Machine-learning algorithms for forecast-informed reservoir operation (FIRO) to reduce flood damages

Smartphone digital image colorimetry couple with chemometric approach for determination of boron in nuts prior to deep eutectic solvent liquid-liquid microextraction: a first application of hybrid chemometrics in SDIC

In this research, a green approach utilizing deep eutectic solvent liquid-liquid microextraction is combined with smartphone digital image colorimetry for the determination of boron in nut samples. A smartphone camera was used to capture the image of the analyte extract located in a custom-made colorimetric box. Using ImageJ software, the images were split into RGB channels, with the green channel identified as the optimum. The distance between the cuvette containing the analyte extract and the detection camera was determined to be 8 cm, while the brightness of the light source was 30%. All the images were obtained at 585 nm monochromatic light positioned as a background source. The extraction was achieved with 450 µL of a 1:4 choline-chloride to phenol mole ratio within 60 s and another minute of centrifugation. The limits of detection and quantification were found to be 0.02 and 0.06 µg mL-1, respectively. The method linearity, as indicated by the relative coefficient, was greater than 0.9955 and the relative standard deviations were below 5.4%. Lastly, the application of chemometrics in the form of artificial intelligence (AI)-based models and hybrid machine learning methodologies has been incorporated with SDIC for the quantitative simulation of SDIC parameters. The results gathered showed that these models are capable of predicting the quantitative SDIC parameters.

A coupled optimized hedging rule-based reservoir operation and hydrodynamic model framework for riverine flood risk management

Long-term changes in reservoir inflow due to climate change and human interferences violate the assumptions of hydrologic stationarity, especially in the reservoir operation during high flood season for managing the downstream critical levee (DCL) sections from overtopping. Utilization of uncertain inflow forecast into a reservoir using the operating rule curve of certain forecast horizon reflects the challenges imposed by nonstationary conditions, downstream flood intensification with spatiotemporally distributed lateral flux and floodplain dynamics. Addressing these issues, this study develops four hierarchical frameworks considering single-stage hedging (1SH) and two-stage hedging (2SH) rules-based reservoir operation models optimized with Particle Swarm Optimization (PSO) and informed with rating curve uncertainty at DCL section. Further, these two frameworks are coupled with HEC-RAS-2D (H2D) hydrodynamic model to reduce the existing flood risk at DCL section. The efficiency of the advocated 1SH-PSO, 2SH-PSO, 1SH-PSOH2D and 2SH-PSOH2D are tested in the Rengali reservoir on the Brahmani River in eastern India. The inflow forecasts into the reservoir are simulated by the coupled SWAT-Pothole and Wavelet-based Bidirectional Long-Short-Term Memory (WBiLSTM) models forced with the bias-corrected GFS weather forecasts with up to 10 days' lead-times. The results demonstrate that the best-performing 2SH-PSOH2D framework-based reservoir operation could reduce the average peak flow depth at the DCL station by 21 % from the baseline with an average reduction in levee failure risk by 22.28 % leading to effective management of high flood events. This advocated framework could be used in other reservoir systems worldwide in reducing the downstream flood hazards through enhanced reservoir operation.

Road urban planning sustainability based on remote sensing and satellite dataset: A review

Infrastructural development and urbanization effects have been investigated over the past decades with novel approaches and adaptation strategies. Road network expansions are more useful for the socio-economic development from urban to rural areas where 75 % of the passenger, and goods transportation sectors are influenced by the road. Road infrastructure and urbanization are perpendicular to each other, and this research investigation indicates that the novel approaches and adaptation strategies for road infrastructure and urbanization effects. This study evaluated the trend in the road network and urbanization-related literature from 2010 to 2022 with some measurable keywords. Around 370 pieces of research literature are analysis and around 85 research evaluations for the road network and urbanization-related Land use and land cover (LULC) studies while numerous road network analysis approaches and LULC-related investigations are evaluated in this research. Three major parts road network analysis-related approaches, LULC, and urbanization-related approaches related to road network expansion and urbanization, were investigated. In this work, many research publications' approaches to LULC simulation, kernel density, shortage distance, and picture classification are discussed and assessed. The survey is more valuable for urban planners, future disaster management teams, and administrators to implement the shortage distance analysis, reduction of road accidents, and urbanization effects on the environment.

Enhancing flood risk mitigation by advanced data-driven approach

Flood events in the Sefidrud River basin have historically caused significant damage to infrastructure, agriculture, and human settlements, highlighting the urgent need for improved flood prediction capabilities. Traditional hydrological models have shown limitations in capturing the complex, non-linear relationships inherent in flood dynamics. This study addresses these challenges by leveraging advanced machine learning techniques to develop more accurate and reliable flood estimation models for the region. The study applied Random Forest (RF), Bagging, SMOreg, Multilayer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using historical hydrological data spanning 50 years. The methods involved splitting the data into training (50-70 %) and validation sets, processed using WEKA 3.9 software. The evaluation revealed that the nonlinear ensemble RF model achieved the highest accuracy with a correlation of 0.868 and an root mean squared error (RMSE) of 0.104. Both RF and MLP significantly outperformed the linear SMOreg approach, demonstrating the suitability of modern machine learning techniques. Additionally, the ANFIS model achieved an exceptional R-squared accuracy of 0.99. The findings underscore the potential of data-driven models for accurate flood estimating, providing a valuable benchmark for algorithm selection in flood risk management.

Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracy

This research explores machine learning algorithms for reservoir inflow prediction, including long short-term memory (LSTM), random forest (RF), and metaheuristic-optimized models. The impact of feature engineering techniques such as discrete wavelet transform (DWT) and XGBoost feature selection is investigated. LSTM shows promise, with LSTM-XGBoost exhibiting strong generalization from 179.81 m3/s RMSE (root mean square error) in training to 49.42 m3/s in testing. The RF-XGBoost and models incorporating DWT, like LSTM-DWT and RF-DWT, also perform well, underscoring the significance of feature engineering. Comparisons illustrate enhancements with DWT: LSTM and RF reduce training and testing RMSE substantially when using DWT. Metaheuristic models like MLP-ABC and LSSVR-PSO benefit from DWT as well, with the LSSVR-PSO-DWT model demonstrating excellent predictive accuracy, showing 133.97 m3/s RMSE in training and 47.08 m3/s RMSE in testing. This model synergistically combines LSSVR, PSO, and DWT, emerging as the top performers by effectively capturing intricate reservoir inflow patterns.

Development of High-Precision Urban Flood-Monitoring Technology for Sustainable Smart Cities

Owing to rapid climate change, large-scale floods have occurred yearly in cities worldwide, causing serious damage. We propose a real-time urban flood-monitoring technology as an urban disaster prevention technology for sustainable and secure smart cities. Our method takes advantage of the characteristic that water flow is regularly detected at a certain distance with a constant Doppler velocity within the radar observation area. Therefore, a pure flow energy detection algorithm in this technology can accurately and immediately detect water flow due to flooding by effectively removing dynamic obstacles such as cars, people, and animals that cause changes in observation distance, and static obstacles that do not cause Doppler velocities. Specifically, in this method, the pure flow energy is detected by generating a two-dimensional range-Doppler relation map using 1 s periodic radar observation data and performing statistical analysis on the energy detected on the successive maps. Experiments to verify the proposed technology are conducted indoors and in real river basins. As a result of conducting experiments in a narrow indoor space that could be considered an urban underpass or underground facility, it was found that this method can detect flooding situations with centimeter-level accuracy by measuring water level and flow velocity in real time from the time of flood occurrence. And the experimental results in various river environments showed that our technology could accurately detect changes in distance and flow speed from the river surface. We also confirmed that this method could effectively eliminate moving obstacles within the observation range and detect only pure flow energy. Finally, we expect that our method will be able to build a high-density urban flood-monitoring network and a high-precision digital flood twin.

Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions

Evapotranspiration is one of the most important hydro-climatological components which directly affects agricultural productions. Therefore, its forecasting is critical for water managers and irrigation planners. In this study, adaptive neuro-fuzzy inference system (ANFIS) model has been hybridized by differential evolution (DE) optimization algorithm as a novel approach to forecast monthly reference evapotranspiration (ET0). Furthermore, this model has been compared with the classic stochastic time series model. For this, the ET0 rates were calculated on a monthly scale during 1995–2018, based on FAO-56 Penman–Monteith equation and meteorological data including minimum air temperature, maximum air temperature, mean air temperature, minimum relative humidity, maximum relative humidity & sunshine duration. The investigation was performed on 6 stations in different climates of Iran, including Bandar Anzali & Ramsar (per-humid), Gharakhil (sub-humid), Shiraz (semi-arid), Ahwaz (arid), and Yazd (extra-arid). The models’ performances were evaluated by the criteria percent bias (PB), root mean squared error (RMSE), normalized RMSE (NRMSE), and Nash-Sutcliff (NS) coefficient. Surveys confirm the high capability of the hybrid ANFIS-DE model in monthly ET0 forecasting; so that the DE algorithm was able to improve the accuracy of ANFIS, by 16% on average. Seasonal autoregressive integrated moving average (SARIMA) was the most suitable pattern among the time series stochastic models and superior to its competitors, ANFIS and ANFIS-DE. Consequently, the SARIMA was suggested more appropriate for monthly ET0 forecasting in all the climates, due to its simplicity and parsimony. Comparison between the different climates confirmed that the climate type significantly affects the forecasting accuracies: it’s revealed that all the models work better in extra-arid, arid and semi-arid climates, than the humid and per-humid areas.

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.

Urban Flood-Risk Assessment: Integration of Decision-Making and Machine Learning

Urban flood-risk mapping is an important tool for the mitigation of flooding in view of continuing urbanization and climate change. However, many developing countries lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can help managers and decision makers to combine existing data with more soft semi-subjective data, such as citizen observations of flood-prone and vulnerable areas in view of existing settlements. Thus, we present an innovative approach using the semi-subjective Analytic Hierarchy Process (AHP), which integrates both subjective and objective assessments, to help organize the problem framework. This approach involves measuring the consistency of decision makers’ judgments, generating pairwise comparisons for choosing a solution, and considering criteria and sub-criteria to evaluate possible options. An urban flood-risk map was created according to the vulnerabilities and hazards of different urban areas using classification and regression-tree models, and the map can serve both as a first stage in advancing flood-risk mitigation approaches and in allocating warning and forecasting systems. The findings show that machine-learning methods are efficient in urban flood zoning. Using the city Rasht in Iran, it is shown that distance to rivers, urban drainage density, and distance to vulnerable areas are the most significant parameters that influence flood hazards. Similarly, for urban flood vulnerability, population density, land use, dwelling quality, household income, distance to cultural heritage, and distance to medical centers and hospitals are the most important factors. The integrated technique for both objective and semi-subjective data as outlined in the present study shows credible results that can be obtained without complicated modeling and costly field surveys. The proposed method is especially helpful in areas with little data to describe and display flood hazards to managers and decision makers.

Developing an Optimized Policy Tree-Based Reservoir Operation Model for High Aswan Dam Reservoir, Nile River

The impacts of climate change on the Nile River and Grand Ethiopian Renaissance Dam (GERD) along with the increased water demand downstream suggest an urgent need for more efficient management of the reservoir system that is well-informed by accurate modeling and optimization of the reservoir operation. This study provides an updated water balance model for Aswan High Dam Reservoir, which was validated using combined heterogeneous sources of information, including in situ gauge data, bias-corrected reanalyzed data, and remote sensing information. To investigate the future challenges, the spatial distribution of the annual/seasonal Aswan High Dam Reservoir surface air temperature trends over the period from 1979 to 2018 was studied. An increase of around 0.48 °C per decade in average annual temperature was detected, a trend that is expected to continue until 2100. Moreover, a set of machine learning models were developed and utilized to bias-correct the reanalyzed inflow and outflow data available for Aswan High Dam Reservoir. Finally, a policy tree optimization model was developed to inform the decision-making process and operation of the reservoir system. Results from the historical test simulations show that including reliable inflow data, accurate estimation of evaporation losses, and including new regulations and added projects, such as the Toshka Project, greatly affect the simulation results and guide managers through how the reservoir system should be operated in the future.