cross-correlation study to recognize possible predictors. The principal component analysis (PCA) was then conducted for the collection of data sets showing key patterns on the chosen data sets. The data sets collected after PCA were then used in the creation of the models ENN and ANN. Moreover, tremendous work is needed to estimate the reliability and performance of the models.

Üneş et al.'s (2019) implementation of the rainfall and runoff partnership is very important for the effective use of water supplies and flood prevention. Nowadays various methods of artificial intelligence (AI) techniques are used to assess the relationship between rainfall and runoff. The present research utilizes ANNs. Popular approaches like MLR are also used. The data collected from the US have been integrated into the report. The multi-linear regression and feed-forward back-propagation ANN models used 731 daily rainfalls, runoff, and temperature data to produce input data. However, the lack of evaluation of watershed characteristics creates issues in deciding the relationship between rainfall–runoff results.

Chadalawada et al. (2020) proposed a novel model-building algorithm using the full potential of flexible modeling frameworks by searching for model space and using machine-learning (ML)-based model configurations. The proposed algorithm for ML is based upon an evolutionary approach to computation using genetic programming (GP). Up until now, state-of-the-art GP modeling implementations have used the algorithm as a short-term forecasting tool that produces an expected future time-series very similar to neural network implementations. However, no sufficient observations were provided in the catchment area.

Chen & Chau (2019) conducted uncertainty analysis on the hybrid double feed-forward neural network (HDFNN) model for generating the sediment load prediction interval (PI). They used the LUBE technique, in which the lower and upper bounds are created directly as outputs of neural network-based models. Partitioning analysis demonstrates that the HDFNN model consistently performs well in creating PI for low, medium, and high loads. However, the LUBE approach does not solve highly nonlinear, discontinuous, and non-differentiable optimization problems.

Fotovatikhah et al. (2018) used flood management systems (FMSs) in their research to aid in the decision-making process in crucial scenarios. They looked at AI and computational intelligence (CI) approaches for detecting early flood events with a low false alarm rate. The city administration was able to respond quickly and effectively in post-disaster scenarios using this strategy. They came to the conclusion that using an ensemble CI technique to predict floods was quite effective. However, using an ensemble CI technique is not always the best option. Furthermore, the ensemble method is incapable of addressing unknown discrepancies between the sample and the population.

Amir Mosavi et al. (2018) analyzed the state of the art of ML models in flood prediction. They investigated providing an extensive overview on the various ML algorithms used in the field through a qualitative analysis of robustness, accuracy, effectiveness, and speed.

They found that the effective strategies are hybridization, data decomposition, algorithm ensemble, and model optimization. However, the hybridization method is expensive, costing up to five times the value of the normal process. The data decomposition method, ensemble algorithm, and model optimization techniques suffer if not provided with the normal requirements.

Taormina & Chau (2015) tested the suitability of the LUBE approach in producing PIs at different confidence levels (CLs) for the 6 h ahead streamflow discharges of the Susquehanna and Nehalem Rivers, US. The PSO in LUBE applications, variants of this algorithm had been employed for CWC minimization and they were found to vary substantially depending on the chosen PSO paradigm. The algorithm has a low convergence rate in the iterative process so that advanced algorithm should be utilized for prediction.

Wu & Chau (2013) employed several soft computing approaches for rainfall prediction. They utilized moving average (MA) and singular spectrum analysis (SSA) techniques for preprocessing. The modular model was composed of local support vector regression (SVR) models or/and local ANN models. Modular models involved preprocessing the training data into three crisp subsets (low, medium, and high levels) according to the magnitudes of the training data, and finally two SVRs were performed in the medium- and high-level subsets. Also, ANN or SVR was involved in training and predicting the low-level subset. The ANN-MA displayed considerable accuracy in rainfall forecasts compared with the benchmark, although, the neural network should include back-propagation to increase the speed of the training process.

In Vidyarthi & Chourasiya (2020) the accuracy of the models is low, moreover, in the study by Asadi et al. (2019), availability of remotely sensed datasets has a limiting factor. In Mosavi et al. (2018), the hybridization method costs up to five times the value of the normal process. Data decomposition, ensemble algorithm, and model optimization techniques suffer if not provided with the normal requirements. From the aforementioned issues, it is essential to develop a new framework for the rainfall–runoff model for enhanced prediction.

For modeling the rainfall–runoff process, models have been developed that are based on conceptual representations of the physical processes of the water flow lumped over the entire catchment area (lumped conceptual type of models). The parameters of such models cannot, in general, be obtained directly from measurable quantities of catchment characteristics, and hence model calibration is needed. To calibrate a model, values of the model parameters are selected so that the model simulates the hydrological behavior of the catchment as closely as possible. The process of model calibration is normally done either manually or by using computer-based automatic procedures. In manual calibration, a trial-and-error parameter adjustment is made. In this case, the goodness-of-fit of the calibrated model is based on a visual judgment by comparing the simulated and the observed hydrographs. However, since there is no generally accepted objective measure of comparison, and because of the subjective judgment involved, it is difficult to assess explicitly the confidence of the model simulations. The development of such relationships is an example of a regionalization methodology and is a useful goal for a number of reasons. For example, the construction of a hydrologic structure such as a bridge or dam may require a prediction to be made of the hydrologic response of a catchment at an ungauged point.

Initially, the framework introduced and applied a novel posterior fire-breathing network to estimate model errors of a conceptual rainfall–runoff model and to perform uncertainty analysis. The solution accounts for model errors by adding time-dependent, random noise into the internal storage of the hydrologic model. The model also effectively quantifies variability in the performance and the parameters arising from variance in the input data used to calibrate. Furthermore, the level of runoff is simulated using the Prophetic Multilayer Network technique which is employed to model infiltration. The technique is also found to be related to the input as the exactness of the ANN model was higher for soil types with higher proportions in the training data. The fraction of precipitation converted into either runoff or infiltration depends on the amount of soil moisture, and the dynamics of soil moisture vary according to the model's rural or urban portion. In addition to overcoming runoff at low water depths on steep slopes, the framework proposed a well-ordered selective genetic algorithm (WSGA) for velocity measurements and predictions in different bend sections and comparing their test results with each other and with actual data. Four experimental test cases have been used to explore and evaluate the numerical model and to understand its behavior better. The overall rainfall modeling is given in the following sections.

The network calculates a conceptual rainfall–runoff process's model errors and analyzes uncertainty. The model error assessment assumes that the results of the discharge reflect exactly the hydrological behavior of the catch. However, time-series are successively extracted from stage measurements and conversions of stage ratings and therefore may contain highly uncertain values. Thus, by incorporating time-dependent, spontaneous noise into the internal storage of the hydrological model, the solution accounts for model errors and thus efficiently quantifies uncertainty in model output and parameters resulting from variation in the input data used for calibration. Adoption of the posterior fire-breathing network in driving a rainfall–runoff model reduces the error by estimating the runoff at the catchment outlet with different rainfall inputs from different parts of the catchment. Initially, the noise parameter of the model errors was calculated and revised sequentially through the approximation of the Gamma density to its posterior. With the approach suggested in the article, Gamma approximations were obtained in these graphs, while Gaussian approximations have the same mean and variance as the Gamma approximations. These observations suggest that the posterior is originally asymmetric and non-Gaussian, but as more data is assimilated, it tends to become more Gaussian.

The objective is to recursively estimate posteriors for the model error precision including for the Hymod states and parameters using the posterior fire-breathing network. Each time a major discharge discovery appears accessible it is incorporated by

• updating the posterior of (precision of model errors) and

• updating the states and parameters of Hymod.

It is realized that the model error accuracy posterior is modified separately from the state and parameter posterior. The alternative of introducing the precision as an external unknown to the state vector, i.e., the state-augmentation method, is never considered here. Information on mathematics to update posteriors is given in the following two sections.

The posterior precision or inverse variance of the model errors is calculated sequentially in the system. It is accomplished by preserving a Gamma density approximation of the posterior: as new data become available the approximation is modified in real-time to represent new knowledge on model errors. Thus, the Gamma approximations are used for their corresponding calculations of the accuracy (or variance) of model errors a positive sum. In Bayesian analysis, the Gamma density is a popular approach for modeling precision variables, partially even with its mathematical properties; e.g., a Gamma prior is conjugated to a Linear-Gaussian probability, leading to posterior closed-form expressions. In the article, work with differential equations, so miss conjugacy and the posterior is not accessible analytically, but they can be effectively estimated, as mentioned below. It factors in an approximate but effective recursive approach for determining the posterior of that is completely Bayesian and extends point (posterior mode) estimation of the model noise.

As all terms under the integral are now Gaussian, the double integral can be easily computed (see Appendix A). While this yields a closed-form formula for the posterior, the resulting density does not correspond to a known parametric density. To have an interactive recursive estimation algorithm, i.e., one that makes fast posterior sampling and eliminates the need to repurpose data and likelihoods from specified time measures, it recommends estimating the posterior by a new Gamma density. Under such estimation, the posterior will increase in complexity every time step, for each time steps an additional possibility term is introduced that transforms the posterior into a non-standard dimension. Therefore, the mathematical expression for the posterior increases in complexity even as the posterior is one-dimensional. The Gamma approximation avoids and preserves a reliable estimation of the posterior, and therefore enables recursive updating rather than going back to different results, while still making it simple to analyze the form.

An upgrade to EnKF values for states and parameters outside their physical range may result in Equation (8). With the purpose of preceding the EnKF update, the accompanying restrictions are extended to states and the model parameters with better optimization in the gamma distribution. Furthermore, to approximate the penetration and runoff level the system integrates a prophetic multilayer network which is concise in the following section.

The paper explores whether linking soil moisture and urban runoff will facilitate model simulations that overtake fixed percentage runoff. A modern approach to modeling infiltration and runoff levels across urban surfaces is implemented for the purpose, which directly considers the relationship between infiltration and soil moisture through a collection of parsimonious computational parameters. The models of infiltration are incorporated within a framework of uniform rainfall–runoff. The prophetic multilayer network allows for probability-based predictions and the classification of items into multiple labels. The advantages of a prophetic multilayer network are capability to learn nonlinear models and models in real-time.

The prophetic multilayer network incorporates a deterministic, continuous-time, lumped, computational rainfall–runoff model intended to simulate catchment runoff like the urban land-cover model (URMOD) for sustainable development. A lumped model doesn't break down catchments into different sub-catchments but takes into consideration a single catchment with only one outlet. The hydrological implications of urbanization are directly accounted for by dividing a catchment into a rural and an urban portion, with specific infiltration (and therefore runoff) and routing features allocated to each segment. URMOD has nine parameters requiring calibration of the observed precipitation, possible evaporation, and river flow.

A leveling concept for the amount of evaporation occurring in urban areas is applied. Although there is no agreement on the importance of evaporation in urban areas in modeling studies, importance is agreed to be lower than the total in rural areas and greater than no evaporation. The first urban extension assumes a fixed percentage of infiltrated rainfall, thus de-coupling the hydrological cycle on urban surfaces with soil moisture. The second extension presumes the generation of runoff and infiltration in urban areas are dependent on a scaling term denoted ⁠, the urban surface infiltration depends on the soil moisture content of the rural areas but is decreased by a factor ⁠. It thus links urban infiltration directly to soil humidity levels.

Equation (22) defined for infiltration can then be replaced by the three soil moisture equations. Thus, the infiltration rate in the soil moisture and runoff level is determined successfully in the urban and rural areas. Then the effect of steep slopes in the low depth of hills on the surface water is estimated by the velocity with a novel genetic algorithm, discussed below.

The well-ordered selective algorithm evaluates the importance of a mathematical model to determine the velocity profile in a watershed with rain-induced runoff, using only a displacement rule customized to every disturbance and upwelling regime. More broadly, for the representation of the velocity field, we describe the speed field in each situation and trust the numerical model.

A fully wet state when the water depth is higher than the bottom difference between two cells, and a partially wet regime otherwise, are distinguished at the interface of two cells. Consider the non-dimensional number ; if is inferior to 1 the regime is partially wet and fully wet otherwise. The exact error made on the source term can be estimated. Then the experimental test cases are to be carried out.

Four test cases are selected at different scales and degrees of complexity. The first two laboratory test cases are selected with simple geometry. The first laboratory case is used to evaluate the effect of the resolution method and the other one to assess the effect of Friction Law on local velocities. Then on a small plot, the model is applied to a real case to confirm the results of the two previous test cases. Finally, the model is tested over a watershed of around 1 km² on which the discharge at the outlet can be repeated.

The first experiment is a flume with a constant slope on which a constant raindrop is used to measure the error due to the numerical scheme. The second test case is a furrow-shaped sinusoidal mold reflecting the agricultural land. In comparison with measurements, the conclusions of these simulated results are then represented using two laboratory experiments obtained on this test case. Eventually, the model was applied to a specific catchment in the Laval where only outlet discharge measurements are available.

A steady stream, with an intensity of 25 or 50 mm/hour depending on the chosen scenario, is applied with a stream simulator over the domain over 600 s. With a time phase of 0.1 s the temporal evolution of the discharge at the outlet of the channel is observed. The temporal evolution of the discharge is achieved by measuring the water that flows from the canal. Once the steady-state is achieved, the water depth and the velocities are also determined along the canal every 60 cm. These are calculated using a system of salt-tracing. For the three slope values, the dataset is available: 2, 5, and 25%. All combinations of slope and rain intensity are used except for the case with a slope of 5% and a rain intensity of 50 mm/hour has no data on the event. This result implies that there is convergence in mesh size because whatever the slope and the positive water height defined, a mesh size is adapted in the case where depth is large enough to reduce the error.

The second test case is a laboratory test case describing a real area with several flood-ratio values. Runoff of water depths from 1 to 3 mm at high speeds is observed at the inlet. The flow is then led to furrows where the depths of the water can exceed 2 cm. The test case is representative of the hydraulic change that can be seen on an actual plot with a hydraulic leap between the runoff and the flow in the furrows. The number of furrows ranges from one to three depending on the slope option and the discharge from the inlet. The data on the test case are calculated in a steady-state so that they do not change in time. The water depths and the bottom elevation are determined using a laser scanner with a spatial resolution of 0.5 mm and the experimental water depth uncertainty is 1 mm. Concerning the velocities, they are measured using the LSPIV process, with a 5 mm spatial resolution and a 0.1 m/s experimental uncertainty. The location of the hydraulic jump and the flow in the furrows are in good agreement with the experiments. Indeed, the maximum water depths, as well as the location of the hydraulic jump entrance in the furrows, are very well reproduced. However, particularly in the experiments with three furrows, the water level is a little overestimated in the furrows that are the furthest apart.

The plot is 10 m long and 4 m wide. For the domain, a uniform rain is applied using a rain simulator. Following many rain events that eroded the soil, the mobility of sediments in the soil was considered marginal and the soil is assumed to be fixed for the time of interest. The soil is composed mainly of sand with a ruggedness scale that will be called spatially uniform. Measurements of the velocities were taken at 62 separate locations on the map. As in the first experimental scenario, the measuring technique is salt-tracing. For a plot experiment case, no water level calculation is required as compared to the previous test case. Some anomalies were found on the topography at the edges, which can be attributed primarily to measurement errors.

The model is then put to the test by measuring the effects of the experiment to observed discharges on a particular catchment from field observations. The climate is a system of mountains influenced by the Mediterranean climate, reflected in particular by heavy summer storms and an annual average of 900 mm of rainfall. Its total area is 0.86 km² and 58% of the mean slope. The soil consists primarily of black marls, and 68% of the surface is bare earth. The data are valid for several rainfall events at the catchment source, with a time stage of 60 s. Only the rainfall is measured every 60 s for each case. The flow rate is measured over a specified fixed section by measuring the water depth by laser. The basin size and steep slopes make the hydrological response fast. The choice of these events is motivated by the fact that the initial state of the soil is very different from one to another, but also because they are the two events that have had the greatest impact on the transport of sediments in one year.

Thus, a greater prediction of precipitation is given by the proposed rainfall model-based artificial framework. The method analyses the errors of the model and decreases the uncertainty for the fine discharge of rainfall. The infiltration regulation also comes with the runoff level estimation in the soil moisture. Formerly the model analyzes the velocity from the steep slopes to inhibit the surface water. Thus, the model estimates the predicted total precipitation.

The section clearly explains the feasibility of the proposed method by evaluating and contrasting the experimental results obtained with traditional methods. Specification tools and the dataset description for implementation are given in the following.

The rainfall in India dataset is used for analyzing the rainfall–runoff. It determines the period from 1901 to 2020. Granularity is represented month-wise as January–February, March–May, June–September, October–December and its location is from the north-east. The annual rainfall record for each month is also described. The rainfall unit is expressed as millimeters in the dataset.

The proposed technique is described in Section 3, where a detailed explanation and its performance are analyzed. The proposed method is implemented in the working platform of MATLAB with the following system specification.

Simulation analysis of the proposed rainfall prognostic artificial model framework is presented in the section. Simulated results show the model errors, infiltration rate with runoff level, and steep slopes of the velocity field using the posterior fire-breathing network, prophetic multilayer network, and well-ordered selective genetic algorithm. The several parameters are taken to estimate the performance of the proposed framework such as precision probabilistic discharge with and without error, infiltration rate of soil, velocity of water in different bend areas. Those parameters are considered to estimate accurate day-by-day prediction as well as month-wise prediction. The infiltration rate affects the runoff prediction so that the soil infiltration rate and discharge is estimated.

From the year 1900 to 1920, the average predicted rainfall level gradually increased from 1,025 to 1,500 mm. This is the highest rainfall level marked to date in the dataset. Then the rainfall value is 1,400 mm predicted and gets constant for several years including 1935, 1960, and 1990. The marked average rainfall value for a couple of years is less compared to the early stage. The predicted rainfall value in the prognostic network of the proposed model is approximately 915–930 mm.

Figure 10 shows the evolution of its estimated posterior, with the posterior fire-breathing network which is updated recursively with each new measurement. The posterior fluctuates through time in response to the mismatch between predicted and observed discharge: the value increases during periods of small error and decreases during periods of large error. The behavior corresponds to an automatic EnKF deflation to ensure the discharge of the rainfall without the model errors in predictive and observation distributions. Figure 11 shows without model error the predictive distribution is already quite good, and the predictive distribution with model error becomes too wide when compared to the likelihood. The results in a shift of the posterior find smaller model noise. In the model, without error, the probability is one.

There are three statistical metrics (root mean square error (RMSE), performance coefficient, coefficient of determination) taken to evaluate the performance of the proposed method that allows term-by-term comparison to identify the actual difference between the estimated and measured value to provide information on a model's short-term performance.

To determine the superiority of the proposed method a comparison is performed with various recent hybrid AI framework methods for runoff regions, i.e., hydro-climatic, hydro-climatic + NDVI, hydro-climatic + illumination condition (IC) [24], hydro-climatic + NDVI + IC for the Calliope River catchment [24] and the Haughton River Catchment [24]. The Haughton River Catchment is utilized in this study because it is one of the largest wetland rivers (316 km² or 122 square miles).

Thus it is inferred from the above findings that the proposed approach provides a better prediction in the range of the rainfall–runoff model with the infiltration rate and the runoff level. Accordingly, it concludes that the rainfall prognostic artificial model framework effectively predicts model errors, runoff levels with infiltration rate, and velocity calculation the assistance for the prediction of rainfall in various temperatures.

Despite the progress made in recent years, modeling hydrological reactions to rainfall prediction remains a complex task in runoff modeling. Thus, the presented paper effectively introduced a rainfall prognostic artificial model framework for the prediction of rainfall. The framework applied a posterior fire-breathing network to estimate model errors with random noise to reduce the uncertainty. Further, to regulate the infiltration rate, the system suggested a prophetic multilayer network that analyses the runoff levels with the soil moisture in urban and rural areas. In addition, to tackle the numerical challenge, the model integrates a well-ordered selective genetic algorithm to forecast different bend zones at low water depths on steep slopes. As a result of the rainfall model, everyday rainfall can be accurately anticipated to avoid environmental problems. Experimental results reveal that the framework surpasses existing runoff models with a better infiltration rate of 1.5 cm/hour, runoff level of 0.05 cm, achieved a velocity of 0.45 mm, and has the highest prediction range of 12–20 mm with subjective results.

• In the future, uncertainty of rainfall–runoff model error shall be estimated using a screening method to extract values from stage measurements and conversions of stage ratings. Also, the noise parameter of the model errors may have been calculated by normality and deviation methods to obtain more accurate estimation.

• Unscented EnKF-based measurement of the nonlinear function may be developed to measure the values even from the nonlinear dynamic model.

• Further rainfall–runoff estimation may be developed with some deep learning methods with optimization technique and run off levels of catchment estimated by using the dataset values to evaluate the performance of the method.

• In the future, steep slope run off velocity has to be calculated with various velocity measurement components as well as decision algorithms to find the probability of run off velocity for better prediction results.

All relevant data are available from https://www.kaggle.com/datasets/rajanand/rainfall-in-india.

The authors declare there is no conflict.