Machine learning techniques for urban flood risk assessment

Abstract Floods can cause severe damage in urban environments. In regions lacking hydrological and hydraulic data, spatial urban flood modeling and mapping can enable city authorities to predict the intensity and spatial distribution of floods. These predictions can then be used to develop effective flood prevention and management plans. In this doctoral thesis, flood inventory data for Mazandaran, Iran were prepared based on historical and field survey data from the Sari and Amol municipalities and the regional water company. Flood risk maps were produced using several machine learning (ML) algorithms: GARP, QUEST, RF, j48DT, CART, LMT, ANN-SGW, SVM, MAXENT, BRT, MARS, GLM, GAM, Ensemble, MLPNN, and MultiB-MLPNN models. The flood influencing factors used in modeling were precipitation, slope, curve number, distance to river, distance to channel, depth to groundwater, land use, and elevation. Two equal sets of points were identified randomly for both categories of flooded and non-flooded areas. Therefore, 113 (for Sari city) and 118 (for Amol city) locations for each category were identified. Each set is divided into training (70%) and testing (30%) groups. The flood locations were assigned a value of 1, and non-flood locations were assigned a value of 0. Different conditioning factors, including urban density, quality of buildings, age of buildings, population density, and socio-economic conditions were considered to analyze urban flood vulnerability. Several confusion matrix criteria were applied to evaluate the accuracy of the ML algorithms. The results demonstrated that the ANN-SGW (as the optimized model), GARP (as the standalone model), Ensemble (BRT, MARS, GLM, and GAM), and MultiB-MLPNN models (as the hybridized model) had the highest performance accuracy, with area under the curve (AUC) values of 0.963, 0.935, 0.925, and 0.847 respectively. The results also indicated that distance to channel played a major role in flood hazard determination, whereas population density was the most important factor in terms of urban flood vulnerability. These findings demonstrate that machine learning models can support flood risk mapping, especially in areas where detailed hydraulic and hydrological data are not available.Tiivistelmä Tulvat voivat aiheuttaa vakavia vahinkoja kaupunkiympäristössä. Alueilla, joista hydrologisia ja hydraulisia tietoja ei ole kattavasti saatavilla, kaupunkitulvien alueellinen mallinnus ja kartoitus avaavat mahdollisuuden viranomaisille arvioida tulvien alueellista jakautumista ja voimakkuutta. Mallinnus auttaa päätöksentekijöitä kehittämään toimivia tulvien ehkäisy- ja hallintasuunnitelmia. Tässä tutkimuksessa tulvainventointitiedot laadittiin Sarin ja Amolin kuntien sekä Iranin Mazandaranin vesiyhtiön historiallisten ja kenttätutkimusten tietojen perusteella. Tulvariskikarttoja tuotettiin useilla koneoppimisalgoritmeillä: GARP, QUEST, RF, j48DT, CART, LMT, ANN-SGW, SVM, MAXENT, BRT, MARS, GLM, GAM, Ensemble, MLPNN, ja MultiB-MLPNN mallit. Mallinnuksessa käytettyjä tulviin vaikuttavia tekijöitä olivat sadanta, maanpinnan kaltevuus, käyrän numero, etäisyys jokeen, etäisyys kanavaan, etäisyys pohjaveden pintaan, maankäyttö ja maanpinnan korkeus. Kaksi samanlaista pistejoukkoa tunnistettiin satunnaisesti sekä tulvivalla että tulvattomalla alueella ja siksi kullekin luokalle tunnistettiin 113 (Sarin kaupunki) ja 118 (Amolin kaupunki) sijaintia. Jokainen sarja on jaettu koulutusryhmiin (70 %) ja testausryhmiin (30 %). Tulvapaikoille määritettiin arvo 1 ja tulvattomille arvo 0. Kaupunkien tulvahaavoittuvuuden analysoinnissa arvioitiin erilaisia tekijöitä, kuten rakennustiheys, rakennusten laatu, rakennusten ikä, väestötiheys ja sosioekonomiset olosuhteet. ML-algoritmien tarkkuuden arvioimiseksi käytettiin useita sekaannusmatriisikriteerejä. Tulokset osoittivat, että ANN-SGW (optimoitu malli), GARP (erillisenä mallina), yhdistelmä-ensemble (BRT, MARS, GLM ja GAM) ja MultiB-MLPNN-mallit (hybridimallina) tuottivat muita paremman suorituksen tarkkuuden, AUC=0.963, AUC=0.935, AUC=0.925 ja AUC=0.847, edellä mainitussa järjestyksessä. Tulokset osoittivat myös, että etäisyys kanavaan oli tärkeässä asemassa tulvariskien määrittämisessä, kun taas väestötiheys oli haavoittuvuuden kannalta tärkein tekijä. Nämä havainnot osoittavat, että koneoppimismallit voivat auttaa tulvariskikartoituksessa erityisesti alueilla, joilla yksityiskohtaisia hydrauliikka- ja hydrologisia tietoja ei ole saatavilla

Variation in physical characteristics of rainfall in Iran, determined using daily rainfall concentration index and monthly rainfall percentage index

Abstract Variations in rainfall characteristics play a key role in available water resources for a country. In this study, spatial and temporal variations in rainfall in Iran were determined using the daily rainfall concentration index (DRCI) and monthly rainfall percentage index (MRPI), based on 30-year (1987–2016) daily precipitation records from 80 meteorological stations throughout Iran. The results showed that MRPI differed between locations within Iran, with increasing or decreasing trends observed in different areas. The highest significant decreasing trend in MRPI (3–7% per decade) was found for March rainfall in western Iran, and the highest increasing trend in MRPI (3–7% per decade) for November rainfall in eastern and southern Iran. The DRCI values obtained varied from 0.57 to 0.71, indicating moderate and high rainfall concentrations, with the highest DRCI values in coastal zones of Iran near the Caspian Sea and the Persian Gulf. Trend analysis showed increasing trends in DRCI values at 80% of meteorological stations, and these trends were significant at 37% of those stations

A scenario-based approach for assessing the hydrological impacts of land use and climate change in the Marboreh watershed, Iran

Abstract In separate analyses of the impacts of land use change and climate change, a scenario-based approach using remote sensing and hydro-climatological data was developed to assess changes in hydrological indices. The data comprised three Landsat TM images (1988, 1998, 2008) and meteorological and hydrological data (1983–2012) for the Aligudarz and Doroud stations in the Marboreh watershed, Iran. The QUAC module and supervised classification maximum likelihood (ML) algorithm in ENVI 5.1 were used for remote sensing, the SWAT model for hydrological modelling and the Mann-Kendall and t test methods for statistical analysis. To create scenarios, the study period was divided into three decades (1983–1992, 1993–2002, 2003–2012) with clearly different land use/land cover (LULC). After hydrological modelling, 10 hydrological indices related to high and low flow indices (HDI and LDI) were analysed for seven scenarios developed by combining pre-defined climate periods and LULC maps. The major changes in land use were degradation of natural rangeland (−18.49%) and increasing raid-fed farm area (+16.70%) and residential area (+0.80%). The Mann-Kendall test results showed a statistically significant (p < 0.05) decreasing trend in rainfall and flow during 1983–2012. In the scenarios evaluated, hydrological index trends were more sensitive to climate change than to LULC changes in the study area. Low flow indices were more affected than high flow indices in both land use and climate change scenarios. The results show little impact of land use change and indicate that climate change is the main driver of hydrological variations in the catchment. This is useful information in outlining future strategies for sustainable water resources management and policy decision-making in the Marboreh watershed

Flood risk mapping and crop-water loss modeling using water footprint analysis in agricultural watershed, northern Iran

Abstract Spatial information on flood risk and flood-related crop losses is important in flood mitigation and risk management in agricultural watersheds. In this study, loss of water bound in agricultural products following damage by flooding was calculated using water footprint and agricultural statistics, using the Talar watershed, northern Iran, as a case. The main conditioning factors on flood risk (flow accumulation, slope, land use, rainfall intensity, geology, and elevation) were rated and combined in GIS, and a flood risk map classified into five risk classes (very low to very high) was created. Using average crop yield per hectare, the amount of rice and wheat products under flood risk was calculated for the watershed. Finally, the spatial relationships between agricultural land uses (rice and wheat) and flood risk areas were evaluated using geographically weighted regression (GWR) in terms of local R² at sub-watershed scale. The results showed that elevation was the most critical factor for flood risk. GWR results indicated that local R² between rice farms and flood risk decreased gradually from north to south in the watershed, while no pattern was detected for wheat farms. Potential production of rice and wheat in very high flood risk zones was estimated to be 7972 and 18,860 tons, on an area of 822 ha and 7218 ha, respectively. Loss of these crops to flooding meant that approximately 34.04 and 12.10 million m³ water used for production of wheat and rice, respectively, were lost. These findings can help managers, policymakers, and watershed stakeholders achieve better crop management and flood damage reduction

Contribution of climatic variability and human activities to stream flow changes in the Haraz River basin, northern Iran

Abstract In northern Iran’s Haraz River basin between 1975 and 2010, hydrological sensitivity, double mass curve, and Soil and Water Assessment Tool (SWAT) methods were applied to monitoring and analysing changes in stream flow brought on by climatic variability and human activities. Applied to analyse trends in annual and seasonal runoff over this period, the sequential MK test showed a sudden change point in stream flow in 1994. The study period was, therefore, divided into two sub-periods: 1975–1994 and 1995–2010. The SWAT model showed obvious changes in water resource components between the two periods: in comparison to the period of 1975–1994, sub-watershed-scale stream flow and soil moisture decreased during 1995–2010. Changes in evapotranspiration were negligible compared to those in stream flow and soil moisture. The hydrological sensitivity method indicated that climatic variability and human activities contributed to 29.86% and 70.14%, respectively, of changes in annual stream flow, while the SWAT model placed these contributions at 34.78% and 65.21%, respectively. The double mass curve method indicated the contribution of climatic variability to stream flow changes to be 57.5% for the wet season and 22.87% for the dry season, while human activities contributed 42.5% and 77.13%, respectively. Accordingly, in the face of climatic variability, measures should be developed and implemented to mitigate its impacts and maintain eco-environmental integrity and water supplies

Impact of managed aquifer recharge structure on river flow regimes in arid and semi-arid climates

Abstract Managed aquifer recharge (MAR) structure is widely used to expand groundwater resources. In arid regions with flash flooding, MAR can also be used as a flood control structure to decrease peak discharge of rivers. In this paper, we present a method for quantifying the role of MAR in head water systems and assess its impact on the total water balance in a river basin. The method is based on rainfall-runoff modeling, reservoir flood routing, recharge analysis and river flow analysis. For the case selected, Kamal Abad MAR in Lake Maharlou basin in southern Iran, we analyzed changes in the downstream river regime using two scenarios (with MAR and without MAR) with different return periods. The results revealed a significant impact of MAR on river flow in terms of changes in flow timing, magnitude and variability. With MAR, the ephemeral river studied became disconnected from the main stream, albeit, whereas the case without MAR, floods with return period higher than 10 years would be connected to the downstream. Even though, MAR structures are useful in arid and semi-arid climates for irrigation water supply, their placing and designing need more attention. The developed method can be used to assess the impacts of MAR on river flow and find the best location for it to make the connection of the ephemeral river and downstream river, an issue which has not received much attention in hydrological research

Urban flood risk mapping using the GARP and QUEST models:a comparative study of machine learning techniques

Abstract Flood risk mapping and modeling is important to prevent urban flood damage. In this study, a flood risk map was produced with limited hydrological and hydraulic data using two state-of-the-art machine learning models: Genetic Algorithm Rule-Set Production (GARP) and Quick Unbiased Efficient Statistical Tree (QUEST). The flood conditioning factors used in modeling were: precipitation, slope, curve number, distance to river, distance to channel, depth to groundwater, land use, and elevation. Based on available reports and field surveys for Sari city (Iran), 113 points were identified as flooded areas (with each flooded zone assigned a value of 1). Different conditioning factors, including urban density, quality of buildings, age of buildings, population density, and socio-economic conditions, were taken into account to analyze flood vulnerability. In addition, the weight of these conditioning factors was determined based on expert knowledge and Fuzzy Analytical Network Process (FANP). An urban flood risk map was then produced using flood hazard and flood vulnerability maps. The area under the receiver-operator characteristic curve (AUC-ROC) and Kappa statistic were applied to evaluate model performance. The results demonstrated that the GARP model (AUC-ROC = 93.5%, Kappa = 0.86) had higher performance accuracy than the QUEST model (AUC-ROC = 89.2%, Kappa = 0.79). The results also indicated that distance to channel, land use, and elevation played major roles in flood hazard determination, whereas population density, quality of buildings, and urban density were the most important factors in terms of vulnerability. These findings demonstrate that machine learning models can help in flood risk mapping, especially in areas where detailed hydraulic and hydrological data are not available

Efficient rainwater harvesting planning using socio-environmental variables and data-driven geospatial techniques

Abstract Water scarcity is increasing worldwide due to population growth and climate variability/change. As a supplementary water resource, Rainwater harvesting (RWH) is a possible solution for dealing with water scarcity, particularly in arid and semi-arid regions with considerable water demand and high variability in precipitation and unexpected extreme events (floods and droughts). The success of RWH systems significantly depends on the location of RWH structures and usually selecting suitable sites is challenging for decision-makers and managers. This paper presents an approach for mapping suitable sites for RWH structures using socio-environmental variables and artificial intelligence algorithms (AIAs). Based on FAO recommendations, the most important conditioning variables for RWH systems are elevation, slope, aspect, precipitation, temperature, distance from the river, curve number (CN), land use, geology, soil type, population density, distance from road, and distance from lakes. An ensemble model was developed based on AIAs, socio-environmental variables, and existing RWH projects, and used for RWH suitability mapping in the large Maharloo-Bakhtegan basin, Iran. Model performance was evaluated using receiver operating characteristic (ROC) and Kappa index. Using the best-performing model, threshold values for conditioning variables were determined from probability curves (PC). The results showed that land use, precipitation, soil type, CN and slope were the most important variables for RHW sites, with the lowest correlation and autocorrelation. The suitability map indicated that 9.7% (3070 km²) of Maharloo-Bakhtegan basin had very high suitability for RWH systems. Thus, in RWH suitability mapping for large area, climate, hydrological, geological, agricultural, topographical, human and socio-economic parameters should be considered to enable efficient RWH planning. Probability curves revealed that the optimum parameter range (α) in Maharloo-Bakhtegan basin was precipitation 357–428 mm, temperature 12.80–15.16 °C, slope 3–6%, elevation 1612–1975 m asl, distance from lake 32–45 km, distance from river 11.4–15.9 km, distance from road 2.59–4.80 km. The RWH suitability map presented can assist decision-makers, hydrologists, and natural resources planners in finding suitable locations for constructing RWH systems

Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models

Abstract Accurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag times have significant effects on the SSL, quantifying and understanding nonlinear interactions of the sediment dynamics has always been a challenge. In this study, three soft computing models (multilayer perceptron (MLP), adaptive neuro-fuzzy system (ANFIS), and radial basis function neural network (RBFNN)) were used to predict daily SSL. Four optimization algorithms (sine–cosine algorithm (SCA), particle swarm optimization (PSO), firefly algorithm (FFA), and bat algorithm (BA)) were used to improve the capability of SSL prediction of the models. Data from gauging stations at the mouth of the Kasilian and Talar rivers in northern Iran were used in the analysis. The selection of input combinations for the models was based on principal component analysis (PCA). Uncertainty in sequential uncertainty fitting (SUFI-2) and performance indicators were used to assess the potential of models. Taylor diagrams were used to visualize the match between model output and observed values. Assessment of daily SSL predictions for Talar station revealed that ANFIS-SCA yielded the best results (RMSE (root mean square error): 934.2 ton/day, MAE (mean absolute error): 912.2 ton/day, NSE (Nash–Sutcliffe efficiency): 0.93, PBIAS: 0.12). ANFIS-SCA also yielded the best results for Kasilian station (RMSE: 1412.10 ton/day, MAE: 1403.4 ton/day, NSE: 0.92, PBIAS: 0.14). The Taylor diagram confirmed that ANFIS-SCA achieved the best match between observed and predicted values for various hydraulic and hydrological parameters at both Talar and Kasilian stations. Further, the models were tested in Eagel Creek Basin, Indiana state, USA. The results indicated that the ANFIS-SCA model reduced RMSE by 15% and 21% compared to the MLP-SCA and RBFNN-SCA models in the training phase. Comparing models performance indicated that the ANFIS-SCA model could decrease MAE error compared to ANFIS-BA, ANFIS-PSO, ANFIS-FFA, and ANFIS models by 18%, 32%, 37%, and 49% in the training phase, respectively. The results indicated that the integration of optimization algorithms and soft computing models can improve the ability of models for predicting SSL. Additionally, the hybridization of soft computing models with optimization algorithms can decrease the uncertainty of models

An index-based approach for assessment of upstream-downstream flow regime alteration

Abstract River regulation is challenging when there is diverse upstream and downstream interest, leading to regional and international conflict. However, quantifying the upstream-downstream flow regime changes and their causes are given less consideration in the river basin. In this study, we presented three new ratios for downstream-upstream low flow contribution (DUL), downstream-upstream high flow contribution ratio (DUH), and meteorological-hydrological drought ratio (MHD), for an integrated assessment of flow regime alteration across the river basin. To test the methods, we compared flow regime alteration upstream and downstream in the Ceyhan basin in central Turkey, which was significantly modified by agriculture between 1984 and 2018 (the irrigated area increased 2.8-fold, rainfed farming decreased by 67.6%). Our analysis revealed a clear change in the contribution of low and high flow seasons to annual flow in the last station of the river at Misis after 1984, but no considerable change in upstream tributaries. In the last decade (2005–2014) and the second half (1995–2014) of the study, the frequency of hydrological droughts increased, while meteorological droughts followed a stationary pattern. Evaluation of the impact of anthropogenic activities on river regime (by comparing flow regime characteristics after 1984 with those from 1975 to 1984 as post- and pre-impact periods) revealed low to incipient impact upstream (Hanköy, Karaahmet, and Kadirli river headwaters), severe impact below the Aslantaş dam in the basin center, and moderate impact at the last station on the Ceyhan river. The new metrics provide supplementary information on the flow regime alteration in the basin and can be introduced as a novel quantitative measure to recognize the driving factor of droughts